Monce reached a point where its cloud environment needed to better match the speed and complexity of its expansion, and Automat-it helped guide the AWS migration featured in this case study. The project was designed to reduce fixed infrastructure costs, improve deployment repeatability, and create a setup that could scale more effectively with customer demand.
The platform Monce brought to industrial customers
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 them directly into ERP.
The company says the platform replaces about 25 minutes of manual data entry per order with 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%. Built by operators who typed orders into AS400 for years, the system is designed around the realities of industrial order handling rather than generic automation.
Those results helped Monce move from a single factory deployment to multiple enterprise accounts across France and into additional sectors. As the company grew, it needed infrastructure that could adapt more easily to different workloads and customer environments.
Where the old cloud model was falling short
The case study identifies three constraints in Monce’s Azure environment.
The first was fixed compute spending. Azure’s container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spending increased as new clients were added even when demand was not consistently high.
The second was inference economics. Monce’s multi-agent LLM pipeline reads full order conversations, performs proprietary matching against catalogs, applies customer-specific logic, and learns vocabulary and patterns. Running that on Azure AI services was more expensive than equivalent AWS alternatives.
The third was deployment overhead. Each new client required custom infrastructure configuration. That meant engineering time was still being used on repeated setup work rather than on product development and Monce’s expansion into revenue intelligence and multi-channel ordering.
Together, those issues limited flexibility. Costs did not move cleanly with demand, and deployment work did not scale in a repeatable way.
The AWS setup Automat-it implemented
Automat-it addressed those problems by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution was based on Amazon ECS architecture and delivered through Terraform Infrastructure-as-code.
That gave Monce a repeatable infrastructure pattern while still allowing different configuration for different deployments. In practical terms, it created a setup that could be reproduced more easily as Monce added new factories and customer environments.
The case study 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 intended to support a secure and stable environment.
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 there. WebSocket connectivity between frontend and backend was handled through an Application Load Balancer.
The results after migration
The migration delivered a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That gave Monce a more flexible cost model, one better aligned with actual usage.
The case study also says the migration was completed with zero client downtime. That preserved continuity for live industrial deployments already running on the platform.
Another important result was faster deployment. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes. Infrastructure costs also became more closely tied to order volume rather than rising mainly because another client had been added.
What flexibility meant for Monce
This case study shows that flexibility in infrastructure is not only about handling more traffic. It also affects how efficiently a company can deploy new environments, manage cost, and support broader expansion. Monce already had a platform that improved performance for industrial customers. The AWS migration helped the infrastructure behave in a way that better matched that same operational efficiency.
Automat-it’s work gave Monce a setup that was more repeatable, more cost-responsive, and easier to extend as the company grew. For an industrial AI startup expanding across sectors, that created a more practical infrastructure foundation for continued scale.
