Give your team answers grounded in the sources they are allowed to use.
Build AI knowledge bases and RAG applications with approved sources, access-aware retrieval, cited answers and evaluation for business teams.
Our approach to AI Knowledge Base & RAG Development
An AI knowledge base combines organized source material with a retrieval experience. We focus on source quality, permissions and useful answers so people can inspect the information behind a response.
Projects can support an internal search tool, a customer support assistant or a product copilot. We agree how sources are ingested, updated and removed before relying on them in production.
When to choose this service
- Internal documentation and policy search
- Support assistants using approved help content
- Product copilots grounded in customer documentation
Project success criteria
We agree measurable goals and acceptance checks during discovery. Outcomes depend on the scope, starting point and how the solution is used.
Delivery
Agreed acceptance criteria
How we review this
Measurement
Baseline and project goals
How we review this
Handoff
Documented ownership
How we review this
What You Get
- Source inventory, ownership and permission modelAcceptance checkReview “Source inventory, ownership and permission model” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Document ingestion and update workflowAcceptance checkReview “Document ingestion and update workflow” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Search or retrieval pipeline and source referencesAcceptance checkReview “Search or retrieval pipeline and source references” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Interface or application integrationAcceptance checkReview “Interface or application integration” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Retrieval evaluation and unsupported-answer behaviorAcceptance checkReview “Retrieval evaluation and unsupported-answer behavior” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Maintenance, deletion and access-review documentationAcceptance checkReview “Maintenance, deletion and access-review documentation” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
How We Execute
- 1Review source quality, intended users and access rulesReview this stepFor “Review source quality, intended users and access rules”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 2Design ingestion, indexing and update behaviorReview this stepFor “Design ingestion, indexing and update behavior”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 3Build retrieval and an interface with inspectable sourcesReview this stepFor “Build retrieval and an interface with inspectable sources”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 4Evaluate representative questions and hand over maintenanceReview this stepFor “Evaluate representative questions and hand over maintenance”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
Next Step
Discuss your project with BrainGlow
Share your goals and current setup. We will give you a clear scope, timeline, and success criteria before build starts.
AI Knowledge Base & RAG Development: common questions
Will everyone see all documents?
Access should follow the agreed permission model. We scope source-level and user-level restrictions and test whether retrieval respects those boundaries.
How does new content reach the knowledge base?
We define an update process suited to the sources, such as scheduled synchronization or a publishing trigger. Deletion and access changes are included in the maintenance plan.
Does retrieval eliminate incorrect AI answers?
No. Retrieval can provide relevant context, but answers still need evaluation and appropriate limits. We include source references and test questions that the material cannot answer.
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Your next step
Ready to plan your project with BrainGlow?
Get a concrete execution plan and timeline matched to your growth goals.
Choose a workflow with a clear owner and a recoverable failure path
Bring a process example, the tools involved, and the point where work stalls. Start with one trigger and one expected result. Identify which decisions can follow fixed rules, which need interpretation, and which require a person to approve the next action.
- Document access requirements and the system that owns each field.
- Test duplicate inputs, invalid data, and unavailable integrations.
- Agree on a manual fallback, monitoring owner, and a baseline before expanding.
Review related delivered projects to assess fit. Timeline and outcomes depend on scope, data quality, and third-party access; define acceptance criteria during discovery.