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.