Most organisations now run several AI tools that answer questions about their systems: a coding assistant, a service desk bot, a partner's analysis tool, an internal copilot. Each one builds its own partial picture of the estate. Their answers conflict, and none of them cite sources.
For an enterprise architect asked to govern AI, that's an uncomfortable position. You're accountable for answers you can't check.
One model, many tools
The fix is to give every tool the same source of truth. Vera's knowledge layer exposes the compiled model of your estate, the same one Vera uses, so other copilots and agents can query it.
- Citations included: answers carry the source lines they came from.
- Gaps passed through: tools are told what isn't known, so they stop guessing.
- Compact context: tools retrieve small, relevant facts instead of loading whole programmes, which keeps prompts and bills smaller.
What governance looks like
Once your tools share one model, governance stops being a policy document and becomes something you can see. You can ask which answers were grounded and which were inferred. You can see what your AI doesn't know. You can decide where agents are safe to act.
You can't govern AI that's answering from its imagination. You can govern AI that's answering from your map.
Where to start
Pick the system your AI tools are asked about most. Compile it, point your tools at the model, and compare their answers before and after. The difference is usually obvious within a week.