Why not just point ChatGPT at our documents?
A generic AI chatbot over your documents is a fair tool: cheap to try and genuinely useful for finding and summarizing what is already written. Its ceiling is exactly there — it can only be as good as the documents, and the knowledge that makes your expert valuable was never in the documents. DigiME answers from a knowledge base built out of the expert himself: his meetings, his guided intake, his reasoning style — with sources under every answer. Different input, different ceiling.
What a generic AI chatbot actually delivers
Document chatbots (RAG assistants) deliver quick wins: instant search across manuals and reports, summaries, first-line answers for well-documented questions. Setup is fast and the technology is commodity. A document chatbot is enough if your critical knowledge genuinely lives in documents — extensive, current, well-maintained documentation whose problem is findability rather than coverage. Some organizations are truly in that position.
What digiME actually delivers
DigiME is the first use case on the MentX knowledge base: a digital knowledge copy of one specific person. The difference is threefold. Input: the base is built from meetings and a guided intake — capturing reasoning that exists in no document. Style: answers follow how this expert weighs and reasons, not a generic summary voice. Trust: every answer carries full source attribution with dates, and when the base has no grounded answer it says so instead of improvising. Cost, honestly: DigiME requires the capture program — it is not a weekend integration.
A generic AI chatbot vs digiME: at a glance
| Criterion | A generic AI chatbot | DigiME |
|---|---|---|
| Knowledge source | Existing documents only | Meetings + guided intake + documents as context |
| Covers tacit knowledge | No — it was never written down | Yes — that is the point of the capture |
| Answer style | Generic summary voice | The expert's reasoning style |
| Attribution | Sometimes cites documents | Full source attribution, per statement, dated |
| Unknown questions | Risk of confident improvisation | Says so when the base has no grounded answer |
| Setup | Days — commodity technology | A capture program over months |
| Ceiling | Quality of your documentation | Depth of the captured expertise |
When you need both
A fictional but common trajectory: a machine builder rolls out a chatbot over its service documentation. First weeks: enthusiasm — manual lookups got faster. Month three: quiet disuse. The questions technicians actually had ('what's wrong with unit 34 this time?') weren't in any manual, and the bot's confident half-answers cost more trust than its good answers earned. The tool wasn't wrong — it was answering a different question than the one being asked. The document bot later returned, usefully, as exactly what it was: fast search over manuals — while the expertise questions needed a base built from the expert. Two tools, two jobs.
Where MentX fits
MentX builds what the generic bot lacks: the knowledge base itself. Connect and Deepen capture the expert's knowledge into a living, temporal knowledge graph with full source attribution; DigiME is the conversational use case on top. Your documents keep their value as context the graph can reference. How DigiME works →
