The same question, two different answers
An engineer asks: does the product withstand 85 degrees? General AI answers from general knowledge of electronics, plausible but ungrounded. Colleag reads your datasheet and your test report and answers with the exact figure and source, or says the evidence is missing. For a customer promise, that is the difference between a maybe and an answer.
| General AI (ChatGPT, Copilot) | Colleag | |
|---|---|---|
| Knows your articles and products | No, answers from general knowledge | Yes, reads your article register, your BOMs and your documents |
| Source references | Sometimes, from open sources | Always, to your own material |
| Reads the business system | No | Yes: stock, orders, suppliers, outcomes |
| Publishes anything itself | The user pastes it in | No, everything passes human approval |
| Where your data ends up | With the service provider, on their terms | In your own archive that you own and can export |
| Roles and permissions | One chat for everyone | Colleagues per role, with your permissions |
But the ERP and PLM already have their own AI, right?
Yes, and it is often good at its own system. That is also its limit: the ERP's AI sees the ERP, the PLM's AI sees the PLM. Your questions rarely stay on one island. What happens if we change supplier for the article? The answer needs the BOM from PLM, prices and orders from the business system, the requirement spec on the file server and the standard that governs.
The hub between your data islands
Colleag connects the business system, PLM, SharePoint, file servers, standards and the outside world, and gathers the company's context in one place. From that whole, the colleagues create answers with sources, reports and optimized specifications. It is a whole a built-in AI cannot reach, however good it is on its own island. And the list is not locked: when you need more connections you choose them yourselves, through open standards like MCP, including your own development tools.
| Built-in AI in ERP or PLM | Colleag | |
|---|---|---|
| Sees | Its own system | All your sources together |
| Cross-cutting questions | Stops at the system boundary | BOM, orders, requirements and standard in one answer |
| New connections | The vendor's roadmap | You choose, via open standards like MCP |
| Your documents | Outside | Sources cited in every answer |
Key benefits
- Answers from your own material, with a source in every answer
- Business system, documents and standards in the same question
- Nothing is published without human approval
- Your archive stays yours: open format, exportable at any time
Read more
Is Colleag just another AI assistant? No. The difference is the material: the colleagues read your sources together, where each built-in AI sees its own island.
A general AI answers from general knowledge. A built-in AI answers from its own system. The colleagues answer from the whole: the business system, the documents, the standards and the outside world, with a source per claim.
And the connections are not locked: new systems connect through open standards such as MCP, including your own development tools.
Against the systems' own AI
- ERP
- The ERP's AI sees the ERP. The colleagues read it together with the documents and the standards.
- PLM
- The PLM's AI sees the PLM. The cross-question needs the BOM, the order and the requirement in one answer.
- LCM
- Lifecycle status is one piece. The whole also says where the article is used and what replaces it.
- Project management systems
- The project tool's AI automates the board. Here the legwork is automated against the article, the drawing and the procedure.
What only the whole can do
- Cross-questions in one answer, with sources
- Connections you choose yourselves, through open standards such as MCP
- Nothing published without human approval