RAG & AI Knowledge Systems
Turn scattered documents, policies, catalogs, support content, videos, and internal records into source-linked answers people can inspect.
What this covers
What is an AI knowledge system?
An AI knowledge system ingests approved business information, retrieves relevant passages for a question, and generates an answer grounded in that evidence. RAG—retrieval-augmented generation—is often part of the architecture, alongside permissions, citations, update pipelines, evaluation, and a usable interface.
Technical Deliverables
- Source and access-model audit
- Ingestion, cleaning, chunking, and update pipeline
- Search, retrieval, reranking, and citation design
- Answer generation with evidence and fallback behavior
- User interface and application/API integration
- Evaluation set, logging, deployment, and documentation
Where it fits
Problems this service can address
- People search the same folders, policies, catalogs, tickets, or videos repeatedly.
- A general model does not know the private, current, or specialized information required for the answer.
- An existing RAG prototype retrieves plausible text but gives no dependable source, permission, or evaluation path.
- Different users should see different knowledge, but access control currently stops at the application login.
Plan before you build
Practical planning resources
Common Questions
Is a knowledge system the same as RAG?
RAG is a common technical pattern inside a knowledge system. The complete product also needs ingestion, permissions, citations, updates, evaluation, fallbacks, interfaces, and operational ownership.
Can it work with PDFs, websites, databases, and videos?
Potentially, yes. Each source needs a suitable extraction and update path. Videos normally require transcripts and metadata; databases may be queried directly or converted into searchable representations depending on the questions.
How do you measure answer quality?
Evaluation separates retrieval from generation. A representative question set checks whether the right evidence was retrieved, whether the answer is supported by that evidence, whether citations are useful, and how the system behaves when evidence is missing.
Can different teams have different access?
Yes, if permissions are designed through the full retrieval path. Application login alone is not enough; document, tenant, team, or record-level constraints may need to filter what can be searched and returned.