// Put Your Data to Work
A Private AI That Answers From Your Own Documents — With Citations
A firm in a regulated, document-heavy industry
The story in briefThe organization sat on years of proprietary and regulatory documents no one could search, while staff were tempted to paste sensitive material into public chatbots. We built a retrieval AI that answers from their own library with sources shown, runs entirely inside their private cloud, and maps directly to industry examination controls.
The situation
The organization held years of proprietary and regulatory documents that no one could search effectively, and staff were increasingly tempted to paste sensitive material into public chatbots to get answers faster. Demand for exactly this capability is loud: a majority of enterprises pursuing data sovereignty have already moved to private AI, and the non-negotiable requirement is always the same — answers must cite their sources, and data must never leave the network.
Why the usual options fell short
Public AI could not see the firm’s data, could not cite anything, and could not be defended to an auditor. Dropping sensitive files into a consumer tool was a compliance incident waiting to happen. And a generic enterprise search box returned links, not answers — it still left a person to read, synthesize, and hope they found everything.
What we built
A retrieval-augmented AI platform over the firm’s own library. Ask a question, and it retrieves the relevant passages first, then answers grounded strictly in those passages with the sources shown — so every answer is traceable, not invented. It runs in the client’s own cloud tenant with per-tenant encryption, no direct server access, and write-once audit logging retained for years.
The part they didn’t expect
Beyond answering questions, the same system drafts routine documents from approved templates — turning a knowledge tool into a production tool — while keeping every draft inside the same governed, logged environment. The controls were designed from the start to map to industry examination requirements, so “is this defensible?” had an answer before anyone asked.
The payoff
- Answers grounded in the organization’s real documents, every one with its sources shown.
- Nothing leaves the client’s control; the architecture maps directly to examination controls.
- A searchable institutional memory that also produces work, not just answers.
- No sensitive file ever touched an outside model.
// is this you?
If this sounds like a problem you recognize — even if you never pictured building your own answer to it — that is usually the sign. Describe your version and a senior engineer will tell you plainly whether it is the kind of thing we build.
Start a project// common questions
Questions about this kind of build
How is this different from using a public AI chatbot?
A public chatbot answers from its training and cannot see your internal documents, so it guesses and cannot cite anything. This system retrieves from your own library first, answers only from what it found, shows the sources, and never sends your data outside your network.
Where does it run?
Inside the client's own private cloud tenant, with per-tenant encryption and no direct server access. For the most sensitive work we also build fully offline versions on local models.
Can it do more than answer questions?
Yes. The same platform drafts routine documents from approved templates, so it becomes a production tool, not just a search box — while every draft stays inside the same governed, logged environment.
// next step
Have a system in mind?
Describe what you are trying to build or fix. A senior engineer reviews every inquiry and responds directly, with a technical read on the problem.