By: Jake Smiths
Scaling an industrial AI platform often means solving infrastructure issues that do not appear in the early stages of growth, which is where Automat-it worked with Monce on the AWS migration examined in this case study. As Monce expanded into more enterprise accounts, the project focused on reducing manual infrastructure work, improving cost efficiency, and supporting faster deployment.
The Industrial Process Monce Automated
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 the results directly into the ERP.
Built by operators who typed orders into the AS400 for years, the platform is designed to remove a large amount of repetitive order-entry work. According to the company, Monce reports that its platform reduces manual data entry time per order from around 25 minutes to under 60 seconds of AI processing. The company also states that it reduces order errors from 8% to 12% to under 1% and lowers processing costs by approximately 70%.
Those reported results helped Monce grow from a single factory deployment to multiple enterprise accounts across France and into new industrial verticals. As that expansion continued, however, the company was still relying on a cloud environment that required repeated infrastructure effort for each new client.
The Constraints In The Previous Setup
The case study identifies three specific challenges in Monce’s previous cloud environment.
The first was a fixed computing cost. The company’s previous container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spend increased with each new client, even during off-peak hours.
The second was the AI inference cost. Monce’s multi-agent LLM pipeline reads full order conversations, performs proprietary catalog matching, applies customer-specific logic, and learns vocabulary and patterns. According to Monce, running that workload on its previous cloud provider was more expensive than the AWS alternatives it evaluated for this specific use case.
The third was manual deployment overhead. Every new client required a custom infrastructure configuration. That meant engineering resources were being pulled into repeated setup work instead of being focused on product development and Monce’s expansion into revenue intelligence and multi-channel ordering.
These issues made infrastructure harder to manage as Monce added customers. The platform itself reduced manual effort for clients, but the environment behind it still carried too much manual effort internally.
The AWS Migration Delivered By Automat-It
Automat-it addressed those issues by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution implemented by Automat-it’s engineers and DevOps experts was based on Amazon ECS architecture and delivered using Terraform Infrastructure-as-Code.
That structure made it possible to create the same infrastructure repeatedly while applying a different configuration for each deployment. For Monce, that meant infrastructure could become more repeatable and less dependent on custom setup for every new client environment.
The case study says Automat-it also applied best practices developed across its experience as an AWS Premier Tier Services Partner supporting hundreds of startup customers. These included cost optimization through infrastructure design and FinOps expertise, as well as scalability planning to support a secure and stable environment.
At the technical level, 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 Changes In Cost And Deployment Speed
According to the case study, the migration produced a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That improved Monce’s cloud efficiency and made spending more responsive to actual demand.
The case study also says the migration was completed with zero client downtime, allowing live industrial deployments to continue uninterrupted. Another reported result was deployment speed. Terraform Infrastructure-as-Code automated environment creation for each new factory, with the company reporting that new client deployment was reduced from days to minutes.*
The case study also notes that infrastructure costs now scale with order volume, rather than rising mainly because another client contract has been added. That created a closer link between usage and spending.
What Changed In Monce’s Operating Process
What changed here was not only the hosting environment, but also the amount of internal work required to support growth. Monce moved from a setup where each new client involved repeated infrastructure effort to one where environment creation could be automated and reproduced more easily.
Automat-it’s migration reduced Monce’s infrastructure costs, accelerated rollout, and provided a more repeatable way to support customer expansion. For a company growing across industrial sectors, this made the infrastructure better suited to the pace and pattern of that growth.
Disclaimer: This article is based on a case study provided by the companies mentioned. Results and outcomes described may vary depending on factors such as implementation, use case, and business environment.
