Designing an AI agent platform connected to an industrial SME's ERP
In brief
A family-owned industrial SME, facing growing administrative workload and slow commercial response times, asked us to design a platform of AI agents connected to its existing ERP system. The project is being rolled out progressively, module by module.
The challenge
The company manufactures and installs custom technical products. Its commercial inbox receives dozens of requests each week: quotes, technical questions, order follow-ups and complaints, all sorted manually. Pricing relies heavily on manual work: reading drawings, extracting quantities, looking up prices and calculating the final quote. Management has to move between several tools to get an overview of commercial activity.
This way of working takes time, slows response times to customers and leaves room for human error in pricing. The company was not looking for another tool, but for a system that fits into its existing way of working, particularly its ERP, rather than replacing it.
The approach
Three principles guided the design.
AI proposes, the human validates. No agent takes a final action on its own, whether that means sending a quote, updating an order or replying to a customer. Every proposal, such as a draft bill of quantities, a draft reply or an activity summary, goes through human validation before any concrete action is taken in the ERP.
Specialised agents rather than one monolithic system. One agent processes incoming emails, identifies intent and drafts a proposed reply or action. A pricing agent extracts the relevant information from technical documents and proposes a draft bill of quantities. A dashboard agent consolidates data to give management a clear, up-to-date view of activity.
The language model structures, the code calculates. The model reads and structures information from different types of documents, including text, images and tables. The actual quote calculation, applying prices and quantity rules, is handled by conventional, deterministic and verifiable business logic.
The platform connects to the ERP already used every day. Prices, product references and business rules remain defined there, and the agents rely on that source rather than duplicating the information elsewhere.
The result
The email agent and management dashboard agent are operational and are being tested with the teams. The pricing agent already extracts information from technical documents and produces draft bills of quantities for validation. The validation portal is in place and used daily by staff to approve, correct or reject the agents' proposals.
What this shows
This project reflects a particular approach to implementing AI in business. Start from real operational pain points, not technology for its own sake. Fit into existing tools rather than impose an isolated new platform. Keep a human in the loop for decisions that commit the business, while automating repetitive work upstream. Separate reasoning from calculation, and move forward in measurable steps rather than aiming for full automation from day one.
Your ERP already contains much of your business logic. If you want to bring AI into existing processes without giving up control over business decisions, let's discuss your situation.