ENTERPRISE KNOWLEDGE ASSISTANT
Enterprise knowledge search and assistance
We build enterprise knowledge assistants that retrieve permitted content and show sources, helping your teams check and use the answers.
- A citation on every material claim, openable in one click
- Abstains on low confidence rather than improvising
- Permission-aware, so users only see what they are entitled to
The single most effective control against hallucination is refusing to answer beyond the evidence.
Our assistants generate strictly from retrieved passages, attach a citation to each material claim, and return "I do not have a confident source for that" rather than improvise.
Users can open the cited passage in one click, so trust is verifiable, not asserted. We measure this with groundedness and faithfulness scores, not vibes, using evaluation harnesses such as RAGAS, TruLens, or Phoenix on a representative question set before launch and on a schedule after.
Hybrid retrieval plus reranking is what separates a demo from a system people trust with real decisions.
RAG is a pipeline, not a prompt.
Each layer is a place where quality is won or lost.
Most knowledge assistants degrade quietly. We design against the known failure modes from day one.
RAG rot
The index goes stale as source content changes, so answers drift out of date. Countermeasure: scheduled re-indexing and freshness monitoring on the corpus.
Poor chunking
Passages split mid-thought, so retrieval returns fragments. Countermeasure: structure-aware chunking tuned to your document types.
Ungrounded answers
The model fills gaps from training data. Countermeasure: strict grounding, abstention on low retrieval confidence, and citation enforcement.
Silent relevance decay
Retrieval quality slips as questions evolve. Countermeasure: continuous groundedness evaluation and a feedback loop from user thumbs-down.
Tuned to your content, permissions and operating context.
Connectors and ingestion
For your document stores, wikis, and systems of record, respecting existing access controls.
A tuned RAG pipeline
Chunking, hybrid retrieval, and reranking configured to your content.
Citation-first generation
Answers with a source on every material claim, and abstention on low confidence.
An evaluation harness
Groundedness and faithfulness baselines, plus dashboards for ongoing monitoring.
Permission-aware retrieval
So users only ever see what they are entitled to.
Five steps, from framing the questions to improving on feedback.
Frame
The questions the assistant must answer well and the sources of truth behind them.
Build
The pipeline, tuning retrieval against a labelled evaluation set.
Ground and gate
Enforce citations and set the abstention threshold to your risk appetite.
Harden
Against RAG rot with re-indexing, freshness checks, and monitoring.
Improve
Continuously, as user feedback becomes evaluation signal.
One of our applied AI solutions.
Applied AI Solutions
The full set of applied, accuracy-bound AI solutions this assistant belongs to.
Scaled GenAI & AI Platforms
The governed platform the assistant runs best on.
Responsible AI & Governance
Grounding and access controls align to your governance requirements.
Healthcare & Life Sciences
Where a cited, permission-aware assistant matters most.
What knowledge and platform leaders ask us first.
It is engineered not to. Answers are generated only from retrieved passages, each material claim is cited, and the assistant abstains when retrieval confidence is low rather than guessing.
No. Content is retrieved at query time within your governance and residency requirements; it is not used to train third-party models.
Scheduled re-indexing and corpus freshness monitoring prevent RAG rot, so the assistant reflects your latest content rather than a stale snapshot.
Tell us the questions costing your experts the most time. We will show what a grounded, cited assistant does with them.
Every answer grounded in your own content, cited to its source, and honest about what it does not know.