Ask in your own words
No field names, no IDs, no need for the right search mask. The chat recognises where in the application you are – “this record” and “the displayed entries” are resolved correctly.
Delivered in a customer project
AI Chat for Your Business Application
Your grown business application gains a language interface. Users query their data the way the question arises in daily work – no search masks, no knowledge of database fields, no internal IDs. And without the AI ever receiving permissions of its own.
The knowledge is in the database – but hardly reachable
In many companies the most valuable knowledge sits in business applications that have grown over years: inventory, test data, contracts, spare parts. Anyone who wants to find something has to locate the right search mask, know the right fields and assemble the results themselves. In practice that knowledge is reachable only for those who know the structure of the application – and those are usually few.
An AI chat turns this around: the application understands the question, instead of the user having to understand the application.
Where this fits
Anywhere structured data sits in a business application and is regularly queried, compared and maintained:
- Inventory and asset data
- Test, measurement and calibration data
- Contract and supplier data
- Spare part and material master data
- Laboratory and equipment databases
- Plant and maintenance data
What the assistant can do
Answers in a fitting form
If the question calls for a comparison, a table comes back. Exceptions and incomplete records do not quietly disappear from the result – they are marked and explained.
Changes only with approval
Changes arrive as a proposal first – with a before-and-after comparison and a permission check. Nothing is saved until you explicitly confirm.
Automatic document processing
Upload PDFs and images straight into the chat. A document AI extracts the relevant values and parameters, classifies the content and turns it into a structured proposal.
Personal conversation history
Conversations and attachments are stored per user. Earlier analyses can be reopened and continued.
Your enterprise chat too
Because the integration runs over MCP, our chat is not the only possible entry point: your company-wide AI assistant can use the same functions. Permission checks, approval and audit logging sit in the interface, not in the chat – and therefore apply regardless of which AI assistant accesses them.
Why this holds up in the enterprise
Generative AI on production inventory data is only as good as its control mechanisms. Four things are not optional here:
- Approval before every change The assistant never saves unnoticed. It shows a concrete before-and-after comparison and executes the change only after explicit confirmation.
- The existing rights system applies The chat receives no special privileges. Existing permissions are checked before every change – and again immediately before saving.
- Limited data access The language model never reaches the database directly, only through clearly defined search, analysis and modification functions of an MCP interface. Sensitive fields can be excluded from the model’s access at the interface itself.
- Complete audit logging Proposals, confirmations, rejections, conflicts and executed changes are logged. AI-assisted changes can be marked as such in the system.
On a lighter note
“Who owns the keys?” – “We do.” Control is easy to claim. We made a short film about how that sounds in meetings.
Which application do you have?
If knowledge is stuck in a business application at your company, a conversation is worth it. We look at your data structure and tell you honestly whether the pattern holds.