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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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A new category of infrastructure companies is emerging: one that does not replace physical systems but instead overlays them with intelligent energy networks. Voltify, which just raised a $30 million seed round, is positioning itself squarely in this shift.

Founded by Dafna Langer and Alon Kessel, the company is not building trains or rail lines. Instead, it is building an energy layer designed to reduce rail fuel costs by more than 20% while enabling large-scale decarbonization without requiring traditional electrification infrastructure.

The round was co-led by Aleph and Fortescue, alongside strategic investors and angels. The participation of Fortescue is particularly notable given its role as a global mining and energy company with deep exposure to heavy logistics systems.

The Structural Problem in Freight Rail

Freight rail is one of the most energy-intensive logistics systems in the world. In the U.S. alone, major operators collectively spend about $11 billion per year on diesel fuel.

While electrification offers a theoretical solution, it has historically required massive infrastructure investment. Overhead wiring systems across rail networks can exceed $1 trillion in total cost, depending on scale and geography.

This cost barrier has left rail partially modernized but not fully transformed.

Voltify’s “Energy Layer” Model

Voltify’s approach is to separate energy infrastructure from physical rail infrastructure. Instead of rebuilding rail lines, the company builds a parallel energy system consisting of:

• Battery-powered locomotives

• Dynamic charging technology

• Distributed renewable microgrids

The system is designed to operate alongside existing rail operations without requiring changes to logistics behavior.

A defining feature is in-motion charging, which allows locomotives to recharge without stopping. This eliminates downtime while maintaining throughput.

Why Microgrids Matter

The microgrid component is central to Voltify’s architecture. These systems generate energy locally using solar power and battery storage, coordinated by software that manages distribution and load balancing.

By decentralizing energy production, Voltify reduces reliance on fossil fuels and centralized grid infrastructure.

The Economics of Decarbonization

CEO Dafna Langer emphasizes that Voltify is not simply reducing emissions but restructuring cost dynamics. “We’re making clean energy the financially smarter option,” she said, arguing that reducing energy costs by over 20% changes the economics of adoption.

Aleph’s Tomer Diari described the company as redefining global rail energy supply chains, while Fortescue’s Gus Pichot linked the technology to broader decarbonization efforts in heavy industry.

Traction and Near-Term Milestones

Voltify has already secured a paid pilot with one of the world’s largest Class I rail operators, signaling early validation in a highly conservative industry.

Deployment is expected in the coming months, and the company plans to demonstrate its integrated system, covering locomotives, charging, and microgrids, later this year.

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Security Growth Depends on More Than Product Strength

For all the attention paid to product innovation in cybersecurity, scale remains a commercial achievement. A company can be technically impressive and still struggle to translate that into durable market traction. CISO Whisperer’s new TVC Analyst Official Sales Leaders Rankings, developed in collaboration with Onfire, tackles that reality directly by focusing on the executives leading the sales and revenue organizations of companies showing meaningful commercial movement.

The report frames cybersecurity as a market benefiting from several simultaneous forces: expanding digital infrastructure, rapid cloud adoption, remote and distributed operations, tighter regulatory expectations, and growing use of AI across enterprise workflows. Together, those forces have pushed security higher into strategic planning. Cybersecurity vendors increasingly sit in mission-critical positions, and the stakes of enterprise purchasing decisions are correspondingly higher.

That makes commercial execution a central part of vendor success, not an afterthought.

The Ranking Framework

CISO Whisperer argues that the chief revenue officer role has evolved well beyond traditional sales leadership. In many cybersecurity companies, revenue leaders help shape broader commercial strategy, oversee market education, drive ecosystem partnerships, and support expansion across existing customers. Because security products often require long evaluation periods and approval from multiple functions, the leaders running those organizations must combine technical credibility with business discipline.

That is the context for the ranking’s methodology, which combines sales organization growth, market positioning, and aggregated industry signals into a total score. This structure allows the report to capture not only who is large or visible, but who appears to be building commercial infrastructure in ways that suggest lasting expansion.

Who Appears in the Top 20

The report’s Top 20 begins with Trellix, where Natalie Polson, Chief Revenue Officer, is named as the executive tied to the No. 1 position. Trellix shows 50 percent sales growth and a total score of 100. Corelight is next with Kevin Williams, Chief Revenue Officer, at 42 percent growth and 88 points. Netskope ranks third with Chief Revenue Officer Raphaël Bousquet at 27 percent growth and a score of 78. Okta follows with Steve Finch, Vice President, Sales Development, at 20 percent growth and a total score of 75. Imperva rounds out the top five with Rob Elliss, VP Worldwide Sales, Application & Data Security, posting 12 percent growth and a total score of 70.

The middle of the ranking is particularly revealing because it mixes high-growth challengers with already established brands. AppViewX takes sixth place with Marc Lecuyer, SVP, Global Sales, and 63 percent sales growth. iboss is seventh with Joe Cosmano, SVP of Sales & Services, Americas, at 34 percent. Invicti Security ranks eighth with Noel Slane, Vice President of Global Sales, at 35 percent. Abnormal AI is ninth with Kevin Moore, Chief Revenue Officer, at 20 percent. Qualys lands tenth with Shawn O’Brien, EVP Sales, at 15 percent.

Delinea follows with Jessica Krowel, then Rubrik with Mike Tornincasa, Keysight with Steve Yoon, Black Duck with Tom Herrmann, and ExtraHop with Michelle Reynaud. Intel 471 with Gerard Simon sits at No. 16 and posts the highest sales growth rate in the ranking at 82 percent. Proofpoint with Rich Green, Barracuda with Miles Persky, Contrast Security with Jack Ekelof, and Checkmarx with Yigal Elstein complete the list.

Scale, Visibility, and Sales Growth

This is not just a roll call of company names. The report connects the rankings to category-level signals. It points to strong momentum in cloud security platforms, identity and access management, security service edge, application security testing, and network detection and response. Those are all areas closely tied to enterprise modernization and zero-trust priorities. The report also notes growing interest in AI-driven security capabilities, especially where vendors use AI for threat detection, anomaly analysis, and automated response.

There is also an interesting tension built into the ranking. High total score and high sales growth are not always the same thing. Trellix leads overall, while Intel 471 posts the fastest visible sales growth. That suggests the list is capturing a more layered picture of the market, where scale, category strength, visibility, and organizational momentum do not always line up perfectly but together tell a fuller story.

A Different Way to Read the Market

CISO Whisperer’s report ultimately makes a straightforward claim: the market’s commercial leaders deserve close attention because they reveal where cybersecurity demand is turning into expansion. In a sector crowded with technical messaging, that is a valuable lens.

It reminds readers that winning in security is not only about what a company builds. It is also about how effectively it can take that offering to market and scale around it. That broader reading of the market is what gives the report its value.

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There is a growing impatience in enterprise security with products that can describe risk but not reduce it. For years, the market rewarded visibility, telemetry, and increasingly broad detection coverage. Those things still matter, but security leaders are now asking harder questions. Which products can help teams act faster? Which ones can reduce real exposure? Which ones can make security operations more manageable rather than more layered?

That context shapes CISO Whisperer’s newly released vendor watch list for RSAC 2026. Scheduled for March 23-26, 2026 at San Francisco’s Moscone Center, RSA Conference remains one of the most influential annual stages in cybersecurity. It is where strategic narratives, product direction, and enterprise buying priorities collide in real time.

Exposure Management Has to Deliver More

One of the clearest examples of this shift is Reclaim Security, which is featured for its remediation-first approach to exposure management. Its AI Security Engineer continuously discovers security gaps, safely remediates misconfigurations, and reduces risk across the stack without disrupting business operations. That is meaningful because many security teams already have a long list of known issues. What they lack is a scalable way to resolve them. Reclaim’s “Attacker’s Worst Day” experience at RSAC 2026 is built around that exact challenge.

SecOps Needs More Context, Not Just More Data

The same market pressure is driving interest in Daylight Security, which is challenging traditional managed security services by delivering outcomes as a service. The company combines agentic AI with elite security experts and integrates telemetry from a wide range of security and IT systems. Just as importantly, it continuously builds business context, which allows more complex cross-system investigations and response. At RSAC, Daylight will present this model at the “Wiz House,” emphasizing how expert-led automation can accelerate threat resolution and help security teams work through alert backlogs.

Understanding What Is Actually Exposed

The third major company highlighted is CyCognito, which addresses another core enterprise problem: incomplete awareness of the external attack surface. CyCognito continuously discovers assets and validates real-world exploitability, helping organizations focus on the exposures that matter most. That attacker-centric view is increasingly important in large environments where unknown assets and overlooked attack paths can persist well outside formal visibility. The company’s recent recognition by GigaOm as an ASM Leader and Outperformer adds momentum heading into the conference.

The Wider RSAC Vendor Mix Reflects That Same Shift

The rest of the vendor mix reflects how broad that push toward actionable security has become. Splunk is demonstrating AI-assisted detection and response, Sysdig is bringing cloud-native runtime security to the floor, and Halcyon is emphasizing anti-ransomware defense and recovery. Alongside them, Abnormal AI, Arctic Wolf, 1Password, Dragos, and Huntress highlight how email security, managed operations, identity, OT resilience, and mid-market protection remain central parts of the conversation.

The Larger Takeaway

What distinguishes this list is its emphasis on actionable security. These are not just vendors with conference visibility. They are companies that reflect what many CISOs now need most: technologies that can help them resolve, prioritize, and operate more effectively. That is why this watch list feels aligned with the real direction of the market rather than just the rhythm of event marketing.

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Enterprises are expanding their AI and analytics workloads at a rapid pace, and with that growth comes escalating cloud and data platform costs. Data platforms like Snowflake, Databricks, and BigQuery have become critical to enterprise operations, but inefficiencies such as oversized clusters, unused tables, redundant pipelines, and misaligned storage can quietly inflate cloud spend. PointFive’s Cloud and AI Efficiency Platform is designed to address this challenge, helping organizations uncover hidden waste and optimize resources across both cloud infrastructure and data platforms.

Uncovering Hidden Inefficiencies

Data platforms can be powerful, but their complexity often makes inefficiencies difficult to detect. PointFive’s DeepWaste™ detection engine analyzes cloud infrastructure alongside data platform activity, surfacing over 400 potential savings opportunities with full technical context. This allows teams to focus on the areas that will deliver the highest impact and reclaim wasted resources for AI initiatives, innovation, or broader operational efficiency.

Targeted Optimization for Major Platforms

PointFive delivers precise, actionable recommendations for the platforms driving enterprise analytics:

• Snowflake: Right-size warehouses, remove pipelines feeding unused tables, and reduce storage overhead from Time Travel and FailSafe features.

• Databricks: Optimize cluster configurations and scaling for workload requirements, while eliminating unused tables and volumes.

• BigQuery: Detect reservation waste, adjust slot commitments, and remove jobs generating outdated or unused datasets.

These optimizations reach deep into compute, storage, and pipeline layers, uncovering inefficiencies that traditional monitoring tools often fail to detect.

Turning Insights Into Action

PointFive does more than just identify inefficiencies. AI-assisted workflows generate Infrastructure-as-Code fixes that run locally and include human approval steps, giving teams full control over changes. Integration with tools such as Cursor, Windsurf, Slack, Jira, and ServiceNow allows remediation to happen within existing workflows, without context switching. Each action is tied to measurable financial outcomes, so teams can see real savings in action.

“PointFive now brings continuous, context-powered optimization to the platforms where some of the most significant and fastest-growing cloud spend lives. The same intelligence, the same results — across the complete stack,” said Sharon Gross, Vice President of Product at PointFive. This quote underscores how the platform extends its proven cloud optimization capabilities to the fast-growing data platform layer, delivering continuous efficiency.

Secure, Metadata-Only Design

The platform operates in a metadata-only, read-only mode, ensuring optimization occurs without touching production workloads or introducing governance risk. Query text analysis is optional, and metadata collection runs on isolated compute resources. Dedicated service accounts with strictly read-only permissions preserve enterprise control while enabling thorough insights.

Contextual Intelligence Across the Stack

PointFive’s InfraFabric continuously maps cost, usage, telemetry, ownership, and system dependencies into a unified, living model of the enterprise environment. This model allows PointFive’s AI assistant, Pointer, to provide plain-language insights about which workloads drive unnecessary spend, who owns them, potential savings, and recommended remediation. AI Co-Workers extend this capability, continuously monitoring environments, surfacing optimization opportunities, and routing actions to the appropriate teams, all within established governance guardrails.

Continuous Efficiency Across Cloud and Data Platforms

By extending optimization to Snowflake, Databricks, and BigQuery, PointFive enables enterprises to embed efficiency into their cloud and data operations as a continuous practice. Organizations can uncover hidden waste, implement fixes quickly, and redirect resources to AI, innovation, or strategic initiatives.

Enterprises looking to see the platform in action can book a demo to explore how PointFive identifies inefficiencies and delivers measurable savings across cloud infrastructure and data platforms.

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In the modern cloud startup ecosystem, scaling rapidly often clashes with regulatory obligations. Standards such as HIPAA, ISO 27001, and PCI DSS are essential for enterprise adoption and market credibility, yet preparing for audits can strain engineering teams, slow product development, and create operational bottlenecks.

To address these challenges, Automat-it, an AWS Premier Tier Partner and Managed Service Provider (MSP), has joined the Vanta partner program. The integration of Vanta’s trust management platform into Automat-it’s Compliance Guard Suite allows AWS-based startups to automate evidence collection, strengthen continuous monitoring, and accelerate audit readiness, turning compliance from a hurdle into a growth enabler.

From Manual Compliance To Real-Time Trust

Compliance has historically been a manual, reactive process, diverting engineering resources and delaying business objectives. Automat-it’s partnership with Vanta aims to shift that model, embedding automation and real-time monitoring into the compliance workflow.

“For too long, compliance has been a manual drag on engineering teams,” said Noam Rahi, Executive VP of Business Development and Strategy. “By embedding Vanta’s trust management platform into our Compliance Guard Suite, we aren’t just checking boxes, we are helping AWS-based startups prove their security posture in real-time, get audit-ready, close deals faster, and build immediate trust with their own customers.”

Vanta’s agentic trust platform supports over 15,000 organizations, including Atlassian, the Golden State Warriors, Icelandair, and Ramp, in automating workflows for compliance, security, and risk, providing continuous visibility into their security posture.

Compliance Guard Suite With Embedded Automation

Automat-it’s Compliance Guard Suite is designed to help AWS startups assess their environments, identify gaps, implement controls, and continuously monitor compliance. Leveraging DevOps, FinOps, and AI expertise, the suite reduces manual workloads and proactively addresses vulnerabilities.

The integration with Vanta automates evidence collection, generates continuous compliance reports, and provides real-time tracking of security posture. Startups can align with AWS best practices and extend regulatory controls across multiple accounts and regions without additional overhead.

The suite features four framework-specific solutions:

• HealthGuard: Automates HIPAA audits, gap analysis, and breach response

• InfoSure: Simplifies ISO 27001 ISMS implementation and evidence collection

• PayGuard: Ensures PCI DSS 4.0 compliance through automated reporting and vulnerability assessments

• TrustGuard: Centralizes management across multiple compliance frameworks

Vanta’s platform strengthens continuous monitoring across all four solutions, helping scale-ups achieve faster audit readiness and comprehensive regulatory coverage.

Strengthening The Compliance Ecosystem

“We’re thrilled to welcome Automat-it to our Partner Program,” said Bill Fitzgerald, VP, Global Sales Channel and Alliances, Vanta. “By integrating Vanta’s agentic trust platform into their Compliance Guard Suite, Automat-it is helping AWS-based scale-ups automate evidence collection, strengthen continuous monitoring, and accelerate audit readiness, turning compliance into a true growth driver for their customers.”

Vanta centralizes security program management, enriches findings with contextual insights, and provides a single source of truth for remediation tracking, giving startups full visibility and control over risk and compliance.

Compliance As A Growth Lever

For AWS-based startups, compliance is increasingly a differentiator for enterprise adoption and customer trust. By combining Automat-it’s AWS expertise with Vanta’s automated trust management platform, startups can accelerate audit readiness, simplify evidence collection, and turn compliance into a strategic growth engine, helping founders scale confidently while building trust with customers and regulators.

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On February 27, 2026, PointFive announced DeepWaste™ AI, highlighting quantified savings estimates and implementation guidance to move AI efficiency from detection to remediation. The product’s differentiator, PointFive says, is not only finding inefficiency across the full AI execution stack, but translating those findings into quantified remediation steps that align with how engineering and FinOps teams operate.

Why “Visibility” Isn’t Enough in Production AI

Production AI introduces a multi-layered cost and performance system. Waste can emerge from model selection, token consumption, routing logic, caching behavior, GPU utilization, retry patterns, and data platform orchestration. Many organizations can see the effects, spend rising, latency drifting, budgets tightening, but struggle to connect those symptoms to actionable changes across teams.

PointFive’s message is that traditional cloud optimization tools weren’t built to analyze AI-specific execution behavior across these layers. DeepWaste AI aims to close the gap between identifying inefficiency and implementing change, turning optimization into a continuous discipline rather than a set of dashboards.

Agentless Deployment to Reduce Friction

PointFive says DeepWaste AI connects directly to cloud APIs, LLM service metrics, GPU telemetry, and billing systems without agents, instrumentation, or code changes. By default, optimization runs using metadata, billing signals, performance metrics, and resource configuration data without requiring raw inference logs. This design is meant to speed adoption while preserving privacy and minimizing data access requirements.

Organizations that want deeper evaluation can enable optional inference-level analysis to assess prompt architecture and orchestration logic. Customers control the depth of analysis, allowing teams to adapt the module to policy requirements.

The Scope: LLM Services, GPUs, and Data Platforms

DeepWaste AI provides native connectivity across AWS (Bedrock, SageMaker, and AI managed services), Azure (Azure OpenAI, Azure ML, Cognitive Services), and GCP (Vertex AI and AI services), plus OpenAI and Anthropic direct APIs. The module also continuously optimizes GPU infrastructure by identifying underutilized or idle GPUs, instance-type mismatches, OS and driver misconfigurations, and hardware-to-workload misalignment.

On the data side, native support for Snowflake and Databricks extends optimization across AI data platforms from data ingestion through inference. PointFive frames the combined coverage as “full-stack” optimization rather than inference-only visibility.

Findings Structured for Action

DeepWaste AI structures and enriches every invocation with task classification, routing context, cost attribution, and infrastructure alignment signals. It detects inefficiency across four layers: Model & Routing Intelligence, Token & Prompt Economics, Caching & Reuse Optimization, and Infrastructure & Operational Leakage. PointFive says each detection is grounded in unified workload signals rather than surface-level billing anomalies, producing a behavioral understanding of how AI services operate and where efficiency can be improved.

Quantified Savings as a Decision Tool

PointFive emphasizes that DeepWaste AI attaches a quantified savings estimate to every finding. That estimate is intended to help teams make tradeoffs in the way they actually work: deciding which engineering changes to prioritize, which infrastructure adjustments to schedule, and which optimizations to validate first. Rather than treating efficiency as a general aspiration, DeepWaste AI is positioned to make it measurable, with projected outcomes defined upfront.

Implementation Guidance Mapped to Workflows

Alongside savings estimates, PointFive says each finding includes clear implementation guidance, with recommendations prioritized by financial impact and mapped directly to engineering and FinOps workflows. This mapping is a key part of the product narrative. AI efficiency work often falls between teams: engineering owns application logic and routing, FinOps tracks spend, platform teams manage GPUs, and data teams run pipelines. DeepWaste AI is positioned to provide a shared set of signals and actions that connect these responsibilities across the stack.

Tracking Improvements Over Time

PointFive’s positioning goes beyond “identify and fix.” DeepWaste AI is meant to help teams evaluate projected savings before acting and track realized improvements over time. In other words, optimization becomes a continuous practice: detect inefficiency, prioritize by impact, implement changes, and measure the result. PointFive frames this as transforming AI efficiency from reactive cost monitoring into a continuous, measurable discipline across models, infrastructure, and data platforms.

Efficiency Without Sacrificing Control

“AI workloads introduce a new category of operational complexity,” said Alon Arvatz, CEO of PointFive. “DeepWaste AI gives organizations the intelligence required to scale AI efficiently, across models, infrastructure, and data platforms, without sacrificing control.”

DeepWaste AI is now available to PointFive customers.

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Reclaim Security announced $26 million in total funding, including a $20 million Series A led by Acrew Capital, with participation from QP Ventures and Ibex Investors. The company builds a preemptive exposure-remediation platform and says the funding will accelerate its effort to automate remediation that is typically handled through manual workflows, tickets, and change approvals.

The company is targeting a problem developers and security engineers see daily. Detection is easy to scale. Remediation is not. As attacker breakout times have fallen to as little as 27 seconds, enterprises still require an average of 27 days to remediate critical exposures. Even when a vulnerability scanner or cloud posture tool flags an issue instantly, fixing it can mean chasing owners, raising Jira tickets, checking blast radius, and hoping a change does not break production.

“There is a massive ‘Remediation Mirage’ in the market right now. Vendors are slapping an AI label on what is essentially just Prioritization 2.0 or faster ticket management,” says Barak Klinghofer, CEO and Co-founder of Reclaim Security.

​​”The recent launch of Claude Code, which wiped billions from the market value of traditional security giants, is a massive wake-up call. While such tools can identify hundreds of vulnerabilities in seconds, they also hand attackers an autonomous, high-speed engine for exploit generation. We’ve seen reports of AI-orchestrated espionage campaigns where 80-90% of tactical operations were executed autonomously. In this new reality, if your ‘remediation’ strategy still ends with a human reviewing a manual Jira ticket, you aren’t just slow, you’ve lost the race.

Reclaim is the only platform providing true Agentic Remediation. Through our PIPE™ engine, we’ve removed the fear of ‘breaking the business,’ allowing our AI to move from discovery to resolution in seconds. While others are perfecting the recommendation, we are perfecting the execution.”

What Reclaim is shipping

Reclaim describes its platform as introducing the industry’s first AI Security Engineer. The system is designed not only to identify exposures, but to resolve them safely and at scale, which is where most automation attempts fail.

The technical anchor is PIPE™ (Productivity Impact Prediction Engine), a simulation engine that predicts the operational and business impact of a proposed security change before it is deployed. Reclaim says the engine models how changes impact applications, workloads, user productivity, and business processes. The goal is to remove uncertainty around remediation by simulating the likely impact before pushing the change into a live environment.

The company says this simulation-first approach enables organizations to prioritize exposures most likely to be exploited by attackers, deploy automated or semi-automated remediations safely, reduce remediation timelines from weeks to minutes, and eliminate manual configuration and ticket-driven workflows so teams can focus on strategic initiatives.

Reclaim also analyzes how real attack techniques would traverse a specific environment, evaluates how existing defenses would respond, and predicts operational impact before changes are deployed. By combining attack path modeling with business-aware remediation validated through simulation, Reclaim aims to remove exploitable pathways without disrupting critical operations.

Adoption and what’s next

Reclaim says early enterprise customers in financial services, healthcare, government, and critical infrastructure report 80% increase in overall threat resilience, 75% increase in ROI from existing security stack and 90% reduction in manual effort when resolving critical exposures.

“Security tools are excellent at explaining why something is risky,” said Mark Kraynak, Founding Partner at Acrew Capital. “What they don’t do is make remediation safe and practical. The real breakthrough isn’t more prioritization, it’s removing risk without breaking the business. Reclaim does exactly that, and that’s why it matters.”

With the funding, Reclaim plans to expand engineering, deepen integrations, and accelerate go-to-market across North America and Europe. The company will showcase the platform and the “Attacker’s Worst Day” interactive experience at the RSA Conference 2026 Early Stage Expo, Booth ESE #63.

About Reclaim Security

Reclaim Security is an automated threat exposure remediation platform that moves enterprises from detection to execution. By combining AI-driven automation with business-aware simulation, Reclaim enables organizations to eliminate exposures safely, reduce operational risk, and strengthen security posture before attackers can exploit vulnerabilities.

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It was the lunch rush at a Bojangles in North Carolina when the difference became clear. Crew members once scrambled between the speaker and the window, taking orders, handling payments, and making drinks, all under the pressure of a line that never seemed to stop. But on this day, a new team member was on duty: BoLinda, Bojangles’ custom-branded AI Order Taker from Hi Auto. The transformation was immediate. “From day one, the biggest thing I noticed with having Bolinda was that the crew members felt a sense of relief,” says franchise owner Neil Pathak. “The person that used to do cash, take orders, make drinks… they’re just so relaxed now compared to before.”

A Voice AI Built for the Real World

Hi Auto’s platform is purpose-built for the drive-thru, a notoriously challenging environment for AI. Engines roar, conversations overlap, and orders must be taken quickly and accurately. Yet across nearly 1,000 restaurants in the U.S., U.K., New Zealand, and Australia, the system consistently hits 93% order completion and 96% accuracy. “It’s exciting to ramp up the number of restaurants using our AI Order Taker, but that metric represents true success when these transactions provide real value for our QSR customers and their guests,” says Roy Baharav, Co-Founder and CEO of Hi Auto. “What we view as the real achievement here is how Voice AI is improving efficiencies and employee satisfaction while boosting revenue and delivering outstanding guest experiences at almost a thousand drive-thru locations.”

Centralized control allows operators to manage upselling scripts, promotions, and menus across multiple locations while still accommodating regional and limited-time offerings. Labor optimization tools save three to eight hours per store per day, giving staff the bandwidth to focus on other operational priorities.

Turning Chaos into Consistency

At Checkers & Rally’s, the difference is tangible. “Before Hi Auto, we used to have stress trying to run to the front and answer the speaker for the guests and ring up orders and stuff,” says general manager Linda Ronda. Jason Amell, executive for Michigan’s largest Checkers & Rally’s franchisee group, adds, “You always get a nice friendly voice with Hi Auto, versus if you have a [human] order taker, they could be grumpy or in a bad mood, and then that gets conveyed to your guests. Hi Auto eliminates that.”

Bojangles CEO Jose Armario emphasizes the stress reduction at his locations. “You could see immediately the day we put Bolinda in our restaurants: we would see those stress levels come down and we would see the guest experience get better.” He also points to BoLinda’s reliability: “You can set [Bojangles’ custom-branded Voice AI] BoLinda to suggest to sell by day part, by promotion, etc. She’s very consistent and very reliable.”

Upselling with Precision

Upselling, once a variable human skill, has also become predictable with Hi Auto. Ryan Weaver, CEO of Lee’s Famous Recipe Chicken, notes, “You might get an upsell 20% of the time with humans in the drive-thru. With Hi Auto, our voice AI order-taking partner, we’re getting it 100% of the time.”

Rally’s franchise owner Mike Rosania adds, “One of my favorite things about Hi Auto is it actually upsells better than my employees can. And I really thought my employees were top-notch upsellers. Hi Auto just knocks it out the park every day, every shift, day in and day out.” General manager Alisha Burton sums it up: “With Hi Auto, they’re 100% consistent. You will never miss a beat on the upselling, being the friendliest, and giving the customer the best service over the speaker.”

100 Million Orders and Counting

Founded in 2019, Hi Auto has gone from an ambitious startup to a global provider of drive-thru voice AI. Surpassing 100 million orders annually is more than a milestone; it is evidence that AI can operate reliably at scale, improving efficiency, staff satisfaction, and guest experience simultaneously. The drive-thru, one of the most demanding retail environments, now has a partner capable of keeping pace, one friendly, consistent voice at a time.

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At this year’s Global Payments Genius Conference, one of the most anticipated sessions was the “Enterprise Automation at Scale” panel. In a fully attended session by CIOs and industry executives, it quickly became clear why the topic had drawn so much attention.

The collaboration between Genius, Bojangles, and Hi Auto is an enterprise deployment active across almost five hundred restaurants and is now entering its next phase of capabilities.

It was the first time that Bojangles and Hi Auto revealed a brand-new set of features already in production that elevate what is possible in the drive-thru.

What surprised many in the room

The traditional POS API was never designed for Voice AI. It originated as a system built for web-based digital ordering that needs to inject orders and can tolerate some delays. Through the new Genius XPI release, Bojangles and Hi Auto have extended the stability and structure of an enterprise POS platform directly into real-time drive-thru Voice AI.

In conversations with several executives who attended the session, many shared that they assumed native real-time POS-to-Voice AI integration was still years away. But as Richard Del Valle explained on stage, the true barrier was not the conceptual technology. The barrier was achieving consistent reliability across a large distributed operation. The Genius and Hi Auto collaboration appears to have solved that challenge.

The operational capabilities that caught attention

Crystal Deadrick, Hi Auto’s Head of Sales, revealed a new set of native POS and Voice AI capabilities that make the integration seamless and introduce important benefits to the many Bojangles locations using Bo Linda, the brand’s Voice AI system. These capabilities were described as part of what makes the Bojangles drive-thru experience work with such consistency at scale.

Immediate item-by-item injection for faster service time

Items spoken by the guest are added to the order instantly and displayed in real-time on both the drive-thru screen and the kitchen display systems. Guests can confirm or correct their order immediately, and kitchen teams can begin preparing items without waiting for the full order. Kitchen staff can work in real time as the order forms are being filled. This reduces service time and increases throughput across the drive-thru.

Support for dual lane drive-thru operations

Voice AI locations with dual lane drive-thrus often struggle to match each order to the correct vehicle when the lanes merge. Bojangles has solved this issue. Through the Genius XPI and Hi Auto integration, each order now includes an image of the car that placed it. This gives staff immediate visual confirmation and significantly reduces mix-ups during busy periods.

Local routing for true reliability

The XPI interface provides local data routing rather than relying solely on cloud processing. This protects operations from cloud connectivity issues, eliminates latency, and keeps the drive-thru running smoothly. Operators noted that this design is already contributing to further improvements in menu time.

Why franchisees embraced the system

Del Valle noted that franchisees were persuaded by real operational performance rather than theoretical benefits. As the deployment expanded from 100 to 300 to nearly 500 restaurants, order completion rates remained around 93% and accuracy consistently exceeded 96%.

With measurable improvements in speed of service, order flow, and consistency, franchisees increasingly adopted the system as a reliable operational asset.

Why this moment matters for the QSR industry

In discussions with several executives after the session, a shared sentiment emerged. This integration signals a shift in what the industry now expects from automation. Voice AI is no longer evaluated solely on conversational performance. It must now deliver reliability, real-time execution, and deep compatibility with mission-critical systems.

The Genius and Hi Auto collaboration shows that these expectations are now achievable at scale. It raises the standard for what drive-thru automation must deliver and represents a significant step forward for the QSR industry.

Voice AI is an enterprise-grade, mission-critical system that must be optimized and consistent across all parameters: speed, accuracy, latency, procedures, insights and analytics, and learning and training.

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Cybersecurity used to be a numbers game. Count the vulnerabilities, patch the riskiest ones, repeat. Boards measured success by reducing CVE backlogs, security teams measured progress by prioritizing “verified exploited vulnerabilities,” and regulatory frameworks reinforced it. But a growing body of evidence suggests that the foundational assumption behind this strategy of defenders having a reliable view of which vulnerabilities are being exploited is breaking down fast.

The reason isn’t philosophical. It’s architectural.

Modern applications are built on open source. The 2025 Open Source Security and Risk Analysis report shows that 97% of applications include open source components, with roughly 70% of the average codebase originating outside the organization and more than 900 open source components per application. These components accelerate development, but they also multiply exposure with every dependency pulled into a build.

Organizations have long relied on the U.S. Cybersecurity and Infrastructure Security Agency’s Known Exploited Vulnerabilities (KEV) list to illuminate the danger within that complexity. If KEV confirmed a vulnerability was being exploited in the wild, teams could prioritize it over the thousands of others that made headlines but posed no immediate threat.

Miggo Security’s latest research suggests that this logic no longer reflects reality.

The Data That Exposes the Prioritization Illusion

Miggo studied more than 24,000 open-source vulnerabilities from the GitHub Security Advisory (GHSA) database, spanning major code ecosystems such as npm, PyPI, Maven, and RubyGems. Out of those disclosures, Miggo identified 572 vulnerabilities with at least one GitHub-hosted exploit repository, evidence that working exploit code already exists publicly.

The comparison to KEV was jolting: Only 69 of the 572 vulnerabilities with known exploits appear in the KEV catalog. This data indicates that 88% of vulnerabilities with available exploits are not represented in KEV at all.

The disparity widened as Miggo dug deeper. Four hundred seven exploits were weaponized or fully functional, and 165 were proof-of-concept exploits. Yet, KEV contained only 68 of the 407 functional exploits, and just 1 of the 165 proof-of-concept exploits.

For defenders, the implication is unavoidable: most of the exploits that attackers could use today sit outside the system that was designed to help organizations identify what attackers are using.

The KEV list is not inaccurate; it is slow. It tracks confirmation. However, exploitation no longer waits for confirmation.

When AI Turns Every Disclosure Into a Live Threat

Miggo’s white paper notes a structural acceleration in the way attacks occur. In the past, attackers needed time to develop or adapt exploit code. Now, AI-assisted tooling allows exploitation within minutes of vulnerability disclosure, dramatically compressing the space between awareness and action.

The report builds this shift into a broader framework, particularly what Miggo calls the Four Vs of modern exploitation:

• Volume: focusing on vulnerabilities that are actively exploitable in production, scaling automatically to thousands of new CVEs

• Velocity: neutralizing attacks at execution from weeks to minutes, and reducing time to mitigation through AI-driven virtual patching

• Variants: monitoring behavior rather than signature

• Visibility: delivering continuous insight to turn blind spots into actionable intelligence

The upshot is simple: if defenders depend on KEV to know when exploitation starts, they are reacting after attacks have already become possible.

The New Definition of “Defense”: Time to Mitigation

Miggo doesn’t argue that KEV is wrong; only that KEV is now too slow to anchor cybersecurity programs. The company positions a replacement not in data sources but in where protection happens.

Instead of defending based on vulnerability listings, Miggo makes the case for proactive runtime defense. This model evaluates whether a vulnerability is exploitable inside the live application, then automatically deploys AI-generated virtual patching to neutralize the attack path in real time.

The white paper reports that runtime defenses can reduce time-to-mitigation from weeks or months to seconds, stopping exploit attempts even before a patch is available or a vulnerability is cataloged.

The logic shift is profound: Vulnerability knowledge becomes helpful, and exploit interruption becomes essential. Knowing is no longer enough; speed is now the differentiator.

What Success Looks Like in the Next Security Era

KEV has not failed. Instead, technology has simply outgrown the defensive model it supports. Exploitation has become faster than confirmation. The result is a protection gap that attackers exploit more effectively than ever.

Miggo’s research makes a pointed argument: security will no longer be defined by who knows about vulnerabilities, but by who can prevent exploitation first.

The future of cybersecurity will not be measured by how many CVEs an organization patches.

It will be measured by how little time attackers have before their attempts are blocked.

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When Yossi Barishev joined hosts Noa Eshed and Ronen Menipaz on the Real Life Superpowers podcast, the discussion went far beyond technical expertise. It became an exploration of what it truly takes to succeed in cybersecurity during one of its most transformative periods, driven by artificial intelligence, constant disruption, and evolving threats.

Barishev, spotlighted by New York Tech Media and NewsBlaze as one of the most promising leaders in cybersecurity, now heads a stealth-mode company that is reimagining digital identity and trust. His story reflects a new generation of founders who view leadership not only as strategy and execution but as an exercise in emotional intelligence and adaptability.

Experience as the Foundation of Credibility

Barishev’s path to entrepreneurship was built through years of experience leading security operations and advising global organizations at Sygnia and Fireblocks. These roles taught him that in cybersecurity, real credibility is earned through consistent problem-solving and the ability to stay calm under pressure.

He views uncertainty as a growth opportunity rather than an obstacle. This mindset aligns with broader industry shifts. Gartner’s 2025 forecast predicts that worldwide spending on cybersecurity will grow by more than 15 percent next year, driven largely by new investments in data protection and identity management. The increasing complexity of digital threats demands leaders who combine technical skill with the psychological readiness to handle the unknown.

Balancing Stealth with Strategic Visibility

A key topic in Barishev’s conversation with Eshed and Menipaz is the balance between staying under the radar and building credibility. Founders in stealth mode often struggle with the need to remain private while still being visible enough to earn the trust of future partners, clients, and investors.

Barishev believes that a founder’s personal reputation becomes the company’s first trust signal. Maintaining professional visibility, sharing knowledge, and engaging authentically with the community can create early traction even before a public launch. At the same time, he warns against the trap of overvaluing external validation. Founders, he explains, must develop internal conviction to stay focused and make sound decisions amid competing feedback and pressure.

Identity and Trust as the Next Frontier in Cybersecurity

Barishev’s stealth venture focuses on one of the fastest-growing areas of cybersecurity: identity and trust. Artificial intelligence is transforming every layer of digital interaction, but it also amplifies the risk of deception and fraud.

Fortune Business Insights projects that the global identity verification market will exceed $40 Billion by 2030. These figures underscore how critical digital authenticity has become for organizations, governments, and individuals alike.

For Barishev, identity is no longer just a security concern. It represents the foundation of digital society and the trust required for global commerce, communication, and collaboration.

Controlled Chaos as a Tool for Resilience

Throughout the conversation, Barishev highlights what he calls controlled chaos, a mindset that embraces uncertainty as a training ground for adaptability. By purposefully engaging with unpredictable situations, leaders can strengthen their ability to make thoughtful decisions even under intense pressure.

This concept also defines his partnership with co-founder Janan Rosenberg, whom he met during his time at Sygnia. Their collaboration, built on shared trust and complementary strengths, demonstrates how the right relationships can turn uncertainty into innovation. Together, they embody a leadership model that prizes agility, accountability, and courage.

The Human Side of Cybersecurity Leadership

The dialogue between Yossi Barishev, Noa Eshed, and Ronen Menipaz reveals a deeper truth about cybersecurity: the most powerful defense is not technology but the people guiding it. Behind every algorithm and every security breakthrough are leaders who take risks, learn from failure, and stay grounded in integrity.

As AI continues to reshape industries, Barishev’s approach offers a reminder that leadership in cybersecurity is as much about human judgment as it is about code. His philosophy of adaptability, controlled chaos, and unwavering trust provides a model for how today’s innovators can build systems that are not only secure but enduring.

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Artificial intelligence has become the foundation of modern business transformation, but for enterprises, the real challenge lies not in creating advanced models but in running them effectively. As organizations deploy large language models (LLMs) into production, they face steep costs, GPU shortages, and complex infrastructure demands. Impala AI, a Tel Aviv and New York-based company, has raised $11 million in seed funding to solve these challenges by reimagining how enterprises handle AI inference at scale.

The funding round, led by Viola Ventures and NFX, will allow Impala AI to expand its team and accelerate the rollout of its proprietary inference platform. Designed for enterprise environments, the system enables organizations to run LLMs efficiently, securely, and affordably, all within their own virtual private clouds (VPCs).

Why AI Inference Has Become the Enterprise Bottleneck

Most AI discussions focus on training, but the real cost comes from inference, the process of deploying and operating models in production. Each time a user interacts with an AI application, the model runs an inference cycle that consumes compute resources and incurs costs.

According to Canalys, global spending on AI inference is expected to exceed $106 billion by 2025 and reach $255 billion by 2030 (Canalys, 2024). This represents a major shift from training to operations, as inference becomes the dominant cost in enterprise AI.

A recent Dell Technologies report revealed that inefficient GPU allocation and underutilized hardware can inflate inference expenses by up to 40 percent. As enterprises scale their AI workloads, managing these inefficiencies becomes critical to profitability and sustainability.

This is the gap Impala AI is filling, helping organizations run AI workloads faster, cheaper, and more securely, without losing control of their infrastructure.

Redefining AI Infrastructure for Cost and Control

Impala AI’s platform offers a fully managed, multi-cloud, and multi-region inference solution that runs directly inside a customer’s VPC. This design gives enterprises full ownership of their data while maintaining the scalability of a cloud-native environment.

At its core is a custom inference engine capable of reducing the cost per token by up to 13 times compared to traditional platforms. This performance improvement is achieved by optimizing GPU scheduling, automating workload distribution, and reducing idle compute time.

As CEO Noam Salinger, a former executive at Granulate, explained, the company’s mission is to make inference seamless. Teams can focus on building AI products while Impala handles the complexity of scaling, provisioning, and optimizing resources behind the scenes.

The Growing Demand for Efficient AI Operations

Enterprises are increasingly realizing that operational efficiency is the true measure of AI maturity. Research from Intuition Labs, “LLM Inference Hardware: An Enterprise Guide to Key Players”, underscores how inference infrastructure has become a defining factor in enterprise AI competitiveness. Companies that can run AI workloads more efficiently gain a measurable edge in speed, cost, and performance.

Similarly, academic studies such as “From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference” have highlighted that inference, not training, represents the majority of AI’s energy consumption and long-term carbon footprint. This adds another layer of urgency for enterprises seeking sustainable ways to scale AI.

By optimizing inference, Impala AI is not only cutting operational costs but also contributing to more energy-efficient AI infrastructure, a growing priority for global corporations under sustainability mandates.

Governance and Security at the Core

As enterprises integrate AI into critical workflows, data security and compliance have become central concerns. A 2025 arXiv paper titled “Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems” warns that poorly managed inference environments can expose sensitive data and create vulnerabilities in production systems.

Impala AI’s architecture mitigates these risks by enabling enterprises to run inference workloads within their own secure environments. The platform includes built-in auditing, access controls, and compliance features, ensuring full transparency and governance. This approach appeals particularly to industries such as finance, healthcare, and government, where strict data policies make most public AI services unviable.

A Foundation for the Inference Economy

As the market shifts from model innovation to operational deployment, inference infrastructure is emerging as the backbone of enterprise AI. Impala AI’s funding marks a significant milestone in this evolution, positioning the company to lead in what some experts now call the “inference economy.”

The combination of cost optimization, enterprise-grade control, and scalability positions Impala AI at the forefront of this next phase. Its platform bridges the gap between AI experimentation and real-world execution, enabling enterprises to scale confidently and sustainably.

In the coming years, the winners in AI will not only be those with the most powerful models but also those who can deploy them intelligently. With $11 million in fresh funding, Impala AI is building the infrastructure that will make that possible.

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AI agents are rapidly moving from research labs into real-world workflows, transforming how software interacts with data. As this shift accelerates, the very foundations of data architecture are being reimagined. Tiger Data, known for building the database layer that connected billions of IoT devices, has now introduced Agentic Postgres, a database engineered specifically for a new class of intelligent users: autonomous agents that learn, adapt, and operate in parallel.

Conventional databases were built for predictable, linear applications and human-driven processes. But agents behave differently. They branch, explore, and evolve simultaneously, requiring systems that can generate limitless, isolated data environments for safe experimentation and continuous learning, without risking production or ballooning infrastructure costs.

“We defined the database for IoT. With Agentic Postgres, we are defining the database for agents: a new kind of user that requires infinite, reproducible environments to branch, explore, and adapt,” said Ajay Kulkarni, Co-Founder and CEO of Tiger Data. “Tiger already powers Fortune 500 companies in IoT, Web3, and AI, proving its readiness for production at scale.”

A New Foundation for Agentic Workloads

Available now on Tiger, Tiger Data’s fully managed Postgres cloud, Agentic Postgres introduces a key breakthrough: forkable infrastructure. This capability allows developers and AI agents to create instant, copy-on-write branches of both databases and storage volumes, making it possible to run parallel experiments safely and affordably.

Forkable infrastructure combines two capabilities that unlock what Tiger Data calls “safe, instant parallelism” for agent workloads:

• Forkable Databases: Developers can spin up zero-copy branches of Postgres itself (including schema, tables, and rows) in seconds. This enables teams to test new logic, debug issues, or run simulations without risking production data.

• Forkable Volumes: Beyond databases, these forks extend to the entire environment, including storage, embeddings, indexes, and artifacts, so each forked environment is a complete, reproducible snapshot that agents can use as if it were production-ready.

Together, these innovations allow developers to create hundreds or even thousands of parallel environments, paying only for incremental changes rather than full replicas. The design aims to scale toward “effectively infinite parallelism,” aligning with the exponential growth in agent workloads expected in coming years.

Three Primitives for the Agentic Era

In addition to forkable infrastructure, Agentic Postgres introduces three new primitives inside Postgres: Interface, Search, and Memory.

• Interface: A control plane accessible through REST APIs, CLI, and Model Context Protocol (MCP) endpoints, available today.

• Search: Hybrid retrieval capabilities combining vector search (via pgvectorscale, available today) and BM25 keyword search (in public preview).

• Memory: Persistent context for agents (such as conversation history, preferences, and shared state) accessible through APIs and MCP endpoints (public preview).

Together, these capabilities establish the foundation for agent-native applications that can recall, reason, and evolve over time.

“Interface, search, and memory are the foundation. Forkable infrastructure is the breakthrough. Together, they make Tiger the Postgres database for agents,” the company noted in its announcement.

An Alternative to Fragmented Stacks and Lock-In

Today’s AI infrastructure landscape is riddled with complexity. Developers often piece together vector databases, memory stores, and orchestration tools to build agent systems, creating brittle, costly pipelines that are difficult to maintain. Some solutions offer forkable databases but stop short of supporting full environment replication. Others rely on proprietary architectures that lock teams into vendor ecosystems.

Agentic Postgres takes a different approach. Built on Postgres compatibility, it allows developers to leverage the mature Postgres ecosystem while gaining access to Tiger’s next-generation storage layer. The result is a platform that offers the speed, safety, and scalability agents require without sacrificing portability or performance.

The system is now available through Tiger’s new free tier, which gives developers hands-on access to forkable databases, hybrid search, memory APIs, and MCP integration at no cost. The goal: let teams experience the benefits of forkable infrastructure firsthand, then scale seamlessly into production.

A Database for Billions of Agents

Tiger Data’s latest launch marks a defining moment in database evolution. Just as the company built the foundation for IoT connectivity a decade ago, it now aims to power the next computing shift, from billions of devices to billions of intelligent agents.

As Tiger Data puts it: “For the agent era, the database is Tiger.”

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Glilot Capital has announced a $500 million raise backed by institutional investors from the United States, Europe, and Israel, pushing the firm’s assets under management past $1 billion. The new capital will be deployed across Glilot Seed and Glilot Plus, extending the firm’s ability to support both early-stage and growth-stage technology companies in cybersecurity, AI, and enterprise software.

“This raise reflects the confidence global financial institutions have in our unique investment model and track record of performance,” said Kobi Samboursky, Co-Founder & Managing Partner. “We are proud to partner with investors who share our vision of supporting extraordinary entrepreneurs building disruptive technologies that shape the future of enterprise software and cybersecurity.”

Expanding the Leadership Team

The raise coincides with a leadership expansion. Glilot appointed Rinat Remler as its fifth partner, joining as an operating partner with a mandate to strengthen portfolio support and accelerate global scaling. Her addition reflects Glilot’s continued emphasis on combining financial capital with operational expertise.

“We are thrilled to welcome Rinat Remler as our newest partner,” said Co-Founder & Managing Partner Arik Kleinstein. “Rinat brings deep operational expertise and a strong track record of working alongside founders to scale globally. Her addition strengthens our ability to provide hands-on support for entrepreneurs at every stage of their journey.”

A Record Built on Exits

Since its founding in 2011, Glilot has established itself as one of the top-performing venture capital firms globally, with 22 exits to date. Its first fund delivered an unprecedented 8-for-8 exit record.

Portfolio companies include AtBay, a cyber insurance leader; UpStream, a pioneer in automotive cybersecurity; and ScaleOps, a FinOps player that recently raised $58 million. Successful exits include Cider Security, acquired by Palo Alto Networks, and IntSights, acquired by Rapyd7. More recent growth rounds, such as Noma’s $100 million Series B, highlight Glilot’s ability to back companies poised to lead in emerging categories.

“These outcomes highlight Glilot’s ability to identify market-defining startups early and provide the resources and strategic connections needed for accelerated growth,” Samboursky noted.

Scaling Through Value Creation

Glilot’s model extends beyond capital deployment. Its Value Creation department is the largest within the firm, connecting startups with Fortune 1000 executives, supporting go-to-market strategies, and offering deep sector expertise. The structure reflects Glilot’s belief that in today’s environment, operational backing is as critical as funding.

“This new capital not only validates our past performance but also empowers us to continue and grow our Cyber and AI philosophy,” Samboursky said. “We deeply believe in the Israeli ecosystem and will continue to lead investments in the best local and global talent.”

Positioned for What Comes Next

The $500 million raise comes at a moment of both opportunity and caution in venture markets. While global VC fundraising has slowed, cybersecurity and AI remain among the most resilient sectors as enterprises face rising security challenges and widespread AI adoption.

With over $1 billion under management and a leadership team reinforced by Rinat Remler’s appointment, Glilot Capital is positioning itself to shape the next generation of category-defining startups. Its focus on combining financial strength with operational depth suggests the firm intends to back entrepreneurs and scale alongside them as global markets evolve.

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