By: Jake Smiths

The challenge for Monce was no longer only product performance, but also whether its infrastructure could keep up with growth. This led the company to work with Automat-it on an AWS migration highlighted in this case study. The project focused on improving deployment timelines, managing cloud costs more effectively, and supporting expansion with less manual setup.

The Order Processing Workflow Monce has improved

Monce runs B2B commercial operations for industrial groups across sectors such as construction, glass manufacturing, surface treatment, aerospace, aluminum, and distribution. Its proprietary multi-agent pipeline reads inbound orders in different formats, extracts technical specifications, matches them against product catalogs with customer-specific pricing, and sends the results to ERP systems.

Built by operators experienced with manual order-entry systems such as AS400, the platform is designed to reduce repetitive data-entry tasks. According to the case study, the system can significantly reduce the time required to process orders, reduce manual errors, and improve operational efficiency.

These improvements supported Monce’s expansion into enterprise accounts across France and into additional industrial verticals. As customer demand increased, deployment speed became a more central concern. The company needed a way to bring new environments online without repeated custom setup.

The Three Deployment-Related Constraints

The case study outlines three main challenges in Monce’s previous Azure environment.

The first involved cost structure. Azure’s container architecture maintained relatively fixed compute costs regardless of processing volume, which made it harder to align infrastructure spending with usage patterns as new clients were added.

The second was inference cost. Monce’s multi-agent LLM pipeline processes full order conversations, applies catalog matching, and incorporates customer-specific logic. According to the case study, running this workload on Azure AI services resulted in higher costs compared to alternative configurations.

The third was deployment overhead. Each new client required a degree of custom infrastructure configuration, which slowed rollout and required engineering resources that Monce preferred to allocate toward product development and expansion into areas such as revenue intelligence and multi-channel ordering.

Together, these factors made scaling operations more resource-intensive.

How Automat-it Rebuilt the Deployment Process

Automat-it addressed these challenges by migrating Monce to an AWS-based architecture, incorporating services such as Amazon ECS and Infrastructure-as-code through Terraform.

This approach created a more repeatable process for deploying infrastructure while allowing flexibility in configuration for each client. Instead of treating each environment as a largely manual setup, Monce moved toward a more standardized and automated rollout model.

The case study notes that Automat-it applied practices developed across prior AWS migration projects, including cost management strategies and infrastructure planning intended to support scalability and stability.

At the technical level, Monce’s existing Firebase frontend was integrated with AWS ECS. Its FastAPI-based backend operated within this environment, with WebSocket communication managed through an Application Load Balancer.

Changes in Rollout Speed and Cost Management

One of the outcomes highlighted in the case study is improved deployment efficiency. By using Infrastructure-as-code, Monce automated parts of the environment setup process for new clients, reducing the time required compared to previous methods.

The migration also contributed to more flexible infrastructure scaling. According to the case study, this helped align cloud costs more closely with actual usage, particularly during periods of lower demand.

The transition was completed without reported disruption to active client operations, allowing ongoing deployments to proceed during the migration.

What Changed in Monce’s Rollout Capacity

This case study illustrates how infrastructure design can influence the pace of customer expansion. Monce already had a platform aimed at reducing manual work for industrial customers. The AWS migration allowed the company to apply similar efficiencies to its own deployment processes.

Automat-it’s work supported improvements in deployment workflows, cost management, and infrastructure repeatability. For a company expanding across multiple factories, customer accounts, and industrial sectors, these changes contributed to a more manageable and scalable operational model.

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.