Monce’s expansion into more enterprise accounts exposed cost and deployment limits in its existing cloud environment, which led the company to work with Automat-it on the AWS migration covered in this case study. The project focused on improving infrastructure economics, reducing deployment overhead, and creating a setup that could scale more efficiently with demand.
The industrial workflow Monce automated
Monce serves 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 had spent years typing orders into AS400 systems, and Monce presents it as a way to compress manual effort in industrial order handling. The case study says the platform replaces around 25 minutes of manual data entry per order with 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 outcomes helped the startup expand from a single factory deployment to multiple enterprise accounts across France while moving into new industrial verticals. As adoption increased, however, the economics of the underlying infrastructure became more important.
The cost pressures in the previous setup
The first issue was fixed compute cost. Azure’s container architecture maintained fixed spending regardless of actual processing volume. As Monce added more clients, that meant infrastructure costs rose even during periods when workloads were not running at full demand.
The second issue was AI inference economics. 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 workload on Azure AI services cost more than equivalent AWS alternatives, which directly affected unit economics as the company scaled.
The third issue was deployment labor. Each new client required custom infrastructure configuration. That slowed rollout and diverted engineering time away from product development and Monce’s planned expansion into revenue intelligence and multi-channel ordering.
Together, those pressures meant the infrastructure question was not only technical. It was affecting the efficiency of the business model behind the platform.
The AWS environment Automat-it implemented
Automat-it migrated Monce to AWS using Amazon ECS architecture delivered through Terraform Infrastructure-as-code. That created a repeatable infrastructure model while still allowing different configuration for each deployment.
The case study says Automat-it identified the opportunity for significant cost savings and improved scalability through AWS serverless architecture, including ECS on EC2. For Monce, that meant shifting toward a more elastic environment, one able to respond better to workload variation instead of preserving the same level of fixed spend during quieter periods.
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, as well as scalability planning intended to support secure and stable growth.
At the application layer, 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 frontend and backend was handled through an Application Load Balancer.
Better alignment between usage and infrastructure spend
The migration produced results that directly addressed Monce’s earlier cost and deployment pressures. Monthly infrastructure costs fell because elastic scaling eliminated fixed compute spend during off-peak hours. That gave Monce a cost structure better aligned with how demand actually moved across its platform.
The company also completed the migration with zero client downtime, according to the case study. That preserved continuity for live industrial deployments and avoided disruption for enterprise customers relying on Monce for daily order processing.
Another major result was deployment speed. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client rollout from days to minutes. That made customer expansion less dependent on manual setup work and more consistent operationally.
The case study also says infrastructure costs now scale with order volume rather than growing mainly because more client contracts exist. That distinction matters because it ties cloud spending more directly to actual activity.
What improved in Monce’s operating model
This case study shows how infrastructure design can shape the economics of scaling an AI product. Monce already had a platform that reduced manual work, improved order accuracy, and lowered processing costs for industrial customers. The AWS migration extended that logic into the company’s own operating model.
Automat-it’s work reduced fixed cloud overhead, improved deployment speed, and created a setup that better matches business demand. For Monce, that means growth across new customers, verticals, and geographies can happen on a more efficient infrastructure base than before.
