Outcomes
- Provenance metadata
- Trust labels
- Retrieval design
- Review workflow
- Route index
Service package
Governed RAG Foundation for Trusted Internal Knowledge Systems
An eight-to-ten-week foundation for internal knowledge where every answer should be traceable to approved sources, content states, ownership, and retrieval evaluation.
Source-governed retrieval
Starting from $75k.
Buyer fit: Knowledge, support, compliance, product, or engineering teams with scattered internal context.
Timeline: Typical duration: 8–10 weeks.
Scope boundary: Not a promise that AI answers are always correct; retrieval output remains source-bound and reviewable.
Sample artifact: Governed knowledge pilot with approved/draft/quarantined content states.
Deliverables
- source policy
- metadata schema
- retrieval architecture
- evaluation checklist
- review handbook
Sample artifact template
Governed Knowledge Source Policy
A source-governed retrieval artifact defining provenance, trust labels, review states, and retrieval boundaries before RAG build-out.
Source policy
- authoritative
- draft
- deprecated
- quarantined
Metadata model
- owner
- review date
- sensitivity
- domain
Retrieval boundary
- citation required
- unknown allowed
- confidence not hidden
- answer review route
Questions answered
What this package resolves before implementation
Use the one-pager and sample artifact to decide whether this scope fits your current risk.
Which sources are authoritative?
What should never be retrieved?
How does a draft become trusted?
How are answers traced back to sources?
Service detail
Who this package is for, what it covers, and how acceptance is reviewed
The page separates buyer fit, technical scope, integration, governance, client responsibilities, and proof so a technical evaluator can assess the package without relying on generic claims.
Who this is for
- Teams with fragmented policies, SOPs, technical notes, tickets, or support knowledge.
- Organizations where stale or conflicting content creates operational risk.
- Buyers who need more governance than generic vector search provides.
Who this is not for
- A promise that retrieval eliminates hallucination.
- Bulk ingestion without source ownership or review states.
- A public chatbot over confidential material without access controls.
Systems and workflows in scope
- Policies and SOPs
- Engineering documentation and code notes
- Support knowledge and ticket patterns
- Product or operations guidance
- Compliance and controlled internal reference material
Problems this package answers
- Which sources are authoritative?
- How are drafts, stale content, and conflicting versions handled?
- What answer requires citation or refusal?
- Who owns review and approval?
- How is retrieval quality measured?
Technical approach
Implementation depth without unsupported guarantees
The exact architecture depends on the system, evidence, access, and risk. These sections show the normal design surface and the boundaries buyers should expect to review.
Technical design
- Source inventory and content-ownership model
- Ingestion and normalization rules
- Metadata, provenance, trust-label, and lifecycle schema
- Index and retrieval architecture
- Citation, refusal, and escalation rules
- Evaluation set and review workflow
Integration and data handling
- Identity and access filtering can be applied before retrieval.
- Content connectors are scoped to approved source systems.
- Answer interfaces expose supporting sources and uncertainty rather than hiding retrieval context.
Security, review, and governance
- Source-level access control
- Sensitive-content classification
- Approved, draft, stale, quarantined, and retired states
- Citation and refusal requirements
- Audit trail for content and retrieval changes
Timeline and responsibilities
What the client provides and what acceptance means
The published timeline assumes timely access to the agreed evidence, system owners, reviewers, and decision makers. Delays in access, source ownership, regulated-data handling, or review can change delivery sequence without changing the public price floor.
Client inputs
- Source owners and representative repositories
- Current content lifecycle and approval process
- Access-control expectations
- High-value questions and failure examples
- Stale-content and conflict examples
Acceptance criteria
- Approved source and metadata model
- Working ingestion/retrieval slice or implementation-ready design
- Visible provenance and answer citations
- Evaluation questions and expected evidence
- Operational review and stale-content process
Example artifacts
- Source inventory
- Content-state model
- Metadata and trust-label schema
- Governed RAG architecture
- Evaluation checklist
- Reviewer handbook
Package FAQ
Questions to resolve before the engagement begins
How is governed RAG different from vector search?
It adds source ownership, access rules, content states, provenance, citation, refusal, review, and evaluation around retrieval.
Does RAG guarantee correct answers?
No. Retrieval can improve source grounding, but output still needs evaluation, citation visibility, and escalation for unsupported questions.
Can we start with a subset of sources?
Yes. A narrow, high-value corpus is usually easier to govern and evaluate than broad ingestion.
Next step
Confirm fit before sharing private system details.
Use the fit call for an early conversation or request assessment scope when the buyer, system, and decision are already clear.
Next step
Start with a short fit call, then scope the assessment.
The first conversation should decide whether the next step is a fixed-scope assessment, modernization blueprint, governed AI pilot, or reliability review.
Book a 20-minute fit call