Consider the seemingly simple question, “Which expense policy applies to this trip?” The answer may depend on the employee’s entity, the travel date, a local addendum, and whether a newer policy supersedes the one that search returned. Finding a relevant paragraph is only part of the job.
We approach knowledge assistance as an information system. The documents have owners and lifecycles. The user has permissions and a context. The response needs evidence, and sometimes the most useful response is a request for clarification rather than an answer.
Selected for the workload—not prescribed as a single mandatory stack. Explore the technology ecosystem ↗
A question across three policy versions
An employee asks whether a supplier visit requires advance approval. The repository contains an old global policy, a current regional policy, and a team-specific operating note. A naive semantic search can retrieve all three because they use similar language.
A stronger pipeline filters by the employee’s permitted sources, considers effective dates and document status, and retrieves the relevant approval passages. If the team note contradicts the governing policy, the response identifies the conflict instead of blending them into a new rule.
The interface should show the policy title, version, relevant passage, and source link. The employee can inspect the evidence, while the policy owner receives a useful signal when ambiguous documentation repeatedly causes questions.
Question + user context + permitted policy set
Source-backed guidance or a clearly stated conflict
Freshness is an architectural responsibility
A source connector needs more than an initial import. Updates, revoked permissions, deleted files, and superseded versions must reach the index. Keep source identifiers and version metadata alongside chunks so a document can be replaced or removed without leaving orphaned content behind.
The source system remains authoritative. An index is a derived view, and citations should resolve to material the user can still access. We test both the answer path and the lifecycle path: what happens tomorrow when the document changes, not only what happens today when ingestion succeeds.
Make the knowledge usable
We design ingestion around the shape of your material: structured records, PDFs, tables, scanned documents, and frequently changing content. Ownership, version history, metadata, and deletion rules are part of the pipeline, not an afterthought.
Retrieve before generating
RAG brings selected source material into the answering process. Vector search can help with meaning; keyword search can preserve exact names and codes. We evaluate chunking, hybrid retrieval, and reranking against questions your teams actually ask.
Respect who can see what
A shared index must not become a shared permission bypass. Access checks need to apply to retrieval and source links, with tenant separation and testing for cross-user exposure. Where evidence is missing, the interface should make that limitation visible.
Keep the answer connected to its source
Useful responses include relevant citations and enough context to check them. We distinguish current documents from superseded ones and test whether a cited passage really supports the answer rather than treating the existence of a link as proof.
Choose the approach for the constraint
| When this matters | An approach to consider | What not to assume |
|---|---|---|
| Policies change frequently | Version-aware retrieval and scheduled or event-driven refresh | Do not use fine-tuning as the primary update mechanism. |
| Names and product codes matter | Hybrid lexical and semantic retrieval | Semantic similarity alone can miss an exact identifier. |
| Documents disagree | Expose the conflict and route to the owner | Do not generate a compromise policy. |
The boundary we keep explicit
A grounded answer is not automatically a correct answer. Sensitive interpretations, conflicting policies, and missing evidence need review or escalation.
What a useful evaluation should reveal
Evaluate this workload against representative examples and agreed consequences—not just a convincing response. The review should make these dimensions visible:
- Retrieval relevance and coverage
- Answer support and citation accuracy
- Permission isolation
- Response time and cost per query
Where this approach fits
- Policy and procedure guidance for employees
- Technical documentation search for engineering teams
- Research across proposals, reports, and project archives
A considered first step
Choose one knowledge domain, an approved source set, representative users, and a collection of real questions. Compare a retrieval baseline with the existing search experience before expanding the scope.
Serving enterprise teams in California, Atlanta, Georgia, and across the United States.
Discuss your requirementsQuestions worth resolving
Do we need to fine-tune a model first?
Not necessarily. For changing company knowledge, improving ingestion and retrieval is often the first thing to test. Fine-tuning is a separate choice when the task needs more consistent behavior or a specialist response pattern.
Can this work with private documents?
Yes, with an architecture selected around your data boundary. We assess hosting, authentication, source permissions, logging, and retention rather than assuming an external model endpoint is acceptable.