By: Jake Smiths
With more enterprise accounts and more industrial deployments to support, Monce needed a cloud environment that was easier to scale, and Automat-it helped deliver that shift through the AWS migration profiled in this case study. The project addressed cost pressure, deployment overhead, and the need for a more repeatable infrastructure model as the business expanded.
The Order Processing Platform Monce Built
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 designed to reduce manual order handling. According to the company, Monce’s platform reduces manual data entry time per order from around 25 minutes to under 60 seconds of AI processing. The company also reports a reduction in order errors from 8% to 12%, down to under 1%, along with a reported 70% decrease in processing costs.
Those reported gains helped the company expand across France and enter additional industrial verticals. As that expansion continued, however, the infrastructure underlying the product needed to handle more customers, more environments, and greater demand variability.
The Constraints That Emerged In The Previous Cloud Environment
The case study identifies three specific issues in Monce’s previous cloud environment.
The first was cost scaling faster than revenue. According to Monce, the previous container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spending increased as new clients were added, even during off-peak hours.
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. According to Monce, running that workload on the previous cloud provider’s AI services was more expensive than alternatives available through AWS.
The third was deployment overhead. Every new client required a custom infrastructure configuration. That consumed engineering time that Monce wanted to use for product development and its expansion into revenue intelligence and multi-channel ordering.
These issues made the existing setup harder to scale cleanly. Growth was driving more demand, but the cloud environment was not responding as efficiently.
The AWS Architecture Automat-It Implemented
Automat-it addressed those constraints by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution delivered by Automat-it’s engineers and DevOps experts was based on the Amazon ECS architecture and implemented using Terraform Infrastructure-as-Code.
That gave Monce a repeatable way to create infrastructure while still allowing different configurations for each deployment. Instead of relying on a setup that required fresh manual work for each client, Monce gained a more standardized infrastructure model.
The case study also says Automat-it applied best practices developed across its experience with AWS migrations completed for other startups. These included cost optimization through infrastructure design and FinOps expertise, as well as scalability planning 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 the frontend and backend was handled through an Application Load Balancer.
The Results After The Migration
According to the case study, the migration reduced monthly infrastructure costs by replacing fixed compute spend with elastic scaling during off-peak hours. That gave Monce a cost model better aligned with actual usage.
The case study also reports that the migration was completed with zero client downtime, allowing live industrial deployments to continue uninterrupted. Another reported result was faster deployment. Terraform Infrastructure-as-Code automated environment creation for each new factory, reducing new client deployment time from days to minutes, according to Monce.
Infrastructure costs also became more closely tied to order volume, rather than increasing mainly because another client contract had been added. That improved the relationship between demand and cloud spending as Monce expanded.
How The Migration Supported Broader Growth
This case study shows that scalability depends on more than application performance alone. Monce already had a platform that, according to the company, reduced manual work, improved order accuracy, and lowered processing costs for customers. What it needed next was infrastructure that could support a growing number of deployments without the same level of fixed cost and repeated setup effort.
Automat-it’s work reduced Monce’s infrastructure costs, accelerated rollout, and enabled a more scalable AWS environment. For a company expanding across industrial sectors and enterprise accounts, this created a stronger base for continued 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.
