What the MCP protocol is
MCP, for Model Context Protocol, is an open standard published by Anthropic. It defines a clean way to connect a language model to a business system: the system declares "tools", precise actions the model can call, and the model uses them instead of guessing.
In practice, your ERP exposes actions such as "find a customer", "list overdue orders" or "prepare a credit note". The model never touches the database directly: it goes through these actions, with your access rights, and every call is logged. The reference documentation is at modelcontextprotocol.io.
What a knowledge graph is
A knowledge graph stores information as nodes and links: a customer is linked to their orders, each order to its lines, each product to its supplier. It is close to how a human connects facts.
This structure changes the quality of answers. A classic text search retrieves passages that "look like" the question. A graph follows the links: to the question "which customers have an overdue invoice on an out-of-stock product", it walks the relations rather than hoping to hit the right paragraph. That is what separates a reliable answer from a merely plausible one.
Why enrich the graph continuously
Your data changes every day. A frozen graph ages and the assistant starts answering wrong without warning. Continuous enrichment connects the graph to the same flows as your ERP: a created order, a changed status, an added document update the graph right away. The assistant stays aligned with reality, without weekend reindexing.
The guardrails, non-negotiable
- The AI assists, it does not decide. It prepares, proposes, computes; a person validates before an action counts.
- Every answer cites its source. The user can trace back to the original data and its date.
- Personal data stays protected. Nothing is sent without necessity, in line with the French data authority's AI guidance and the European AI Act.
- Quality is measured. A sample of answers is reviewed regularly to catch drift, especially after a model change.
Opening the assistant to your users
Once the tool is proven by your teams, the same architecture opens to your end customers: each queries the assistant on their own data, with strict rights and a trace of every exchange. This is the shift from an internal tool to a service, and it is where AI connected to the ERP creates value visible to the customer.
What we know about this architecture
We design and operate this architecture in production: an assistant connected to an ERP through MCP, backed by a continuously enriched knowledge graph, with the guardrails above. That is what lets us talk about it concretely, call costs and edge cases included, rather than in theory.
References
- Model Context Protocol: the open standard for connecting models to systems.
- AI and the French data authority: the French framework on personal data.
- An AI agent connected to your ERP or CRM: the offer that applies this method to your case.