Company knowledge assistants
Share what your experts know. Without another interruption.
The procedure explains the normal case. Your experienced people know what to do when it goes wrong. We capture both: interviews, decisions, examples and approved documents. Then we build an internal AI knowledge assistant that helps employees find an answer, check its source and know when to ask an expert.
Discuss this workflowWhat this could look like
Illustrative scenario, not a client case study
A new coordinator needs to handle an unusual customer request. The assistant retrieves the approved procedure, explains the relevant exception and links to the source. It identifies the responsible expert when a decision is outside its scope. That expert can correct the underlying knowledge so the next employee receives a better answer.
What you receive
- A practical knowledge playbook: expert decisions, terminology and exceptions
- Source inventory with accountable owners, review dates and access requirements
- An internal assistant that retrieves permitted information and cites its sources
- Answer-quality tests, expert escalation and a process for keeping knowledge current
How we measure whether it works
Evaluate whether employees can find a correct, current answer faster. Track source coverage, escalations, expert review effort and unresolved questions. Test restricted material using different user roles.
What to know before you start
An AI assistant cannot recover expertise nobody has captured or make outdated documents authoritative. Retrieval-augmented generation (RAG) supplies relevant evidence to a model; it does not guarantee correctness. Experts remain reviewers and owners.
Common questions
What do you mean by knowledge mining?
We mean capturing approved business know-how through expert interviews, work observation and document review. It is different from statistical data mining, which looks for patterns in structured datasets.
Do you train a new model on our company data?
Often that is unnecessary. Retrieval can connect a suitable model to approved sources without training a new foundation model. Deployment, retention and provider data-use settings are reviewed for each engagement.
Bring us one process worth improving.
Describe the work, the systems it touches and the result you want. We can explore fit before proposing a defined scope and fee. Do not include confidential records.
We agree on scope, acceptance tests and commercial terms before work begins. Implementation, third-party usage and ongoing support are itemized.