For Monce, growth created a new infrastructure challenge as its industrial AI platform expanded across customers and sectors, and Automat-it was brought in to deliver the AWS migration outlined in this case study. The move was intended to reduce fixed cloud costs, improve scalability, and make deployment more repeatable as Monce added more enterprise accounts.
The industrial workflow Monce set out to improve
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 company describes the platform as a way to eliminate a large amount of repetitive manual work. Built by operators who typed orders into AS400 for years, Monce says it reduces about 25 minutes of manual data entry per order to under 60 seconds of AI processing. It also reduces order errors from 8% to 12% to under 1% and cuts processing costs by 70%.
Those results helped Monce move from a single factory deployment to multiple enterprise accounts across France while expanding into new industrial verticals. As that happened, the infrastructure supporting the product became a more important part of the company’s overall operating model.
Why the previous Azure environment became limiting
The case study identifies three constraints that were starting to threaten Monce’s growth trajectory.
The first was cost scaling faster than revenue. Azure’s container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spend increased with each new client, even during off-peak hours.
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 the company’s unit economics as it scaled.
The third was manual deployment overhead. Each new client required custom infrastructure configuration. That used engineering time that Monce wanted to put toward product development and its expansion into revenue intelligence and multi-channel ordering.
Taken together, those issues made the existing setup harder to sustain. The infrastructure was no longer just supporting growth in the background. It was starting to shape how efficiently Monce could grow.
The AWS migration delivered by Automat-it
Automat-it identified the potential for significant 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 through Terraform Infrastructure-as-code.
That approach made it possible to create the same infrastructure repeatedly while adjusting configuration for each deployment. For Monce, that meant a more standardized deployment model without losing the flexibility needed for different client environments.
The case study also 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 meant to help 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 AWS environment. WebSocket connectivity between the frontend and backend was handled through an Application Load Balancer.
The results after moving to AWS
The migration produced a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That gave Monce a cost model that responds more directly to actual 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, and continuity mattered for enterprise customers relying on the platform for daily order processing.
Deployment speed also improved. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes. That allowed Monce to support growth across glass, surface treatment, aerospace, and industrial distribution more efficiently than before.
Infrastructure costs also became better aligned with order volume. Instead of rising mainly because another client was added, spending now scales more closely with actual demand.
The infrastructure change behind Monce’s next phase
What stands out in this case study is that the AWS migration addressed multiple business pressures at once. Monce did not simply need a new cloud provider. It needed a setup that reduced fixed cost, improved deployment speed, and supported further expansion without the same level of manual infrastructure work.
Automat-it’s migration gave Monce a more repeatable deployment model and a more flexible cost structure. For a startup growing across customer environments and industrial sectors, those changes create a stronger base for the next stage of expansion.
