Build
AI Assistants
Assistants that answer from your own documents, data and process, not from the open internet.
The problem
A general chatbot is confidently wrong about your business, because it has never seen your standards, your contracts or your job history. The value only appears when the assistant is grounded in your own material and can show where an answer came from.
What we build
- Retrieval over your document set with citations back to source
- Permission-aware access so people only see what they should
- Assistants scoped to a role or a task rather than one that does everything
- Integration into where your team already works, whether that is Teams, Slack or the job system
- Evaluation sets so accuracy is measured, not assumed
How the engagement runs
Four to eight weeks for a first assistant, including the evaluation work. Longer if the document set needs cleaning first, which it usually does.
What you get
- The assistant in your environment
- A measured accuracy baseline
- The evaluation set for future changes
- A documented content maintenance process
Where this does not fit
If the knowledge only exists in people's heads and not in documents, there is nothing to retrieve. That is a documentation project first.
Related solutions
- Workflow AutomationThe deterministic plumbing underneath: systems connected, manual steps removed, processes redesigned around what the tooling can now do.
- AI AgentsAgents that carry out defined operational work end to end, with a person approving the decisions that matter.
- Customer AIThe AI your customers meet: qualification, support and answers grounded in your own knowledge base.
Start with a discovery call.
An hour on how your business runs, where the time goes, and whether AI is worth spending money on yet. You get our read either way.