# AI Knowledge Base & RAG Development

Source: https://brainglow.io/services/development-services/knowledge-bases

Build AI knowledge bases and RAG applications with approved sources, access-aware retrieval, cited answers and evaluation for business teams.

## Overview

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.

## Use cases

- Internal documentation and policy search
- Support assistants using approved help content
- Product copilots grounded in customer documentation

## Who this is for

- Internal documentation and policy search
- Support assistants using approved help content
- Product copilots grounded in customer documentation

## Deliverables

- Source inventory, ownership and permission model
- Document ingestion and update workflow
- Search or retrieval pipeline and source references
- Interface or application integration
- Retrieval evaluation and unsupported-answer behavior
- Maintenance, deletion and access-review documentation

## Process

- Review source quality, intended users and access rules
- Design ingestion, indexing and update behavior
- Build retrieval and an interface with inspectable sources
- Evaluate representative questions and hand over maintenance

## Scope and commercial model

Estimated after discovery

Implementation and content ingestion scope; ongoing indexing optional

## Acceptance criteria

- Delivery: Agreed acceptance criteria
- Measurement: Baseline and project goals
- Handoff: Documented ownership

Acceptance and measurement are agreed during discovery; outcomes are not guaranteed.

## Questions and answers

### 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.

## Related services

- [chatbots](https://brainglow.io/services/development-services/chatbots)
- [ai applications](https://brainglow.io/services/development-services/ai-applications)
- [api integrations](https://brainglow.io/services/development-services/api-integrations)

## Enquire

[Discuss your project](https://brainglow.io/contact). Scope and commercial terms require human confirmation. This reference does not authorize a submission or purchase.