Internal

Enterprise Knowledge Assistants

Staff ask in plain language and get a cited answer from approved internal sources.

What this is

A permission-aware assistant over your policies, procedures, product documentation and tickets. Every answer carries citations to the passage it came from, retrieval respects your existing access rules, and the system abstains rather than guessing when the answer is not there.

The problem

What this solves

If more than one of these is familiar, there is usually a case worth scoping.

Knowledge is spread across drives, wikis, tickets and PDFs and search finds none of it.
New staff ask colleagues questions that are documented somewhere.
Answers come from superseded documents with no way to tell.
Answers without sources have to be verified manually anyway.

How it works

The shape of the build

Indicative. The final design follows your systems, data and constraints.

Retrieval-augmented generation: every answer traces back to an approved source passage.

Ingest
Connectors across file stores, wikis, databases and ticket systems with incremental sync.
Index with permissions
Access metadata attached at indexing time, enforced during retrieval.
Retrieve well
Hybrid vector and keyword search with a reranking pass.
Answer with sources
Grounded answers citing the passage, with explicit abstention when unsupported.
Maintain it
Freshness tracking, re-indexing and a ranked report of unanswered questions.

Scope

What is included

Deliverables you receive, in your own infrastructure and repositories.

Ingestion pipeline across your document and ticket sources
Permission-aware retrieval honouring your existing access rules
Grounded answering with inline citations
Accuracy benchmarks against a question set built with your experts
Admin view for content owners
Re-indexing schedule and gap reporting

Outcomes

What changes once it is live

Operational changes you should expect. We measure the specific numbers for your business rather than quoting generic figures.

Answers traceable to an approved source, every time
Less interruption of experienced staff for documented questions
Contradictions between documents surfaced rather than hidden
A ranked list of knowledge gaps worth writing up

Delivery

How we run the engagement

Our approach

Discovery

Process walkthroughs, data review, constraints and a written scope you sign off before build.

Architecture

Data model, integration boundaries, hosting and the security model, agreed up front.

Proof of concept

A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.

Development

Two-week increments, each ending in a working deployment and a review.

Testing

Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.

Deployment

Staged rollout into your infrastructure with monitoring and a tested rollback path.

Monitoring & support

Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.

Questions