The move from Azure to AWS marked an important step in how Monce prepared its infrastructure for broader growth, withAutomat-it leading the migration explored in this case study. As the company expanded across enterprise accounts and industrial verticals, the project focused on lowering fixed costs, improving deployment speed, and building a more scalable operating foundation.
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 the result directly into ERP.
Built by operators who typed orders into AS400 for years, the platform is positioned as a way to reduce repetitive order entry work. 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 supported Monce’s expansion from a single factory deployment to multiple enterprise accounts across France and into new industrial verticals. But that expansion also exposed the operational cost of relying on a deployment process that still required custom infrastructure work for each customer.
The deployment bottlenecks in the previous setup
The case study outlines three specific constraints in Monce’s Azure environment.
The first was cost structure. Azure’s container architecture maintained fixed compute costs regardless of processing volume, which meant infrastructure spending rose 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, 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 itself. Every new client required custom infrastructure configuration. That slowed rollout and consumed engineering time that Monce wanted to spend on product development and its expansion into revenue intelligence and multi-channel ordering.
These issues all fed into the same operational problem. Monce had a product that could support more customers, but the environment behind it was making each new deployment more labor-intensive than it needed to be.
How Automat-it changed the deployment model
Automat-it addressed that problem by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution was based on Amazon ECS architecture and delivered using Terraform Infrastructure-as-code.
That gave Monce a repeatable way to create infrastructure while still applying different configuration for different deployments. Instead of treating each new client environment as a separate setup exercise, the company could move toward a more automated rollout process.
The case study also says Automat-it applied best practices developed across hundreds of AWS migrations completed for other startups. These included cost optimization supported by FinOps expertise and scalability planning aimed at creating 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 in that AWS environment. WebSocket connectivity between the frontend and backend was handled through an Application Load Balancer.
The effect on rollout speed and infrastructure efficiency
The most visible result was deployment speed. According to the case study, Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes.
The migration also produced a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. At the same time, Monce completed the migration with zero client downtime, allowing live industrial deployments to continue without interruption.
The case study also says infrastructure costs now scale with order volume rather than rising mainly because another client contract has been added. That gave Monce a better connection between actual usage and cloud spending.
For a company expanding across glass, surface treatment, aerospace, and industrial distribution, those changes mean new customer environments can be launched more quickly and supported on a more efficient infrastructure base.
What changed in Monce’s rollout capability
What changed here was not just the cloud environment, but the way Monce could support expansion operationally. The migration replaced a setup that required repeated manual configuration with one built around repeatable infrastructure creation.
Automat-it’s work gave Monce faster deployment, lower infrastructure costs, and a more scalable process for bringing new client environments online. For a growing industrial AI company, that kind of rollout capability becomes an important part of how expansion is executed in practice.