Automat-it Supports Monce’s AWS Migration With Zero Client Downtime

Monce had already built a platform that improved industrial order processing, but scaling it efficiently required a different infrastructure model, which is where Automat-it came in through the AWS migration featured in this case study. The project aimed to reduce infrastructure strain, improve cost efficiency, and support expansion without disrupting live client operations.

The operational role Monce plays for its 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 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 meant to reduce manual work inside industrial order handling. 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 gains helped Monce grow from a single factory deployment to multiple enterprise accounts across France and into additional industrial verticals. But as the company expanded, the infrastructure underneath the platform became more difficult to scale in a way that matched its growth.

The constraints Monce needed to address

The case study identifies three main issues in Monce's previous Azure environment.

The first was cost. Azure's container architecture maintained fixed compute costs regardless of processing volume, which meant infrastructure spending increased as new clients were added even during quieter periods.

The second 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 on Azure AI services was more expensive than equivalent AWS alternatives.

The third was deployment overhead. Every new client required custom infrastructure configuration. That took engineering attention away from product development and the company's expansion into revenue intelligence and multi-channel ordering.

These issues mattered on their own, but they were especially important because Monce was already supporting live industrial deployments. Any infrastructure change had to improve the operating model without interrupting customers already in production.

The AWS architecture implemented by Automat-it

Automat-it addressed those challenges 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 structure made it possible to recreate the same infrastructure repeatedly while changing configuration for each deployment. It also gave Monce a more consistent framework for launching new customer environments.

The case study says Automat-it 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.

From a technical integration standpoint, Automat-it connected 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 during and after the migration

The migration reduced monthly infrastructure costs by eliminating fixed compute spend during off-peak hours through elastic scaling. That improved Monce's infrastructure efficiency while giving the company a more flexible cloud cost model.

The case study also says the migration was completed with zero client downtime. That was one of the most important outcomes because Monce's industrial customers depend on the platform for daily order processing. The move to AWS improved the infrastructure without disrupting the live environments already in use.

Another major result was faster deployment. Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes. For a company expanding across glass, surface treatment, aerospace, and industrial distribution, that created a more practical path for scaling customer operations.

Infrastructure costs also became more closely tied to order volume rather than rising with each additional client contract.

What the migration achieved beyond cost reduction

This case study stands out because it combines infrastructure improvement with continuity in live operations. Monce needed lower costs, better scalability, and faster deployment, but it also needed those gains without interrupting the industrial customers already depending on the platform.

Automat-it's work gave Monce a more flexible AWS environment while preserving service during the transition. That made the migration meaningful not only as a cloud project, but also as an operational change that supported growth without breaking continuity.