Automat-it worked with Monce on the AWS migration examined in this case study as the industrial AI startup expanded across customers and verticals. The project addressed a practical scaling problem. Monce had already built a platform that improved order processing speed, accuracy, and cost efficiency for industrial customers, but its cloud environment was becoming harder to scale cleanly as the business grew.
Monce’s platform and the pressure created by expansion
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.
Built by operators who typed orders into AS400 for years, the platform is designed to reduce manual order handling. Monce says it cuts around 25 minutes of manual data entry per order to under 60 seconds of AI processing. It also reduces order errors from 8% to 12% down to under 1% and lowers processing costs by 70%.
Those results helped Monce grow from a single factory deployment to multiple enterprise accounts across France while expanding into new industrial verticals. As that growth continued, the infrastructure supporting the platform started to show limits that were becoming harder to ignore.
The three constraints in Monce’s Azure environment
The case study identifies three specific constraints.
The first was cost scaling faster than revenue. Azure’s container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spending increased as new clients were added even during off-peak periods.
The second was AI inference economics. Monce’s multi-agent LLM pipeline reads full order conversations, matches them to catalogs using proprietary matching, applies customer-specific logic, and learns vocabulary and patterns. Running that workload on Azure AI services was more expensive than equivalent AWS alternatives, which affected Monce’s unit economics as it scaled.
The third was deployment overhead. Every new client required custom infrastructure configuration. That consumed engineering time that Monce wanted to direct toward product development and its expansion into revenue intelligence and multi-channel ordering.
Together, those issues turned the cloud environment into a scaling constraint rather than just a background technical layer.
The AWS migration designed by Automat-it
Automat-it identified the potential for cost savings and improved scalability by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution implemented by Automat-it’s engineers and DevOps experts was based on Amazon ECS architecture and delivered using Terraform Infrastructure-as-code.
That approach made it possible to create the same infrastructure repeatedly while applying different configuration for each deployment. For Monce, that meant a more repeatable setup for customer rollout without having to rebuild each environment from scratch.
The case study says Automat-it also applied best practices developed across hundreds of AWS migrations completed for other startups. These included cost optimization through infrastructure design and FinOps expertise, along with scalability planning aimed at helping customers grow their applications without affecting their own users.
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 in that environment. WebSocket connectivity between the frontend and backend was handled through an Application Load Balancer.
The operational and cost results
The migration produced a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That made Monce’s cloud spending more responsive to actual usage.
The case study also says the migration was completed with zero client downtime. That mattered because Monce was already supporting live industrial deployments, and continuity was important for maintaining enterprise customer trust.
Another major result was speed. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes. For a company expanding across glass, surface treatment, aerospace, and industrial distribution, that meant growth could be translated into live operations much more quickly.
Infrastructure costs also became better aligned with demand. Instead of rising mainly because another client had been added, spending now tracks more closely with order volume.
What the migration changed for Monce’s next stage
What stands out in this case study is that the AWS move addressed several business pressures at once. Monce did not need a new cloud environment simply to modernize its stack. It needed infrastructure that could support expansion without carrying the same level of fixed cost or manual setup work.
Automat-it’s migration gave Monce a more repeatable deployment model, lower monthly infrastructure costs, and a setup that scales more cleanly with activity. For a company growing across industrial sectors and customer environments, those changes create a stronger foundation for the next phase of expansion.
