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Bloom Security has launched from stealth with $20 million in seed funding, a round first reported by Axios, to secure what it calls the AI-native endpoint, the increasingly complex layer of AI agents, extensions, and code now running on employee devices. The Tel Aviv-based company announced the round today, led by Glilot Capital Partners, with participation from Ten Eleven Ventures (1011vc), Okta Ventures, and Runtime Ventures. Angel investors in the round include founders of Dig Security, Demisto, Snyk, and Talon.

The Announcement

The funding will support a platform already in production use. Bloom Security is deployed at dozens of large enterprises across the United States and Europe, where customers are using it to gain visibility into their endpoints and replace rigid blanket policies with contextual remediation. The company is targeting large enterprises adopting AI at scale.

Bloom Security was founded by a team with a record of building enterprise security companies from zero to acquisition. It currently employs 30 people, many of whom previously worked together at Dig Security.

Why the Company Exists

According to Bloom Security, the endpoint itself has changed. Devices that were once managed and predictable have become ecosystems of agentic software, MCP servers, browser extensions, and code packages that employees assemble daily. Browsers, IDEs, and AI agents now ship with their own app stores and marketplaces, creating a software layer that grows faster than any security team can track. Existing security infrastructure was never built to see it.

“In the AI era, the employee device is no longer just a managed endpoint,” said Itay Keren, Co-Founder and CEO of Bloom Security. “Every endpoint is now running software no one reviewed, connecting to services no one provisioned.”

Traditional endpoint detection and response tools were designed for malware: binaries, executables, and malicious processes. Bloom Security argues that malware is now only part of the problem. A misconfigured AI agent, a plugin with excessive data permissions, a screen recorder on an executive’s laptop, or a code library pulling from an untrusted source can all create dangerous attack paths. Risk starts with what is already running, and most security teams lack a way to control it.

“As AI adoption accelerated, it became clear that existing endpoint controls were not designed for this new reality,” Keren added. “Security teams need a way to understand, govern, and control modern tools without disrupting how employees work.”

What the Platform Does

Bloom Security combines contextual visibility, proactive enforcement, granular remediation, and proactive prevention in a single platform. It provides a holistic view of every piece of software running across every endpoint, covering tools, extensions, and code, along with the context of how they interact with data and systems. The platform analyzes supply chain risk and examines configurations and permissions to understand actual exposure.

Context is central to the product’s design. “The same tool can be completely acceptable on one endpoint and high-risk on another,” said Ofir Balassiano, Co-Founder and Chief Product Officer at Bloom Security. “Risk depends on context: the user’s role, their access to sensitive data, the other tools operating on that endpoint, their configurations, and how everything interacts. Bloom Security was designed to evaluate that context in real time.”

Beyond visibility, teams gain active control at every stage. Users can block risky installs before they reach employee endpoints, enforce secure configurations directly, and remediate risks without manual approval workflows or disruption to employee work.

The Team Behind It

CEO Itay Keren previously held engineering and sales engineering leadership roles at Palo Alto Networks, Dig Security (acquired by Palo Alto Networks), and Demisto (acquired by Palo Alto Networks). Before entering cybersecurity, Keren served as a Naval Officer, leading teams in high-pressure environments.

Chief Product Officer Ofir Balassiano led the Cortex Cloud Posture Security research group at Palo Alto Networks, focusing on AI, identity, and data security. He previously led the research group at Dig Security and served as a Senior Security Researcher at XM Cyber, beginning his career in the IDF’s Mamram Unit.

Chief Technology Officer Itay Frishman is an engineering leader who built core AISPM and DSPM solutions at Palo Alto Networks and Dig Security, with prior cybersecurity R&D leadership experience in the IDF’s Unit 81.

“While this is technically our first company as founders, our team has built and integrated category-defining products before,” said Frishman. “We understand how enterprise security environments operate, and we built Bloom Security specifically for the reality of how endpoints are used today.”

The Investor View

Glilot Capital Partners, which led the round, framed the investment as a bet on both the problem and the people. “AI has changed the enterprise endpoint in ways the security industry is still catching up to. Agents, MCP servers, browser extensions, and code packages now run on every employee’s machine, entirely outside the reach of traditional controls,” said Kobi Samboursky, Founder and Managing Partner at Glilot Capital. “Bloom identified this gap before the market did, and the business traction we’ve seen in their first months is unprecedented for a company at this stage. A team this experienced with a problem this urgent and momentum this strong is what category-defining companies look like from day one.”

What Comes Next

The launch positions Bloom Security in one of the most underprotected layers of modern enterprise security. The company describes its mission as delivering clarity, governance, and active enforcement to the AI-native endpoint. It aims to create a secure environment where productivity tools can be used to their full potential, free from unnecessary risk.

With enterprise deployments already underway on two continents, a founding team drawn from acquired security companies, and $20 million in fresh capital, Bloom Security enters the market with an argument that will resonate with any organization watching AI tools multiply across its fleet. The endpoint has changed. The company is betting that endpoint security must change with it.

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Modus has emerged from stealth with a $10 million seed round led by Insight Partners, announcing plans to build what it calls the Context Warehouse, a new infrastructure layer for enterprise AI, as reported by Axios. Soma Capital and Bullet Ventures participated in the round, alongside prominent technology founders and operators including Eyal Kishon, Nadav Avrami of Wix and Dazl, the co-founders of Cyera, and the founders of Epsagon.

The Tel Aviv-based company is targeting what it describes as the Context Gap: the distance between what AI can access and how a business actually works. Its answer is an independent, AI-native foundation that learns how an organization operates and gives every AI agent only the context it needs.

What the Context Warehouse Does

Enterprises deploying AI in production are running into a counterintuitive problem. Connecting models to more systems often makes them slower, more expensive, and less reliable. AI agents over-fetch information, re-query enterprise systems, and burn through far more tokens than necessary because they cannot tell which context actually matters. Costs climb. Answers cannot be trusted. Pilots stall before reaching production. In Modus’s framing, the problem is not too little context but too much of the wrong context.

The Context Warehouse is designed to close that gap. It continuously learns how a business operates, keeps that understanding current as the business evolves, and composes only the relevant context required for each AI interaction. The company says this allows agents to reason on signal instead of noise while reducing unnecessary retrieval and token consumption by up to 10x.

Modus draws a direct parallel to an earlier era of enterprise infrastructure. Just as data warehouses became the system of record for enterprise data, the company believes the Context Warehouse will become the system of understanding for enterprise AI. There is one important difference. Where a data warehouse takes years of pipelines to fill and maintain, the Context Warehouse fills itself, learning from the work already happening.

Access Is Not Understanding

Models today can reach tables in a warehouse, dashboards in BI tools, documents, tickets, code repositories, and collaboration systems. What they cannot inherently understand is which definitions the business trusts, which dashboard teams actually rely on, why a metric changed last quarter, or which business logic should take precedence today.

Across the industry, enterprises are investing in context engineering, experimenting with context layers, and starting to build their own company brain. The terminology differs, but the objective is the same: giving AI a continuously maintained understanding of how the business actually works. Modus believes enterprises should not have to build and maintain that understanding themselves.

Rather than relying on documentation, semantic models, or application-specific context that must be manually maintained, the platform learns from real usage across structured data, unstructured data, and the tribal knowledge in between. That includes the queries analysts keep coming back to, the dashboards teams rely on, and the pipelines, docs, and decision threads that trace how the business really runs. Context is earned from real usage, not declared once and left to drift.

“Companies are no longer just trying to get their teams to use AI. They are asking how to scale it across the organization without accuracy dropping, governance breaking, or costs spiraling,” said Daniel Shimoni, CEO and co-founder of Modus. “Whether people call it a company brain, a context layer, or context engineering, they are all trying to solve the same problem. We believe every enterprise needs a continuously maintained understanding of how the business operates before it can build any of those things. That is what the Context Warehouse provides.”

The Founding Team

Modus was founded by Shimoni, former VP of Product at Lusha, and Tomer Mesika, former Head of Architecture at Cyera. At Lusha, Shimoni saw that AI was only as useful as the business context behind the data. At Cyera, Mesika built infrastructure to classify, govern, and secure enterprise information at scale. Their careers converged on the same realization: the systems enterprises rely on were never built for AI agents.

The platform operates independently of any data warehouse, AI model, or application platform. Enterprises can adopt new models and tools without rebuilding how context is managed, and the product works with the agents teams already use, including through MCP. It learns from metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems, without requiring organizations to centralize sensitive business data. The reasoning is simple: context cannot live inside one platform if the business does not live inside one platform.

Governance is enforced before context reaches the model. Every AI interaction receives only the information it is authorized to access, and sensitive customer data remains inside the customer’s environment. Agents, the company argues, are only as safe as the context they receive.

Maintenance Is the Real Cost

Many enterprises have already started building their own context layers and company brains. Modus contends that the challenge is not building the first version but keeping it accurate as the business changes over time.

“Building a context layer is not the hardest part,” said Tomer Mesika, CTO and co-founder of Modus. “Keeping it current is. Every change your business makes changes the context AI depends on. The real decision is no longer buy versus build. It is whether you want to own the ongoing cost of maintaining that understanding. We built the Context Warehouse so engineering teams can build what differentiates their business instead of maintaining the infrastructure underneath it.”

Early Deployments and Investor View

Modus is already deployed with enterprise customers across financial services, technology, and SaaS. The company says those organizations have improved AI accuracy, strengthened governance, accelerated response times, and reduced the cost of operating AI at scale.

“Every major wave of enterprise software has required a new foundation,” said Ganesh Bell, Managing Director at Insight Partners. “Data warehouses became foundational infrastructure for enterprise data. As AI becomes production infrastructure, organizations need a system of understanding that every agent and application can build on. We believe Modus is defining that category with the Context Warehouse.”

Today, organizations use the Context Warehouse to improve the accuracy, efficiency, and governance of AI agents. Over time, Modus sees the same continuously maintained foundation supporting a more proactive class of AI: surfacing what matters, detecting what changed, and helping teams move from trusted answers to trusted action.

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Mate Security has raised a $35M Series A led by Canaan Partners, closing the round roughly eight months after the company stepped out of stealth, as reported by Axios. Insight Partners, Team8, and M12, Microsoft’s Venture Fund, all participated. Every backer from the company’s oversubscribed $15.5M Seed round returned for the Series A, pushing total capital raised past $50M.

That sequence matters more than the headline number. Seed rounds get followed. Seed rounds do not usually get followed this quickly, by every existing investor, at this size, in a category as crowded as security operations.

The Round in Context

Mate Security describes itself as the company building the open foundation for AI security operations. The Series A arrives against a backdrop of accelerating enterprise demand: a growing number of Fortune 500 enterprises have adopted Mate as their agentic security operations platform, and the company has grown by over 500% since Q3 2025. Capital from this round is earmarked for meeting that demand rather than manufacturing it.

Canaan led. Insight, Team8, and M12 followed on. For a company that was still in stealth in late 2025, assembling that particular group of names on a Series A cap table represents a strong signal about how quickly the market has moved.

Why Security Operations Became the Pressure Point

Founded by Wiz and Microsoft veterans, Mate addresses a critical cybersecurity challenge: security operations architecture is not designed for the speed and dynamic nature of AI-scale attacks.

The failure mode is not purely a volume problem. Many of today’s AI security solutions have failed to earn the trust of security teams, leaving analysts overwhelmed not just by a higher volume of alerts, but with AI outputs they cannot verify or act on with confidence. An unverifiable recommendation is not a time saver. It is a second investigation stacked on top of the first.

Mate was built to solve that.

The Product Argument

Mate provides an open, agentic security operations platform that enables organizations to contain AI-scale attacks. The platform is built on a patent-pending context layer designed for agent precision, powering specialized agents that detect, investigate, respond to, and hunt for threats based on a deep understanding of the customer’s business.

The company’s framing is that Mate builds an organizational “brain” equivalent to the collective knowledge of an experienced, elite cyber defense team, with a deep understanding of how the organization operates. As a result, Mate can make precise and fast verdicts.

Two worked examples illustrate the point. When a security alert is raised for multiple suspicious login attempts, Mate will know whether security testing was planned during this time, and will report this as a likely non-threat. If an employee downloads multiple sensitive files, Mate understands the broader organizational context, including personnel changes and document classifications, to accurately assess whether the behavior is a genuine threat.

Neither scenario requires exotic detection. Both require knowing things about the business that a generic model does not know.

Engineering for Trust

Investing in security know-how alone is not sufficient to build trustworthy AI security at scale. Mate invests heavily in bringing frontier-class AI for security. Mate’s agents run on persistent memory that compounds with every investigation, communicate through structured agent-to-agent protocols, and operate under least-agency principles, meaning that each agent only gets the permissions and context that its task requires.

“When we started Mate, we knew we had to invest in the foundation: context and trust, and build them deeply into our product,” said Asaf Wiener, CEO and Co-Founder of Mate Security. “We brought in some of the best AI builders and security experts, and I’m excited to see how well this approach is being received by the market. We will continue moving fast and stay laser-focused on our customers, as we expand into new markets and categories to build the Open Security Operations foundation of the future.”

The Architecture Underneath

Mate provides a single, open place where it collects, resolves, cleans, and maintains the customer’s business knowledge in a Security Context Graph, then lets AI security operations run on that governed context managed by Mate’s orchestrator agent.

This foundation allows Mate agents, best-of-breed vendor subagents, and customer-built agents to encode deep organizational expertise, while Mate’s trust mechanisms enforce permissions, quality, coherence, auditability, and earned autonomous remediation and response. Mate queries evidence across the customer technology stack, at the source, and lets each agent use the same governed context and controls, so customers can extend AI security operations without fragmenting trust, reasoning, or response.

The word doing the work there is “earned.” Autonomy is not switched on at deployment. It accrues.

What the Investors Say They Bought

“AI is forcing a fundamental rethink of security operations. What stood out to us about Mate wasn’t simply its use of AI; it was the team’s conviction that trustworthy AI requires a deep understanding of how an organization operates,” said Joydeep Bhattacharyya, General Partner at Canaan. “By building a shared context layer that gives AI agents that understanding, Mate has taken a fundamentally different approach to security operations. The customer feedback and success we’ve seen in competitive evaluations reinforce our belief that the team is solving an important problem in a differentiated way.”

Insight Partners framed the bet around execution. “Security operations was not built for the speed or scale of modern AI-driven attacks,” said Teddie Wardi, Managing Director at Insight Partners. “Mate is doing something few security companies have managed: combining genuine AI depth with operational trust to rebuild security operations for the AI era. We are proud to support a team that consistently outexecutes.”

Team8 Capital, which specializes in the category, put it in comparative terms. “The pace in cybersecurity right now is faster than anything we’ve seen,” said Ori Barzilay, Partner at Team8 Capital. “As one of the leading cybersecurity venture funds, we have a clear view of what exceptional looks like, and Mate still shines above the rest. Their business traction, product development, and talent hiring are exceeding every benchmark for a company at their stage, even in the agentic era.”

M12 focused on the openness of the platform. “Mate is bringing frontier-level AI models to cybersecurity, and its exceptional growth reflects a defining shift we see in the market,” said Todd Graham, Managing Partner at M12, Microsoft’s Venture Fund. “Organizations want the freedom to adopt the latest AI advances without being locked into a single model or vendor. Mate’s open platform delivers exactly that for security operations, and we’re excited to partner with the team on this journey.”

The Bench

The team behind Mate combines experienced AI builders, who shipped production LLM systems, with cyber defenders who ran investigations in the world’s largest organizations. Mate’s AI experts specialize in high-stakes business and security decisions. They’ve held senior AI product and research leadership positions at companies including Meta and Microsoft, and include alumni of Cornell University, the Weizmann Institute, the Israeli Technion, IDF Unit 8200, and more.

Next Stop

Mate will be at Black Hat USA between August 3 and 6, 2026, in Booth 4717.

For a company eight months out of stealth with a Series A closed and a 500% growth figure behind it, the exhibition floor is now the easier part. The harder test is whether governed context holds up as the deployment count climbs.

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The math behind enterprise identity spending has long favored consultants over software vendors. Insight Partners and Glilot Capital are now putting $20 million behind a company trying to reverse that equation. Axios had the exclusive on the deal, reporting that the round positions Way Security to compete for a market far larger than IAM licensing alone.

A Market Bigger Than IAM Software Itself

Implementation and operational services often cost organizations three to five times more than IAM software itself. That gap has made the services ecosystem around identity larger than the software market it supports, and it is the specific inefficiency that Way Security, founded by Yossi Barishev and Yonatan Rosenberg, was built to address. Despite being one of the most widely deployed categories in cybersecurity, IAM remains heavily dependent on service providers, with implementations that are often highly bespoke and manual, which is exactly the pattern the company set out to break.

“Identity is one of the most critical layers of enterprise security, yet organizations still struggle to operationalize their investments efficiently,” said Jeff Horing, Co-Founder and Managing Director at Insight Partners. “Way Security is addressing a large and persistent gap between IAM software and real-world execution.”

The Round

Glilot Capital’s Managing Partner, Nofar Amikam, framed the investment as a bet on both team and market. “As investors, we’re constantly looking for the rare combination of an exceptional team, a massive market opportunity, and a differentiated approach,” Amikam said. “With Yossi and Yonatan, all three were evident from our very first meeting. They possess a deep understanding of the identity challenges facing large enterprises because they’ve lived them firsthand, and they’ve built a fundamentally better way to solve them. Identity has become the foundation of modern security, and we believe this team has the vision, talent, and determination to build the company that will define how the next generation of enterprises manages and secures it.”

The Founders’ Thesis

Barishev and Rosenberg spent years advising Fortune 500 organizations through complex identity initiatives and leading enterprise security organizations from within. Their time leading incident response for major cyber incidents showed them repeatedly how weak identity controls became a root cause or critical enabler of successful attacks, a pattern reinforced by Rosenberg’s background as an officer in Israel’s Unit 8200.

Their conclusion was that the market did not need another identity platform. It needed a way to make the platforms enterprises already own actually work as intended, without the multi-year consulting engagements that have become standard practice across the industry.

The Product

Way Security’s AI-powered agentic platform automates the operational tasks that traditionally required costly consulting engagements, without asking enterprises to replace or replatform existing IAM systems. Using agentic execution capabilities trained on real-world IAM workflows and implementation patterns, the platform can take on much of the work that has historically required armies of consultants, system integrators, and internal engineering resources, enforcing lifecycle management, access governance, authentication, and policy consistently across the enterprise. That extends to legacy, homegrown, and custom applications that do not support standard federation protocols, connecting them to the IGA, IdP, and authentication controls an organization already has so that single sign-on, multi-factor authentication, and access reviews work across the full environment.

“The issue isn’t that enterprises lack the right tools,” said Barishev. “It’s that those tools are incredibly expensive and complex to operationalize. We built Way Security to remove that friction.”

Customers Already on Board

Way Security has already signed enterprise customers across multiple industries, including Fortune 500 companies, global healthcare organizations, and financial institutions, using the platform to accelerate deployments and extend IAM coverage across complex environments.

Scaling the Opportunity

With fresh capital in hand, Way Security intends to expand its U.S. footprint and continue building out its platform, aiming to establish itself as the category leader in identity operations before the broader market catches up to the opportunity Insight Partners and Glilot Capital are backing.

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Security operations teams have long faced a difficult balancing act. They need to respond quickly as threats, technologies and infrastructure evolve, but the same changes that help organizations adapt can also introduce new risks into the systems responsible for detecting those threats.

Fig is taking aim at that challenge with a new approach to Security Operations Resilience, announcing what it calls the full SecOps engineering lifecycle. The company says its platform enables Security Operations Engineers to build, ship and observe detection and configuration changes across infrastructure, bringing a CI/CD-style workflow to security operations.

The announcement reflects a broader shift toward applying engineering principles to the security operations center. Rather than treating changes as isolated updates, Fig’s platform is designed to provide context across the SecOps environment, test proposed modifications before they reach production and continuously monitor what happens afterward.

The Problem With Constant Change

Modern security operations environments are rarely static. New data sources, detection rules, automations and cloud services are introduced regularly, while changes to upstream systems can occur without warning.

That creates a challenge for SecOps teams. A change that appears small in isolation can have consequences elsewhere in the detection pipeline, potentially causing a security control to stop working without immediately making the problem obvious.

Fig’s answer is built around security data lineage, which the company describes as a deterministic graph of the entire SecOps infrastructure. The system maps detections, data sources and the connections between them into a single view of detection flows.

The goal is to provide SecOps Engineers with a detailed understanding of how their infrastructure fits together before they make a change. Fig says the lineage gives the platform visibility into the infrastructure “down to the inch,” allowing it to account for both upstream and downstream dependencies.

That context becomes the foundation for the company’s engineering workflow. A SecOps Engineer describes the change they want, and Fig analyzes the live environment before proposing an implementation. The proposed change is then simulated and tested to assess its impact before production.

Once it is ready, the change can be deployed with version control and rollback capabilities. Fig’s continuous observability then monitors detection flows to confirm that both existing and newly introduced processes continue to operate as intended.

A Different Way to Handle SecOps Work

Fig’s approach is designed to address more than individual detection updates. The company says its capabilities can help teams turn threat reports into detections and queries intended to protect their environments immediately.

The platform is also positioned as a way to simplify large-scale infrastructure changes. According to Fig, SIEM migrations can be completed in weeks rather than months while remaining fully operational throughout the process.

Data management is another part of the company’s proposition. Fig says its full control over the data plane allows teams to determine ingest and storage spending without having to modify live detections.

For security teams, the distinction is important. Instead of spending time managing the plumbing required to implement and maintain changes, SecOps Engineers can focus on the logic behind those changes while Fig handles the surrounding infrastructure workflow.

The approach is already being used by organizations including AppLovin. Jayme Hancock, Head of Security Operations and Engineering at the company, described the difference in practical terms: “With Fig, we build and ship accurate detection changes in minutes instead of weeks, without the endless plumbing. My team builds with a confidence we’ve never had, and yeah, we’ve even started ‘vibe parsing.’”

Moving Toward Resilient Security Operations

The announcement also builds on Fig’s broader mission to make security operations more resilient as organizations contend with an accelerating threat landscape.

The company was founded by veterans of Google SecOps and Siemplify who, according to Fig, witnessed how changes could silently break complex SOC environments. Its platform is intended to address that problem by ensuring changes are made with context, validated before deployment and continuously checked afterward.

Fig’s trajectory has also included $38 million in funding from Team8, Ten Eleven Ventures and Crosspoint Capital. The company has been named an RSAC Innovation Sandbox finalist and says its platform has been deployed across dozens of Fortune 500 companies.

For Gal Shafir, Co-Founder and CEO of Fig, the objective is to remove the perceived tradeoff between speed and operational confidence.

“Security teams shouldn’t have to choose between moving quickly and maintaining confidence in their SecOps Infrastructure,” said Shafir. “Fig gives SecOps Engineers the same modern engineering workflow that software developers have long relied on. They can design changes with complete context, prove those changes work before deployment, and continuously verify that their security operations remain resilient as their environments evolve.”

As security infrastructure becomes increasingly dynamic, Fig is betting that the next evolution of the SOC will require more than adding new tools. Its vision is to give the engineers responsible for security operations a repeatable lifecycle for managing change—one that treats building, deploying and observing security infrastructure as connected parts of the same engineering process.

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The Bay Area has never been easy to cover. It is a region where a startup can become a global company in just a few years, where public policy influences billion-dollar industries, and where culture evolves as quickly as technology. For readers, keeping track of these developments means finding a publication that can connect seemingly unrelated stories into a broader narrative.

That is why trusted regional journalism continues to matter. While national outlets often focus on the biggest headlines, local publications are better positioned to explain the people, businesses, and events shaping their communities. Among those publications, The San Francisco Tribune has established itself as the number one channel for readers seeking thoughtful reporting on the issues that matter most across the Bay Area.

Capturing the Region’s Constant Evolution

Few places experience change as rapidly as Northern California. New companies emerge every week, established businesses expand into new markets, and technological breakthroughs frequently reshape entire industries.

The San Francisco Tribune follows these developments with reporting that emphasizes both immediacy and perspective. Readers can stay informed about the latest business activity while also gaining insight into the economic and social forces driving long-term change. Rather than treating each story as an isolated event, the publication examines how individual developments fit into the larger picture.

This approach makes the publication particularly valuable for professionals who want more than a summary of the day’s headlines.

Coverage That Reflects Everyday Interests

Although the Bay Area is often associated with technology, life in the region extends well beyond startup campuses and corporate headquarters. Communities are shaped by local government, cultural institutions, sporting events, restaurants, artists, and the people who contribute to the region’s identity.

The San Francisco Tribune reflects that diversity through editorial coverage spanning Art, Entertainment, Business, Politics, Sports, Food, Tech, and Wellness. By covering a broad range of topics, the publication appeals to readers with varied interests while maintaining a consistent commitment to informative, well-researched journalism.

The result is a publication that feels connected to the community it serves rather than focused on a single industry.

Reporting With Context

Readers today have access to more information than ever before, yet many stories are presented with little explanation of why they matter. Headlines can generate attention, but meaningful reporting requires context.

That has become one of The San Francisco Tribune’s defining strengths. Whether covering a fast-growing startup, a shift in public policy, a major sporting event, or a cultural trend, the publication goes beyond the immediate news to examine its broader significance.

This editorial philosophy helps readers understand not only current events but also the trends that may shape the region in the months and years ahead.

A Home for Different Perspectives

Strong journalism benefits from a variety of voices. Business executives, entrepreneurs, artists, policymakers, athletes, and community leaders all contribute to the conversations shaping the Bay Area.

Through news stories, feature articles, magazine content, and podcasts, The San Francisco Tribune creates opportunities for readers to hear from people working across different industries and disciplines. These conversations add depth to the publication’s daily reporting and encourage readers to consider issues from multiple viewpoints.

In an increasingly specialized media environment, that diversity of perspectives remains one of its greatest strengths.

Built for Modern Readers

How audiences consume news has changed significantly over the past decade. Readers move between smartphones, tablets, and laptops throughout the day, expecting news to be both accessible and reliable.

The San Francisco Tribune has adapted to those expectations by publishing stories that combine timely reporting with thoughtful analysis. Its articles are designed to inform without overwhelming readers, making complex topics easier to understand while preserving the depth expected from quality journalism.

This balance has helped the publication attract a readership that includes entrepreneurs, executives, investors, professionals, students, and anyone interested in the future of the Bay Area.

An Editorial Voice for One of the World’s Most Influential Regions

The Bay Area’s influence reaches far beyond California. Decisions made by local businesses, elected officials, researchers, and cultural leaders often shape conversations across the country and around the world.

Covering such a dynamic region requires consistency, curiosity, and a willingness to explore stories from every angle. By reporting across business, technology, politics, arts, entertainment, sports, food, and wellness, The San Francisco Tribune offers readers a comprehensive view of the community it serves.

As the region continues to evolve, readers will continue looking for a publication that explains not only what is happening, but why it matters. Through its broad editorial coverage and commitment to thoughtful reporting, The San Francisco Tribune continues to strengthen its reputation as the number one channel for stories that define the Bay Area.

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Climate technology is entering a new phase. While early investment centered largely on renewable energy generation, today’s innovators are increasingly focused on solving the complex operational challenges that stand between ambitious climate goals and real-world implementation. Reflecting that shift, TVC Analyst has published its latest list (Published in the Financial Times ) of 11 greentech startups redefining climate innovation in 2026, recognizing companies developing practical technologies for transportation, carbon management, energy infrastructure, and climate intelligence.

The selected startups represent a broad cross-section of the climate technology ecosystem. Rather than competing in the same market, they are tackling different bottlenecks that together could accelerate the transition to a lower-carbon economy.

Reinventing critical infrastructure

Heavy industry and transportation remain among the hardest sectors to decarbonize, making infrastructure innovation increasingly important.

Voltify is targeting freight rail, where diesel-powered locomotives continue to dominate North American rail networks. The company is developing battery-electric locomotive retrofits supported by autonomous charging systems and AI-optimized microgrids. Its infrastructure is also designed to supply clean electricity to nearby industrial operations, extending its impact beyond rail transportation.

Hydrogen presents another opportunity for reducing industrial emissions, but transporting it efficiently remains a challenge. H2SITE addresses that problem through membrane reactor technology capable of extracting high-purity hydrogen from molecules such as ammonia and methanol, supporting wider adoption of hydrogen across industrial applications.

The expansion of clean energy projects also depends on faster development timelines. Paces uses artificial intelligence to help developers identify suitable locations, evaluate permitting requirements, and reduce project risks before construction begins.

Helping businesses measure and manage emissions

As sustainability reporting becomes more sophisticated, organizations are seeking better ways to understand and reduce their carbon footprint.

Climatiq provides a carbon intelligence platform that simplifies emissions calculations using a database containing more than one million scientifically vetted emission factors alongside AI-powered tools. The platform enables companies to generate product carbon footprints and strengthen sustainability reporting.

CarbonChain approaches the challenge from the supply chain perspective. Its software allows organizations to measure emissions across suppliers, prepare for evolving environmental regulations, and integrate carbon data into procurement, finance, and commercial decision-making.

Scaling carbon removal

Reducing emissions alone is unlikely to meet long-term climate targets, increasing interest in technologies capable of permanently removing carbon dioxide from the atmosphere.

Three companies on the TVC Analyst list take distinct approaches to that challenge.

Graphyte captures carbon through its Carbon Casting process, preserving biomass-derived carbon in engineered underground storage designed for long-term durability. 44.01 relies on naturally occurring geological processes by converting carbon dioxide into stable carbonate minerals within reactive rock formations. Mombak, meanwhile, combines ecological restoration with carbon removal by restoring biodiverse forests in the Amazon, creating long-term carbon storage while supporting local ecosystems and communities.

Turning environmental data into business intelligence

Another emerging area of climate technology focuses on improving visibility into environmental risks before they become operational or financial problems.

Treefera analyzes agricultural supply chains by monitoring crop conditions, soil moisture, land-use changes, and other environmental indicators. The platform helps businesses and financial institutions identify potential disruptions early in the production cycle.

Climate X focuses on the financial consequences of climate change. Its software models the impact of hazards including flooding, hurricanes, and wildfires, helping organizations assess risk exposure, strengthen resilience strategies, and meet regulatory expectations.

At the same time, rising global temperatures are increasing demand for more efficient cooling technologies. SkyCool Systems addresses this challenge with radiative cooling technology that transfers heat into space, reducing energy consumption across commercial buildings, industrial facilities, and data centers.

A broader view of climate innovation

The startups recognized by TVC Analyst illustrate how climate technology has expanded well beyond renewable power generation. Today’s innovators are addressing every stage of the transition, from planning new energy infrastructure and electrifying transportation to improving supply chain transparency, advancing carbon removal, and helping organizations make better climate-related decisions.

Together, the 11 companies reflect an industry increasingly focused on practical deployment rather than theoretical potential. As businesses, investors, and governments continue pursuing decarbonization strategies, solutions that combine scientific innovation with operational scalability are likely to play an increasingly significant role in shaping the future of the global economy.

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Perion announced on July 9, 2026 a new distribution partnership with Acrossmedia241, a leading digital advertising and media technology company based in Greece. The agreement brings Outmax, Perion’s AI agent, to agencies and brands across Greece and the broader Central and Eastern European market.

The two companies will publicly launch the partnership at the Programmatic & Beyond Conference in Athens on July 9, where Perion joins Acrossmedia241 as a Grand Sponsor.

What the Partnership Covers

Outmax sits across major digital channels, including YouTube, Meta, TikTok, and other leading DSPs. It layers optimization across each channel to unify performance toward advertisers’ own custom business outcomes rather than platform-standard benchmarks. For agencies and brands operating across Greece and CEE, the partnership adds Outmax as an option built to optimize toward specifically defined outcomes, integrated into existing media buying workflows.

Acrossmedia241 brings deep market access to the arrangement. The company holds agency relationships across the region and existing work with major Greek tourism boards, along with an international buying scope. It connects brands and agencies with digital, programmatic, CTV, DOOH, video, audio, native, and AI-powered media solutions, and has long-standing relationships across the Greek and regional advertising market.

Building on Prior Work

The partnership is not a cold start. It builds on Acrossmedia241’s existing experience with Perion’s DOOH offering. Perion expects that foundation, combined with Acrossmedia241’s established relationships across the market, to lower customer acquisition costs and accelerate Outmax’s path to revenue growth.

The structure follows the partner-led expansion model Perion has applied elsewhere. The company points to its GIMC partnership in China and its McSorely Media/Mediamark partnership in Africa as precedents for the approach now being applied in Greece and CEE.

Leadership Comments

Tal Jacobson, Perion’s CEO, framed the deal in terms of market credibility and the company’s longer-range plan. “Acrossmedia241’s reach across Greece and CEE, and its standing with the region’s tourism and agency community, gives Outmax a credible path into a market where advertisers are asking for accountability, not just access,” he said. “Expanding our distribution partner network, as we’ve done with GIMC in China and McSorely Media and Mediamark in Africa, is central to how we extend our reach and compound toward our 2028 growth targets. This is land-and-expand in practice: extending Outmax through an existing partner relationship, at low incremental cost and with margin-accretive growth potential, reinforcing the strategic trajectory outlined in our 2028 growth plan.”

Irini Sacha, Founder & CEO of Acrossmedia241, pointed to what advertisers in the region are now demanding. “Advertisers today want more than strong performance on any single platform; they want every channel working toward their own business goals. Perion’s Outmax gives us a way to do that, sitting across the major digital channels with execution built around our clients’ own outcomes, not only platform defaults,” she said.

Why It Matters

Perion positions itself as an advanced technology leader solving for the complexities of digital advertising through AI-native execution infrastructure. As advertisers increase investment across a widening range of digital channels, the company argues, the operative question has shifted from where media runs to whose objectives it is optimized against.

That framing sits at the center of this announcement. The partnership extends Perion’s footprint into a new region through a partner with established relationships across national tourism boards and global buying desks, broadening outcome-driven execution beyond platform-standard benchmarks. Each platform in a modern media plan carries its own defaults and its own definition of success. Outmax is designed to sit above those defaults and pull each channel toward the advertiser’s stated objective instead.

For Perion, the deal adds another region to a distribution network that already spans China and Africa. For Acrossmedia241, it adds an AI-driven optimization layer to a portfolio that already covers programmatic, CTV, DOOH, and other channels. For advertisers in Greece and CEE, it introduces a tool built around their outcomes rather than the platforms’ own scorecards, arriving through a partner they already know.

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The race to build AI applications has accelerated dramatically over the past two years, but many developers are discovering that attracting users is only part of the equation. As AI products become easier to create and more expensive to operate, attention is shifting toward a different challenge: building sustainable business models.

Velocity is betting that solving that problem requires a new layer of infrastructure. The startup announced that it has raised $27 million in seed funding to develop monetization and distribution technology for AI-native software. The round was led by NFX and Red Dot Capital Partners, with participation from Stardom Ventures, Corner Ventures, and Transcend.

Founded by Tal Shoham together with Amir Shaked and Nimrod Zuta, the company is led by executives whose backgrounds include more than a decade building monetization, advertising, and growth infrastructure at ironSource and Unity.

The Business Model Behind AI Is Still Evolving

The explosion of generative AI has produced a wave of applications spanning productivity, software development, creative tools, search, and consumer services. Yet despite widespread adoption, converting free users into paying customers remains difficult for many AI companies.

At the same time, operating costs continue to climb as users expect increasingly advanced responses and richer AI experiences. For many companies, subscription revenue alone has proven insufficient to offset growing inference expenses.

Velocity argues that this has led developers toward restrictive pricing strategies that may ultimately limit product growth. Prompt limits, feature restrictions, and early paywalls can reduce costs, but they may also discourage engagement before users fully experience a product’s value.

Instead, the company is advocating for an additional monetization model that allows AI applications to expand free usage while generating revenue through native advertising tied to user intent.

Building Around Conversations Instead of Cookies

Traditional digital advertising has long relied on historical browsing behavior and user profiles. Velocity believes AI applications present a fundamentally different environment.

Because users interact through natural language, conversations reveal what someone is trying to accomplish in real time. The company has built infrastructure that interprets multi-turn conversations, extracts structured intent, and introduces relevant recommendations within the AI experience itself.

According to CEO and co-founder Tal Shoham, this shift represents a broader change in how software will grow.

“AI is becoming the dominant interface for software, creating entirely new opportunities around monetization and distribution,” Shoham said. “We believe the biggest opportunity of the AI era will not only be building products, but also monetizing and distributing them. We’re building the infrastructure layer that helps solve both.”

Velocity says its platform combines three components: an AI-native advertising network for intent-driven placements, a mediation and auction system that maximizes revenue across demand sources, and a conversation intelligence layer that converts dialogue into structured, privacy-safe intent signals.

Shoham said maintaining the quality of the user experience remains a priority.

“AI monetization should feel native to the experience,” he said. “Users expect AI interactions to be useful, contextual, and trustworthy. Our goal is monetization, recommendations, and product discovery that enhance the experience rather than interrupt it.”

Early Customers Report Positive Results

The company says its technology has already been deployed by AI-native applications seeking to balance monetization with user growth.

Among those customers is Leadtech / MAU. Diego Díaz, CEO of the Subscription Division at Leadtech / MAU, said the company has been encouraged by both the product and the working relationship.

“Velocity has been a great partner to work with. The team is responsive, collaborative, and moves quickly,” Díaz said. “We’ve been impressed by both the product and the people behind it, and we’re excited about the opportunities ahead. We’re confident this partnership will lead to meaningful outcomes as we continue building together.”

Velocity also highlighted its work with AIBY’s Chaton application.

Artsiom Turavets, Lead Product Manager at AIBY (Chaton), said the partnership has produced “strong performance while maintaining a high-quality user experience,” adding that the company has seen “meaningful monetization outcomes alongside healthy engagement and retention metrics.” He also described Velocity as “a true partner” that consistently develops tailored solutions.

Investors See a Long-Term Opportunity

Investors participating in the round believe AI-native software will require new infrastructure designed specifically for growth.

Gigi Levy-Weiss, General Partner at NFX, said every major technology platform creates new infrastructure categories. He believes AI-native applications represent one of the largest emerging technology platforms and said Velocity is building “the growth infrastructure layer that enables that intent to drive monetization, distribution, and growth.”

Red Dot Capital Partners shares that view. Partner Atad Peled said AI-native applications are creating “a massive new opportunity around user intent” and pointed to the founding team’s experience building and scaling global software platforms as a differentiator.

For Velocity, the funding marks the next step in pursuing what it believes will become a defining layer of AI infrastructure. Shoham argues that every major computing era has been shaped by a dominant signal, concluding that, “We believe the AI era will be built around intent, and we’re building the growth infrastructure layer to monetize and distribute through it.”

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Every morning, Chief Information Security Officers wake up to a fresh set of priorities. A newly disclosed vulnerability may demand immediate attention. A ransomware group may have shifted its tactics. A regulator could introduce new compliance expectations. Meanwhile, venture-backed startups continue to reshape the security market, established vendors pursue acquisitions, and artificial intelligence is accelerating innovation across nearly every corner of the industry.

For security executives, the challenge isn’t simply staying informed; it’s knowing which developments deserve immediate attention and which can wait. As cybersecurity becomes increasingly intertwined with business strategy, many leaders are looking for reporting that delivers clarity instead of volume. That changing expectation has created an opportunity for a new kind of publication, one that arrives with the launch of CISO HQ.

Meeting the needs of today’s security executive

CISO HQ is an independent publication created for Chief Information Security Officers and enterprise cybersecurity leaders. Its mission is straightforward: provide fast, reliable coverage of the stories shaping enterprise security without requiring readers to sort through an endless stream of headlines.

The publication follows the industry’s most significant developments, including emerging cyber threats, technology innovation, market trends, funding rounds, mergers and acquisitions, executive appointments, and the broader forces influencing cybersecurity as a business.

The result is a publication designed around the realities of executive leadership rather than technical specialization alone.

Reflecting how cybersecurity has changed

The role of the CISO has expanded considerably over the past decade. Security leaders now participate in board discussions, evaluate strategic technology investments, oversee regulatory readiness, and help organizations navigate an increasingly complex risk landscape.

That evolution has also changed the kind of information they need. Technical analysis remains important, but it is only one part of a much larger picture. Understanding where investment is flowing, which companies are gaining momentum, how vendor landscapes are shifting, and what policy changes are emerging has become equally relevant.

CISO HQ was built with that broader perspective in mind.

Journalism that respects the reader’s time

One of the publication’s defining characteristics is its editorial approach. Rather than producing lengthy analyses for every story, CISO HQ focuses on concise reporting that delivers the essential facts alongside the context executives need to understand their significance.

The philosophy recognizes a simple reality: today’s security leaders consume information differently than they did a decade ago. News is often read between meetings, while traveling, or during brief moments throughout the workday. Clear, focused reporting makes it easier to remain informed without becoming overwhelmed.

In many ways, the publication reflects a broader shift toward executive journalism: coverage that values precision, relevance, and speed in equal measure.

Watching the business behind cybersecurity

Enterprise security is shaped by more than technical innovation alone. Market consolidation, startup investment, leadership transitions, and regulatory developments all influence how organizations evaluate risk and allocate resources.

By covering these stories alongside traditional cybersecurity news, CISO HQ aims to provide readers with a more complete understanding of the industry’s direction. The publication recognizes that every funding announcement, acquisition, or executive hire has the potential to affect the technologies and strategies organizations rely on in the years ahead.

That wider lens distinguishes cybersecurity as not only a technical discipline but also one of the fastest-evolving sectors in enterprise technology.

A publication for what comes next

Cybersecurity’s importance has never been greater, but neither has its complexity. As threats continue to evolve and business expectations of security leaders grow, access to timely, relevant information becomes increasingly valuable.

CISO HQ enters the market with a clear purpose: to help CISOs and cybersecurity executives stay informed without slowing them down. By combining independent reporting with a concise, executive-first editorial style, the publication aims to become a trusted source for the news, trends, and market developments that define modern enterprise security.

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The observability market is large, mature, and well-funded. It is also, by Hud’s account, incomplete. The company’s appointment of Shai Alani as Vice President of Marketing is a calculated move to establish Runtime Intelligence as the layer observability was never designed to provide, and to do it before the category gets named by someone else.

A Deliberate Market Positioning Move

Hiring a VP Marketing at this stage of a company’s development signals that the product has reached a point where the primary challenge is no longer building but communicating, and that the company believes its market moment has arrived. Hud’s choice of Alani, a marketer with direct experience in developer observability and AI monitoring, reinforces the specificity of that intent.

Alani previously served as VP Marketing at Lightrun and held marketing leadership roles at Coralogix and Aporia. Those stops represent companies operating in the developer tooling and AI monitoring space, where technical buyers are sophisticated, category boundaries are contested, and the ability to articulate a differentiated position matters enormously. Alani arrives at Hud with direct knowledge of how these markets develop and where messaging tends to break down.

At Hud, his scope includes global marketing strategy, category creation, brand, and demand generation. The explicit inclusion of category creation in that mandate is the detail worth noting. Hud is not simply trying to sell into an established market. It is trying to define the terms of a new one.

What Runtime Intelligence Is and Why It Matters Commercially

Hud uses Runtime Intelligence to describe a specific capability: production behavior resolved to the function level, combined with forensic depth when failures need to be investigated. The commercial case rests on a problem that is real and worsening.

AI coding tools have compressed development cycles significantly. Engineering teams are shipping more code, faster, and with greater reliance on automated generation. When something fails in production, traditional observability confirms the failure but rarely explains it at the function level. Reconstructing what happened requires pulling together logs, traces, and metrics from multiple sources, a process that is slow, expensive, and dependent on instrumentation that may not have been in place when the failure occurred.

Coding agents face a version of the same problem. They can read a codebase and propose fixes, but they operate without access to runtime evidence of how that code performed under real production conditions. The result is a feedback loop that breaks precisely where it matters most, in the transition between code generation and production reliability.

Leadership on the Category

“AI has changed the speed of software creation, but production is still where code proves itself,” said Roee Adler, Co-founder and CEO of Hud. “The next major category in the AI SDLC is Runtime Intelligence: production behavior resolved to the function level, coupled with deep forensics when things go wrong, so humans and agents can understand, fix, and validate software with confidence. Shai brings the experience we need to build that category and scale Hud into a defining company for AI-native engineering teams.”

Alani’s own framing reflects the same conviction about the market opportunity.

“Runtime Intelligence is the missing layer in the AI software stack,” said Shai Alani, VP Marketing at Hud. “AI has made it easy to generate code, but it has not made it any easier to stand behind that code once it is running in production, where reliability is actually decided. That gap is fast becoming one of the defining problems for AI-native engineering teams, and it is exactly the kind of category you build a company around. That is why I joined Hud, and it is the story I am excited to take to market.”

Reading the Competitive Landscape

Positioning Runtime Intelligence against established observability platforms is a careful play. Hud is not claiming that observability is broken or that existing tools should be replaced. The argument is more precise: observability was designed for a development pace that no longer applies to AI-native engineering teams, and it was not designed to provide function-level runtime evidence to coding agents that need production data to make informed recommendations.

That framing allows Hud to operate as a complement rather than a direct competitor to entrenched observability vendors, at least in the short term. It also allows Hud to own a specific and defensible piece of terminology. If Runtime Intelligence becomes the standard name for this capability, Hud will have shaped the category before others had the chance. Alani’s appointment puts that effort on a deliberate and experienced footing.

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Enterprise security teams are increasingly being asked to secure systems that don’t behave like traditional applications. As generative AI becomes embedded in engineering, operations, and knowledge workflows, it is producing activity that is dynamic, context-heavy, and often difficult to interpret through conventional monitoring tools.

In response to this shift, Daylight announced that its Managed Detection and Response (MDR) service now integrates with Claude Enterprise, extending detection and investigation capabilities into AI-native environments where much of this new activity is taking place.

AI systems are becoming operational infrastructure

The adoption of enterprise AI platforms such as Claude Enterprise is no longer limited to isolated productivity use cases. Organizations are embedding these systems into core workflows, ranging from software development and data analysis to internal automation and business operations.

As usage expands, AI systems are interacting with sensitive data and enterprise tools at scale. This creates a new challenge for security teams: understanding not only what users are doing, but how AI agents are participating in those actions.

Claude Enterprise provides audit logs that capture usage across Claude chat, Claude co-work, and Claude Code. These logs represent a meaningful step forward in visibility, but they still require interpretation to become actionable security intelligence.

Turning AI activity into security signals

Daylight’s integration is designed to convert raw AI telemetry into structured detection and response workflows. By building on top of Claude Enterprise audit logs, the MDR platform identifies behaviors that may indicate AI-native risk.

These include unauthorized or newly introduced MCPs, risky Skills or plugins, prompt injection attempts, abnormal file access patterns, and unusual AI-driven behavior that deviates from established usage baselines.

Once identified, these signals are escalated into Daylight’s MDR system, where they are investigated in context. Analysts correlate AI activity with identity data, SaaS usage, endpoint signals, cloud infrastructure, and organizational context to determine what occurred and whether it represents a security concern.

The result is a shift from passive visibility to active investigation.

“AI visibility only matters if you can act on it”

“AI adoption is moving faster than traditional security monitoring was designed to support,” said Hagai Shapira, co-founder and CEO of Daylight. “Claude Enterprise gives organizations important visibility. Daylight’s MDR service turns that visibility into detection and response.”

The statement reflects a broader industry reality: enterprises are rapidly gaining access to AI usage data, but still lack mature systems for interpreting and responding to that data at scale.

Early deployment: integrating AI into real security operations

One of the early adopters of the integration is Miro, which has been expanding its use of Claude Enterprise across internal teams while evolving its security operations to account for AI-driven activity.

As adoption scaled, Miro’s security team prioritized ensuring that AI usage would be fully integrated into existing MDR workflows rather than monitored as a separate surface.

“As we adopted Claude Enterprise, we wanted to make sure AI usage didn’t become a new blind spot for our security team,” said Mark Strande, CISO of Miro. “Daylight helped us bring Claude activity into our MDR workflow, giving us visibility into AI-native risks and the context to investigate them.”

A key area of focus has been monitoring newly introduced MCPs and evaluating whether they introduce risk based on their behavior and system interactions.

A new layer of security operations: AI-aware MDR

The integration highlights a broader shift in how security operations are evolving. MDR platforms were originally designed to detect threats across endpoints, identities, cloud workloads, and SaaS applications. Now, they are being extended to include AI systems as first-class participants in enterprise environments.

In this model, AI activity is no longer treated as auxiliary telemetry. It becomes part of the core security dataset that are analyzed, correlated, and investigated alongside traditional signals.

This evolution effectively positions MDR as a control layer for AI behavior, not just infrastructure behavior.

Expanding toward broader AI observability

Daylight’s integration is currently available through Claude Enterprise’s Compliance API, which exposes structured AI activity data for security use cases. The company expects this type of telemetry to expand as AI platforms mature and adopt more standardized observability frameworks.

Future iterations are likely to include deeper visibility into prompts, tool calls, Skills, and autonomous agent workflows, particularly as standards like OpenTelemetry extend into AI systems.

Daylight also expects similar capabilities to emerge across other enterprise AI platforms, including ChatGPT and Gemini, as organizations push for consistent security coverage across increasingly complex AI ecosystems.

As AI becomes more deeply embedded in enterprise infrastructure, the boundary between application behavior and security behavior continues to narrow, placing MDR systems at the center of how organizations understand and control AI-native risk.

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The business intelligence market has long been dominated by dashboards that tell you what happened yesterday. Arito AI wants to change that, and it just raised $6 million in seed funding to make the case. The Tel Aviv- and Palo Alto-based startup, founded by Daniel Zahavi and Michael Estrin, is building an agentic analytics and monitoring platform purpose-built for finance and revenue teams, designed to move organizations from reactive data consumption to continuous, AI-driven intelligence.

The seed round was led by Amplify Partners, with two CFO-turned-angel investors also participating. The investment signals growing conviction that the next frontier in enterprise analytics is not better dashboards; it is agents that monitor, analyze, and act on data autonomously, without waiting for a human to ask the right question.

The Problem With Traditional BI

Finance and revenue teams have historically relied on a fragmented combination of spreadsheets, dashboards, and analyst queues to get answers from their data. The process is slow, technically dependent, and prone to version inconsistencies. Arito’s platform takes a different approach by enabling what the company calls autonomous data onboarding. The system learns the internal structures of finance and revenue tools natively, removing the need for manual modeling or complex integration work.

From there, users interact with their data in natural language. They can build self-updating dashboards, run scenario analyses, and configure real-time alerts without technical expertise. The platform’s patent-pending technology also lets organizations teach the AI how specific analyses should be performed, grounding the system’s outputs in institutional knowledge and business context.

Amplify Partners Makes the Call

For Amplify Partners, the investment is rooted in Arito’s approach to a problem that has resisted easy solutions for decades. “Arito is tackling one of the most persistent challenges in modern organizations: the gap between data availability and data usability,” said Mike Dauber, GP at Amplify Partners. “Their agentic approach removes the friction from analytics and empowers finance and revenue teams to act faster and with greater confidence.”

Dauber also singled out the platform’s security architecture as a key differentiator. “As companies move toward agentic analytics and continuous monitoring, where AI systems proactively analyze and act on business data, the stakes for security rise dramatically,” he said. “Arito’s architecture stands out not only by creating a unified control plane for user permissions, but by extending RBAC to systems that never supported it before. That combination is critical for enabling safe, enterprise-wide adoption of AI.” The platform’s built-in RBAC system extends governance even to environments like spreadsheets, enforcing permissions at the cell level across tools that have never natively supported that kind of granular access control.

Betting on the Autonomous Future

Zahavi framed the company’s ambition in terms of making intelligence truly self-serve. “At Arito, we believe every business team should be able to operate with real-time intelligence, securely, and without waiting on analysts or outdated dashboards,” he said. “This funding allows us to double down on our vision of making insights truly self-serve, proactive, and actionable through intelligent agents that understand the business context and adhere to rules and permissions defined by the organization while maintaining full data lineage.”

Thomas Seifert, Cloudflare CFO and a participating angel investor, added his own endorsement of the platform’s direction: “The future of analytics is not just self-service; it’s autonomous and collaborative. Arito is redefining how organizations interact with their data, turning it into a continuous, intelligent feedback loop.” With the new capital, Arito plans to scale its engineering and go-to-market teams while deepening its product capabilities across finance and revenue use cases. For a market long overdue for disruption, the timing may be exactly right.

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Across modern enterprises, finance teams have become dependent on an expanding stack of tools, each solving a narrow problem, but collectively introducing complexity, fragmentation, and operational overhead. Even with automation layered in, many organizations still rely on manual reconciliation and fragmented data sources to close the books.

It’s against this backdrop that Viewz is positioning its emergence from stealth. The company announced a $7 million seed round led by Ibex Investors and Flint Capital, with a goal that goes beyond improving finance software. Instead of adding another tool to the stack, Viewz is attempting to replace the stack itself with a unified operating system for financial operations.

The argument: finance doesn’t suffer from a lack of tools, but from a lack of structure

The founding team behind Viewz (Moti Cohen, Omer Aviad, and Liran Kessel) brings more than 50 years of combined experience spanning audit, CFO roles, and finance operations. Their shared conclusion is that the problem in finance is not tool availability, but structural fragmentation.

“I started Viewz because I spent 20 years watching finance fail in the same way, not from a lack of data, but from a lack of structure. We are not a better tool. We are a different answer to the same question every finance leader has been asking for years: why does this still feel so hard?” said Cohen.

That framing challenges a dominant trend in enterprise software: layering AI on top of existing systems in hopes of improving output. Viewz argues that without unified data architecture, those AI layers often inherit and amplify the inconsistencies of fragmented systems beneath them.

A shift from finance software to finance infrastructure

Rather than integrating into existing workflows, Viewz consolidates them into a single system. The platform combines a native general ledger, AI agents, and an embedded finance operations layer spanning bookkeeping, payroll, FP&A, compliance, and reporting.

At its core is a governed ledger that continuously structures and reconciles financial data. This enables what the company calls a “continuous close,” replacing the traditional monthly cycle with ongoing financial visibility.

“Finance was never meant to feel this heavy. But it does. More tools. More people. Less clarity. That’s the problem we set out to fix - not by improving the model, but by replacing it,” Cohen added.

This approach places Viewz closer to infrastructure than traditional SaaS. Instead of optimizing individual workflows, it aims to redefine how financial operations are executed end-to-end within a single system.

Early traction suggests replacement, not experimentation

Since launching quietly about a year ago, Viewz reports reaching multi-million-dollar ARR, strong Q4 growth, and zero voluntary churn. For early-stage infrastructure software, that retention signal is often interpreted as evidence of deep operational dependency rather than surface-level adoption.

That interpretation is shared by investors.

“Moti, Omer, and Liran have spent twenty years inside the problem they’re now solving. You can feel it in how they talk to CFOs. Most finance-oriented startups are layering intelligence on top of broken plumbing. Viewz rebuilt the plumbing. That’s a much harder thing to do, and it’s the only version of automated finance that scales,” said Aaron Rinberg, Partner at Ibex Investors.

Sergey Gribov, General Partner at Flint Capital, emphasized that the retention pattern matters more than headline growth metrics.

“What stood out wasn’t the growth; it was the retention. Zero voluntary churn tells you customers aren’t using Viewz alongside their existing tools. They’re using it instead. One thing that really caught my attention was feedback from one of my CFOs: if he were using this platform, he believes he could run his team with roughly 30% fewer people,” he said.

That shift is reflected in customer experience as well. “Viewz is my finance department from A to Z; everything I need in one place. When I moved companies, I brought Viewz in from day one,” said Erez Fisher, VP of Finance at Dig Security.

Infrastructure becomes the competitive layer in AI-driven finance

As enterprises continue adopting AI across finance functions, the constraint is increasingly shifting away from model capability and toward data integrity and system architecture. In that context, platforms that unify financial data and execution may play a more foundational role than point solutions layered on top of legacy systems.

Viewz’s roadmap centers on expanding what it describes as a “fully agentic finance team,” built directly into its unified system. The ambition is not to assist finance teams with isolated tasks, but to redefine finance as a continuously operating system.

Whether that vision becomes a broader industry standard remains uncertain. But the company’s emergence highlights a growing shift in enterprise software: the next wave of innovation may depend less on adding intelligence to existing tools, and more on replacing the fragmented systems those tools were built to navigate.

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The technology communications industry is undergoing a structural realignment as startups, investors, and enterprises adapt to an environment where visibility is no longer defined by traditional press cycles alone. Media influence is now distributed across podcasts, social platforms, newsletters, investor networks, and increasingly, AI-driven discovery systems that synthesize information in real time. In this landscape, the role of a PR firm has expanded from media relations into broader narrative engineering.

Within this shift, Omri Hurwitz Media has emerged as a name frequently associated with modern tech communications strategy. The firm operates at the intersection of earned media, founder branding, and digital amplification, reflecting how companies today must build credibility across multiple layers of attention rather than relying on single-channel exposure.

A Communications Model Built for Fragmented Attention

The most significant change in tech PR is not just the rise of new platforms, but the fragmentation of audience attention itself. Founders are no longer speaking to one unified media landscape; they are engaging with overlapping ecosystems that include journalists, investors, employees, customers, and algorithmic systems that curate and surface content.

This has led to a shift in how success is defined. Coverage alone is no longer sufficient. Instead, companies are expected to maintain consistent visibility across channels while reinforcing a coherent narrative that can travel across formats and audiences.

Omri Hurwitz Media has positioned itself within this evolution by focusing on integrated visibility strategies that extend beyond traditional press outreach. The firm’s work is often described in terms of building sustained narrative presence rather than isolated media wins, aligning with how startups now grow in highly competitive and saturated markets.

The Rise of GEO and AI-Driven Discovery

One of the most important developments shaping modern PR is the emergence of GEO, or Generative Engine Optimization. As AI systems increasingly become the first point of discovery for information, companies are beginning to think not only about how they rank in search engines, but also how they appear in AI-generated responses and summaries.

Unlike traditional SEO, GEO is less about keywords and more about authority signals, including credible media mentions, consistent messaging, and third-party validation across trusted sources. This shift is fundamentally changing how communications strategies are designed, particularly in sectors where perception and trust directly influence investment and adoption.

In this context, PR is becoming more closely tied to information architecture itself. The way a company is covered, referenced, and described across the internet now influences how both humans and AI systems interpret its relevance and credibility.

Founder-Led Influence and Narrative Strategy

A key part of Omri Hurwitz Media’s identity is its founder, Omri Hurwitz, who established the firm with a focus on redefining how modern media ecosystems function for startups. His approach reflects a broader understanding that influence today is not centralized in a handful of publications, but distributed across multiple channels that collectively shape perception.

In a profile published by Rolling Stone UK, Hurwitz discussed the evolving relationship between traditional media and digital distribution, emphasizing how the boundaries between the two have become increasingly blurred over time. His perspective highlights the importance of integrating different forms of media into a unified strategy rather than treating them as separate disciplines.

Scale Within the Startup Ecosystem

Omri Hurwitz Media is frequently associated with high-growth technology companies operating across venture capital ecosystems, including early-stage startups, emerging scale-ups, and established unicorns. The firm has reportedly worked with more than 300 startups, alongside billionaires and over 20 unicorn companies, spanning industries such as AI, fintech, cybersecurity, and SaaS.

While these figures are often referenced in industry conversations, they reflect the firm’s broader presence within startup and investor networks rather than formally audited metrics. What remains consistent is its positioning within environments where narrative visibility and market perception play a central role in growth outcomes.

From Media Relations to Narrative Infrastructure

The broader PR industry is moving away from campaign-based publicity and toward continuous narrative infrastructure. Instead of focusing on short-term press cycles, companies are now building long-term visibility systems that operate across media, social platforms, investor communications, and AI-driven discovery layers.

This evolution reflects a deeper shift in how information itself is consumed. Visibility is no longer a static achievement but an ongoing process shaped by repetition, reinforcement, and cross-platform consistency.

Omri Hurwitz Media operates within this framework, treating communications as a system rather than a sequence of outputs. In doing so, the firm reflects a broader transformation in tech PR where narrative control, distribution strategy, and AI-era discoverability are becoming inseparable components of modern brand building.

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When Omri Hurwitz talks about the venture capital industry, he does not reach for diplomatic language. In a wide-ranging conversation with Yoel Israel, the founder of Omri Hurwitz Media laid out a view of the VC landscape that is blunt, data-adjacent, and, to anyone who has spent time close to the industry, difficult to refute.

The full conversation is available on YouTube, and the section on venture capital alone is worth the watch.

His core claim is this: roughly 5% of VCs account for roughly 95% of the capital returned in the asset class. It is a Pareto distribution taken to an extreme that, as Hurwitz noted, you would not find in hedge funds or most other financial models. In almost any other industry, a gap that wide between the leaders and the rest would trigger a structural reckoning. In venture capital, it mostly gets ignored.

What makes it worse, in Hurwitz’s view, is what happens in the space left by underperformance. The VCs who are significantly behind on returns for their LPs are often the loudest voices in the room. They are on podcasts. They are posting thought leadership on LinkedIn. They are telling founders what the market wants and where to build. And founders, hungry for signal in a noisy environment, are listening.

“They’re severely underperforming and they’re saying all this stuff throughout the media,” Hurwitz said. “Founders are just getting so confused. Just listen to the ones who make the money.”

The herd mentality that flows from this dynamic is one he finds particularly damaging. Software is going to eat the world. SaaS is dead. AI is going to kill everyone. AI is not going to replace humans, only humans who do not use AI. The narrative shifts constantly, driven not by results but by whoever has the largest audience and the most confident delivery.

This is where Hurwitz draws a hard line between platform investors and everyone else. Firms like Andreessen Horowitz, Sequoia, and Lightspeed occupy a fundamentally different category, and he is unusually direct about what working with them actually means.

“It’s like good that they own you,” he said. “They make the calls and it’s good because they have the leverage.”

That framing is deliberate. Hurwitz is not describing a power imbalance to be wary of. He is describing an arrangement that, for the right founder, is genuinely advantageous. Platform investors bring the international network that gets a company into rooms with Fortune 500 clients. They have the operational infrastructure, the follow-on capital, and the pattern recognition from scaling dozens of companies before yours. They steer the ship, and for a founder whose strength is product or technology rather than distribution, that is not a loss of control. It is access to a machine that most founders could not build on their own.

“They can scale you,” Hurwitz said simply.

The problem is that most founders do not raise from platform investors. They raise from the other 95%, many of whom talk like platform investors and deliver very different results. Knowing the difference before you sign a term sheet, Hurwitz suggested, is one of the more important things a founder can do.

Israel pressed him on whether there is a middle ground, a way to take VC money, absorb the useful input, and filter out the noise. Hurwitz was skeptical. In his experience, the pressure to make investors happy creates a particular kind of founder behavior that is more concerned with optics than outcomes. The founders he respects most are the ones who understand whose money they took and why, and who are not waiting for board approval before they move.

He also made clear that the calculus changes depending on which tier of investor you are dealing with. If a top-tier platform firm has written you a check, deferring to their judgment on major decisions is not weakness. It is rational. They have the track record and the infrastructure to back their calls. If a mid-tier or underperforming fund has written you a check and is making the same demands, that is a different conversation entirely.

The uncomfortable implication sitting underneath all of this is that the venture capital model, as currently constructed, may not be optimally designed to serve founders at all. It is designed to serve fund economics. For founders who understand that distinction early, and who know exactly which category their investor falls into, a great deal becomes clearer.

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Monce’s platform was already reducing manual order handling for industrial customers, but the infrastructure behind it was under increasing pressure, leading the company to work with Automat-it on the AWS migration detailed in this case study. The project addressed cost scaling, deployment overhead, and the need for a cloud environment better suited to expansion across sectors.

The industrial sectors Monce serves

Monce runs B2B commercial operations for major industrial groups across construction, glass manufacturing, surface treatment, aerospace, aluminum, and B2B distribution. Its proprietary multi-agent pipeline reads inbound orders across any format, extracts technical specifications, matches them against product catalogs with customer-specific pricing, and sends the result directly into ERP.

The platform was built by operators who typed orders into AS400 for years, and Monce presents it as a way to reduce repetitive manual effort inside industrial order workflows. According to the case study, the platform reduces around 25 minutes of manual data entry per order to under 60 seconds of AI processing. It also cuts order errors from 8% to 12% to under 1% and lowers processing costs by 70%.

These results helped the company expand from a single factory deployment to multiple enterprise accounts across France. They also supported Monce’s move into new industrial verticals, which increased the need for infrastructure that could support a broader and more varied customer base.

The infrastructure issues that emerged with growth

The case study identifies three key constraints in Monce’s Azure environment.

The first was cost scaling faster than revenue. Azure’s container architecture maintained fixed compute costs regardless of processing volume, so infrastructure spending increased as Monce added more clients even during off-peak hours.

The second was AI inference cost. Monce’s multi-agent LLM pipeline reads full order conversations, performs proprietary catalog matching, applies customer-specific logic, and learns vocabulary and patterns. Running that on Azure AI services was more expensive than equivalent AWS alternatives.

The third was deployment overhead. Every new client required custom infrastructure configuration. That meant engineering resources were being used on repeated environment setup rather than on product development and Monce’s expansion into revenue intelligence and multi-channel ordering.

For a company growing across several industrial categories, those issues became increasingly important. Expansion into more verticals created more opportunity, but also made repeatable deployment and cost control more important.

The AWS migration led by Automat-it

Automat-it addressed those issues by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution delivered by Automat-it’s engineers and DevOps experts was based on Amazon ECS architecture and implemented through Terraform Infrastructure-as-code.

That gave Monce a repeatable way to create infrastructure while still allowing different configuration for different deployments. It also helped shift the company toward a more flexible environment better suited to varying demand across customer accounts.

The case study says Automat-it applied best practices developed across hundreds of AWS migrations completed for other startups. These included cost optimization through infrastructure design and FinOps expertise, as well as scalability planning designed to support a secure and stable environment.

On the technical side, Automat-it integrated Monce’s existing Firebase frontend with AWS ECS. The FastAPI Python application structure, which had been part of Monce’s monolithic backend before the migration, ran there. WebSocket connectivity between the frontend and backend was handled through an Application Load Balancer.

The results for expansion across sectors

The migration produced a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That helped Monce support growth with a cost structure more responsive to usage.

The case study also says the migration was completed with zero client downtime. That was important because Monce was already supporting live industrial deployments across active customer environments.

Deployment speed improved as well. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes. For a company expanding across construction, glass manufacturing, surface treatment, aerospace, aluminum, and B2B distribution, that created a more practical model for supporting rollout across different sectors.

Infrastructure costs also became better aligned with order volume rather than rising mainly because more client contracts had been added.

What the migration supported as Monce expanded

This case study shows how expansion across industrial verticals can place new demands on infrastructure even when the product itself is performing well. Monce already had a platform that reduced manual entry time, lowered errors, and cut processing costs. What it needed next was a cloud environment that could support broader growth without creating the same level of cost and deployment friction.

Automat-it’s migration gave Monce lower infrastructure costs, faster environment creation, and a more scalable operating model. For a company extending its reach across multiple industrial sectors, those changes created a stronger foundation for continued expansion.

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The drive-thru is increasingly becoming a focal point for operational innovation in the quick-service restaurant industry. It is where labor shortages are most visible, where speed expectations are highest, and where consistency is most difficult to maintain. Lee’s Famous Recipe Chicken is now addressing these pressures by expanding access to Hi Auto’s AI Order Taker across its franchise network.

The expansion follows a 30-location rollout that demonstrated strong performance and provided the operational validation needed for broader availability.

Standardization Enables System-Wide AI Capability

Before introducing AI at scale, Lee’s focused on a foundational challenge: inconsistent systems across locations. The company unified its POS system and menu database, creating a standardized operational framework across its restaurants.

This alignment was essential for AI deployment. Without consistent menu structures and backend systems, performance can vary widely between locations, limiting scalability.

With this foundation established, Lee’s tested Hi Auto in both company-owned and franchise stores under live drive-thru conditions to ensure real-world readiness.

Optional Rollout Preserves Franchise Autonomy

A defining feature of the expansion is that adoption is not mandatory. Franchisees are being given access to Hi Auto’s AI Order Taker but retain full discretion over whether to implement it.

This approach reflects Lee’s broader philosophy of empowering operators rather than enforcing top-down operational changes.

“Our operators are the backbone of Lee’s, and it’s our job to give them every advantage we can,” said Ryan Weaver, CEO of Lee’s Famous Recipe Chicken. “After seeing the results Hi Auto delivered in our first 30 stores, including better labor efficiency, shorter lines, a happier team, and guests getting their orders just the way they want them, we wanted to make this tool available to every franchisee who wants it.”

The strategy emphasizes adoption through demonstrated value rather than requirement.

Performance Data Supports Broader Adoption

Across participating locations, Hi Auto has achieved more than 95% order completion rates and 97% accuracy in live drive-thru environments. These figures are particularly important in high-volume restaurant settings where efficiency directly impacts guest experience.

In addition to order performance, the system has delivered operational improvements including three to eight labor hours saved per day, a 17% reduction in employee turnover, and a 1.5% increase in average ticket size.

These results position the technology as both a labor optimization tool and a revenue-supporting system.

Redefining Frontline Workflows In The Drive-Thru

The introduction of AI ordering changes how labor is allocated inside restaurants. By automating order-taking, staff are able to focus more on food preparation, order assembly, and customer interaction.

This reduces multitasking pressure during peak hours and helps stabilize operations when demand spikes.

Rather than eliminating roles, the system redistributes tasks to improve efficiency and consistency.

Hi Auto’s Operational Scale Strengthens Confidence

Hi Auto brings significant operational scale to the partnership. The company powers nearly 1,000 drive-thru locations globally and processes more than 100 million orders annually. It also serves approximately 200 franchisees across multiple regions.

This scale demonstrates that the platform has been tested in diverse environments, reinforcing its reliability for broader deployment.

Hi Auto CEO Roy Baharav has emphasized that the company’s mission is to empower operators with tools that improve performance while preserving human roles in service operations.

A Stepwise Evolution Of Drive-Thru Operations

Lee’s approach to AI adoption is incremental rather than disruptive. By combining backend system standardization with optional deployment, the company is creating a framework where innovation can scale without forcing uniform operational change.

This ensures franchisees can adopt technology at their own pace while still benefiting from centralized investment in modernization.

As rollout continues, Lee’s strategy may serve as a model for how mid-sized QSR brands can integrate AI into core workflows while maintaining operational flexibility and franchise independence.

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One of the most important developments in artificial intelligence is that it is starting to feel less exceptional. At HumanX 2026 in San Francisco, the most compelling startups are not always the ones making the boldest futuristic claims. They are the ones making AI feel ordinary in the best sense of the word: useful, embedded, dependable, and close to the workflows people already live with every day.

That kind of ordinariness is a sign of maturity. When a technology begins to disappear into execution, it usually means it is getting closer to real value. AI is moving into sales systems, legal processes, public-service platforms, infrastructure environments, knowledge retrieval, and trust layers. The point is no longer to spotlight intelligence for its own sake. It is to make systems work better, more consistently, and with less friction.

The San Francisco Tribune identified 11 startups at HumanX that best represent this more embedded phase of the market. They vary widely in category and purpose, but they all show what happens when AI starts behaving less like a spectacle and more like part of the operating environment.

Where AI Is Becoming Routine

Alta makes AI feel ordinary by applying it to a familiar and essential business challenge: go-to-market execution. Its unified system combines more than 50 data sources, including CRM systems, intent signals, job postings, and product usage, to help teams identify the right prospects and act on the right timing. It also supports orchestration across email, LinkedIn, SMS, WhatsApp, and calls. Alta’s AI agents adapt according to engagement patterns and trigger events, helping improve outbound pipeline generation, qualify inbound leads quickly, reduce no-shows, and revive closed-lost deals. The more natural this process becomes inside revenue teams, the more AI starts to look like ordinary infrastructure.

Baseten does something similar at the infrastructure layer. By focusing on inference, it helps organizations deploy and scale machine learning models in production without treating every deployment as a custom reinvention. The platform supports open-source, fine-tuned, and custom models, with optimized runtimes, cross-cloud availability, and flexible deployment options including self-hosted environments. That kind of infrastructure discipline helps AI move from special project status into repeatable use.

Binti shows how software can become part of daily institutional work. Its platform modernizes foster care and adoption processes for agencies and social workers, reducing friction in approval and placement. Since launching in 2017, Binti has helped more than 110,000 families get approved to foster or adopt and is used by over 12,000 social workers across 34 states. Agencies using the platform have seen a 30 percent increase in family approvals. Its role in this group is important because it shows operational technology becoming routine in a public-serving system.

Where AI Starts Blending Into the Flow of Work

Yutori is building toward a web where users delegate repetitive tasks to autonomous agents rather than manually handling every step. Grocery ordering, reservation management, and group travel planning are all examples of the kinds of workflows it wants agents to absorb. If that vision succeeds, AI becomes less visible precisely because it becomes more woven into digital life.

Crosby is making AI part of legal execution by combining automation with lawyer expertise. Its aim is to help fast-growing companies close deals more efficiently and reduce friction in contract cycles. In that sense, it is treating AI as a support layer for professional work rather than a dramatic replacement for it.

Kognitos is moving automation closer to ordinary business usage by letting users define workflows in plain English. Its English as Code paradigm lowers the barrier to workflow design, while its neurosymbolic architecture and Time Machine runtime emphasize reliability, exception handling, and continuity. That combination makes automation feel more usable and less brittle.

Mithril is helping make compute access more manageable by aggregating GPUs, CPUs, and storage across multiple cloud providers into a unified interface. That operational simplicity matters because infrastructure only becomes truly ordinary when it stops feeling fragmented and difficult to control.

Where AI Extends Into Access, Information, and Trust

Kikoff is using AI-driven underwriting models to help consumers build credit histories, especially those underserved by traditional financial systems. It shows how operational AI can become part of everyday financial mobility rather than a niche capability.

Vectara is focused on search and retrieval, supporting conversational applications grounded in enterprise knowledge. As more organizations want users to access internal information through intelligent interfaces, systems like Vectara’s make that interaction feel more natural.

Semafor is building a journalism model based on transparent, multi-perspective reporting and verified facts. In a media environment filled with complexity and distrust, that structure is part of what makes its offering distinct.

GetReal Security is making authenticity verification more operational by helping enterprises and governments detect deepfakes and identity manipulation before those threats create damage. In the AI era, proving what is real is becoming part of ordinary digital defense.

The Sign of a More Mature Market

The startups highlighted by the San Francisco Tribune suggest that one of AI’s biggest milestones may be its ability to feel less dramatic. When intelligence becomes embedded into workflows, infrastructure, and trust systems, it starts to matter in more durable ways.

That is one of the most useful readings of HumanX 2026. AI is becoming ordinary, and that is exactly why it is becoming more important.

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The early era of enterprise AI was defined by modularity. Companies stitched together models, APIs, cloud infrastructure, and orchestration layers to build applications. That modular approach worked well when workloads were small and experimental.

But as AI systems move into production, that architecture is beginning to break down.

The partnership between Impala and Highrise AI reflects a broader industry shift toward vertical integration, where inference, compute, and infrastructure are tightly coupled rather than loosely assembled.

From Modular Cloud to Integrated Execution Systems

At the core of the partnership is a simple idea: AI workloads are too demanding to remain fragmented across independent layers.

Impala provides a high-performance inference stack designed to maximize throughput and reduce cost per token. Highrise AI provides a GPU-native infrastructure layer designed for production-scale AI workloads, including distributed training, fine-tuning, and inference execution.

Together, they form a vertically integrated execution system that connects model output directly to optimized compute resources.

This integration is further supported by Hut 8’s energy infrastructure, which enables Highrise AI to operate large-scale GPU clusters backed by gigawatt-level power capacity.

Why Fragmentation Becomes a Liability at Scale

In early-stage AI deployments, modular systems offer flexibility. But at scale, fragmentation introduces inefficiencies: latency overhead, compute underutilization, and unpredictable cost scaling.

Highrise AI’s infrastructure is designed to address this by providing predictable, high-density GPU clusters with consistent performance characteristics. These systems support high-bandwidth networking and distributed workloads that require tightly synchronized compute resources.

Impala builds on this foundation by ensuring that each unit of compute is used more efficiently, reducing waste at the inference layer.

The combination reduces friction between workload demand and infrastructure allocation.

Economics Driving Architectural Change

The shift toward vertical integration is not ideological—it is economic. As AI workloads scale, inference costs often become the dominant operational expense.

Impala’s platform is designed to reduce cost per inference by increasing GPU utilization efficiency. Highrise AI reduces infrastructure costs through optimized cluster design and energy-backed scaling capabilities.

The result is a compounding efficiency model where improvements at one layer reinforce the other.

Vince Fong of Highrise AI summarized the dynamic: “We’re at an inflection point where the enterprises that win will be the ones that can run AI reliably and affordably at scale.”

Security and Compliance as Structural Requirements

For enterprises in regulated industries, infrastructure design must account for compliance from the outset. Healthcare and financial services organizations, in particular, require strict controls over data handling and processing environments.

Impala’s single-tenant deployment model ensures workload isolation within customer environments. Highrise AI adds confidential compute capabilities that protect data throughout processing.

This layered security model is designed to meet enterprise requirements without sacrificing performance.

The Return of Vertical Systems in AI

The Impala-Highrise AI partnership reflects a broader industry trend: a return to vertically integrated systems. As AI workloads become more complex, abstraction layers are being replaced by tightly coupled systems optimized for performance and cost efficiency.

In this model, infrastructure is no longer a passive foundation. It becomes an active part of system performance.

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