Automat-it’s work with Monce focused on a practical problem explored in the AWS migration covered by this case study: how to support growth without carrying the same level of fixed cloud cost and deployment overhead. As Monce expanded its industrial AI platform across more enterprise environments, the project was designed to improve scalability, cost control, and rollout speed.
Monce’s platform for industrial commercial operations
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.
Built by operators who typed orders into AS400 for years, the platform is designed to reduce the manual workload involved in processing industrial orders. 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% 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 and into new industrial verticals. As growth continued, the company needed infrastructure that would respond more efficiently to how that growth actually happened.
The limits of a fixed-cost cloud model
The case study identifies three main constraints in Monce’s previous Azure environment.
The first was cost scaling faster than revenue. Azure’s container architecture maintained fixed compute costs regardless of processing volume. As Monce added more clients, infrastructure spend increased 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.
The third was deployment overhead. Each new client required custom infrastructure configuration. That slowed rollout and consumed engineering time that Monce wanted to use for product development and for its expansion into revenue intelligence and multi-channel ordering.
Together, those issues meant the cloud environment was not scaling in step with actual demand. Costs and effort were rising, but not in the most efficient way.
The AWS solution Automat-it put in place
Automat-it identified the potential for significant cost savings and improved scalability by migrating Monce to AWS serverless architecture, including ECS on EC2. The migration was based on Amazon ECS architecture and delivered using Terraform Infrastructure-as-code.
That made it possible to create the same infrastructure repeatedly while still applying different configuration for each deployment. It also gave Monce a more flexible operating model for supporting new customer environments.
The case study says Automat-it also applied best practices developed across hundreds of AWS migrations for other startups. These included cost optimization through infrastructure design and FinOps expertise, along with scalability planning intended to support a secure and stable environment.
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 the frontend and backend was handled through an Application Load Balancer.
The results in cost, speed, and scalability
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 setup that responds more directly to actual workload patterns.
The case study also says the migration was completed with zero client downtime, which allowed live industrial deployments to continue without interruption. Another major result was faster rollout. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes.
The case study also notes that infrastructure costs now scale with order volume rather than growing mainly because another client contract exists. That gave Monce a better link between usage and spending while supporting expansion across glass, surface treatment, aerospace, and industrial distribution.
What changed in Monce’s ability to scale
What changed here was the relationship between growth and infrastructure. Monce already had a platform that could reduce manual work and improve operational accuracy for customers. The AWS migration made the company’s own environment behave in a way that was more consistent with that same efficiency.
Automat-it’s work gave Monce lower infrastructure costs, faster deployment, and a model that scales more cleanly with demand. For a growing industrial AI company, that means expansion can happen on a cloud base that is better aligned with actual activity rather than fixed overhead.