As Monce grew from a single factory deployment to multiple enterprise accounts across France, Automat-it led the AWS migration at the center of this case study. The project was designed to reduce cost pressure, remove repeated deployment work, and give Monce a more scalable environment for continued growth.
How Monce handles industrial order processing
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 in any format, extracts technical specifications, matches them against product catalogs with customer-specific pricing, and sends them directly into ERP.
The company describes the platform in direct operational terms. Built by operators who spent years typing orders into AS400 systems, Monce says it reduces around 25 minutes of manual data entry per order to under 60 seconds of AI processing. It also lowers order errors from 8% to 12% to under 1% and cuts processing costs by 70%.
Those results helped Monce grow from a single factory deployment to multiple enterprise accounts across France and into additional industrial sectors. But as the company expanded, onboarding new clients still depended too heavily on infrastructure work that had to be repeated each time.
Where deployment friction came from
The case study outlines three constraints in Monce’s previous Azure environment.
The first was cost structure. Azure’s container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spending increased as new clients were added even when workloads were not consistently high.
The second was AI inference cost. Monce’s multi-agent LLM pipeline handles full order conversations, proprietary catalog matching, customer-specific logic, and learned vocabulary and patterns. Running that pipeline on Azure AI services cost more than equivalent AWS alternatives.
The third challenge was manual deployment overhead. Each new client required custom infrastructure configuration. That absorbed engineering time that Monce wanted to use for product development and for its planned expansion into revenue intelligence and multi-channel ordering.
These issues all pointed to the same operational problem. The business was growing, but deployment still carried too much manual friction.
How Automat-it rebuilt the deployment model
Automat-it addressed those issues by migrating Monce to AWS. The implementation was based on Amazon ECS architecture and delivered through Terraform Infrastructure-as-code, making it possible to recreate the same infrastructure while adjusting configuration for each deployment.
The case study notes that Automat-it identified significant cost savings and improved scalability through AWS serverless architecture, including ECS on EC2. That gave Monce a more flexible environment than one built around fixed compute spending.
Automat-it also applied best practices developed through hundreds of AWS migrations for other startups. These included cost optimization supported by FinOps expertise and infrastructure planning built to provide a secure and stable environment as customers scale.
From a technical standpoint, 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 within that environment. WebSocket connectivity between the frontend and backend was achieved through an Application Load Balancer.
Faster rollout for new customer environments
The most visible change was deployment speed. According to the case study, Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes.
Monce also reduced monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. At the same time, the migration was completed with zero client downtime, allowing live industrial deployments to continue uninterrupted.
The case study also says infrastructure costs now scale with order volume rather than increasing simply because another client contract exists. That gave Monce a better link between usage and spending.
For a company expanding across glass, surface treatment, aerospace, and industrial distribution, that shift matters because growth depends not only on signing customers, but also on getting new environments into production efficiently.
How the migration reduced operational overhead
What changed here was not only the hosting environment. The migration also changed the amount of manual effort required to support expansion. Monce moved from a setup where each new client required custom infrastructure work to one where environment creation could be automated and repeated much more quickly.
Automat-it’s work gave Monce lower infrastructure costs, faster customer rollout, and uninterrupted service during the transition. Just as importantly, it reduced the amount of engineering attention tied up in repetitive setup tasks. That makes the case study less about cloud switching in the abstract and more about making growth easier to execute in practice.
