02.1
Sources and permissions
Select authoritative sources, model identity and access, and preserve source-level permissions throughout retrieval.
Solution · AI knowledge systems
Build grounded answers that show where the information came from.
Connect approved knowledge sources, access rules, retrieval, citations, evaluation, and feedback into an internal search or assistance workflow.
DIRECT ANSWER / 01
An AI knowledge system is appropriate when teams repeatedly search across trusted internal sources and need faster synthesis without losing permissions, provenance, or a route back to the source material.
When to use it
Signals that the work is ready for review.
- S1People ask the same questions across scattered documents and tools.
- S2Existing search finds keywords but not the answer context.
- S3A RAG prototype gives plausible answers without dependable citations.
- S4Different users must see different source material.
Architecture
Retrieval is only one layer of answer reliability.
02.2
Retrieval and context
Choose search, chunking, metadata, long context, and ranking based on real questions and source structure.
02.3
Answer and evidence
Require traceable citations, define refusal behavior, and let users inspect the material behind a response.
Evaluation
Test retrieval, answer quality, and failure separately.
03.1
Question set
Build representative questions including ambiguous, outdated, permission-sensitive, and unanswerable cases.
03.2
Failure analysis
Distinguish missing source content, retrieval misses, context loss, unsupported generation, and stale data.
03.3
Operational feedback
Capture useful, incorrect, and unresolved outcomes without sending private question text to aggregate analytics.
Defined boundary
What the engagement produces.
- Source and permission inventory
- Representative evaluation set
- Feasibility prototype
- Citation and refusal behavior
- Production and maintenance recommendation
Not included by default
What the service does not imply.
- A chatbot over every company document
- Permission flattening
- Fine-tuning before the information problem is understood
- Claims that citations eliminate hallucination
Method evidence
Evidence is intentionally limited at launch.
Pure Insight publishes the decision and evaluation method here rather than implying a client outcome that has not been approved for public use.
Inspect the evidenceBuyer questions
Before a fit review.
Is RAG always the right approach?
No. Conventional search, structured queries, long context, curated answers, tools, or a combination may be simpler and more reliable. The source and question distribution should decide.
Can permissions from our current tools be preserved?
Often, but only if source identities and access controls can be mapped into retrieval and caching. Permission behavior is an architecture requirement, not a later UI feature.
Should we fine-tune a model?
Usually not for frequently changing factual knowledge. Fine-tuning can support behavior or format, but it does not replace authoritative retrieval and source governance.
What does it cost and how long does it take?
Scope, delivery sequence, and commercial terms are documented after the fit review. Pure Insight does not estimate a system from a form alone; the first engagement is shaped around the smallest step that can reduce material uncertainty.
Technical fit review
Make the first step reduce uncertainty.
Share the system, workflow, or delivery risk you need to resolve. The first review focuses on fit and a practical next step.
Assess a Knowledge Workflow