AI · AI for Business Automation
AI Knowledge Base
An AI knowledge base turns scattered documents, wikis, and tickets into answers your teams and agents trust. We build it the compounding way: instead of only chunking files for vector search, an LLM compiles your sources into a maintained, interlinked library that improves as it is used. You leave with grounded, cited answers, access controls, and a pipeline that keeps it current. Senior engineers own the build.
In short
What is AI Knowledge Base?
An AI knowledge base is a system that answers questions from an organisation's documents, wikis, and tickets with cited, access-controlled responses. Metaborong builds the compounding version: an LLM compiles sources into a maintained, interlinked knowledge library, not just chunked vector search, so answers stay accurate as sources change. Senior engineers own the build, delivered from India with global reach.
What we deliver
Concrete artefacts, not capabilities
- 01
Deployed knowledge base answering from your documents, wikis, and tickets
- 02
LLM-compiled, interlinked knowledge library that compounds over time
- 03
Role-based access control and per-source permission boundaries
- 04
Answer-quality evaluation harness with citation and freshness checks
- 05
Update and versioning pipeline that keeps the library current
Key concepts
Key terms, defined
- AI knowledge base
- An AI knowledge base is a system that answers natural-language questions from an organisation's internal content, returning cited responses scoped to the asker's permissions, rather than returning a list of documents for the person to read and search through themselves.
- Retrieval-augmented generation
- Retrieval-augmented generation, or RAG, is a pattern where relevant source passages are fetched at query time and supplied to a language model, so answers reflect proprietary data instead of the model's training memory.
- LLM knowledge library
- An LLM knowledge library is a maintained set of model-compiled, interlinked pages distilled from raw sources. Knowledge compounds across sessions instead of being rediscovered per query, an alternative to chunk-only retrieval that improves answers on complex questions.
- Access control
- Access control in a knowledge base enforces, at retrieval time, which sources and answers each user may see, so the system never surfaces content a person is not cleared to access, even inside a generated summary.
- Answer evaluation
- Answer evaluation scores a knowledge base on accuracy, citation correctness, and freshness against a labelled question set, run continuously so quality regressions are caught in CI before users encounter a wrong or stale answer.
How we work
Engagement phases
Source mapping and ingest
We map every knowledge source: documents, wikis, databases, help centres, ticket history, and the APIs behind them. Ingestion captures content with its permissions and provenance intact. We decide per source whether it is indexed for retrieval, compiled into the knowledge library, or both, before any answering is wired up.
Compile and ground
An LLM compiles raw sources into structured, interlinked knowledge pages, so the system reasons over distilled knowledge rather than rediscovering it per query. Vector retrieval grounds answers where freshness matters. Together they produce cited answers that stay accurate as the underlying sources change over time.
Access and accuracy
Role-based access control and per-source permissions are enforced at retrieval time, so users only see what they are cleared for. An evaluation harness scores answer accuracy, citation correctness, and freshness. Low-confidence answers defer to a human or a source link rather than guessing at a response.
Maintenance and handover
A scheduled pipeline recompiles changed sources and versions the knowledge library, so updates flow through without a rebuild. Usage analytics surface gaps and stale answers. We hand over with a runbook so your team owns ingestion, permissions, and the evaluation set without re-engineering the system.
Tech stack
What we build on
- OpenAIModels
- AnthropicModels
- pgvectorVector store
- PostgreSQLStore
- MarkdownKnowledge library
- LangChainOrchestration
- RedisCaching
- SentryObservability
- OpenAIModels
- AnthropicModels
- pgvectorVector store
- PostgreSQLStore
- MarkdownKnowledge library
- LangChainOrchestration
- RedisCaching
- SentryObservability
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| Your teams or customers waste time hunting for answers across scattered sources. | You only need a single document searched once - a knowledge base is overkill. |
| You have documents, wikis, and tickets that change and must stay accurate. | Your content is fully public and a standard search box already serves it. |
| You need access controls so answers respect who is allowed to see what. | You expect answers with no source grounding - we build cited, verifiable systems. |
Related services
Adjacent engagements
- AI
RAG & Retrieval Pipelines
Retrieval pipelines that ground LLMs in your data: embeddings, vector stores, reranking, evaluations.
- AI
Generative AI Development
GenAI built into your product: content generation, enrichment, and backend integration.
- AI
AI Copilots & Internal Tools
Custom copilots for support, sales, and ops teams, grounded in your data and wired into your stack.
Frequently asked questions
An AI knowledge base answers questions from your organisation's documents, wikis, and tickets, returning cited answers scoped to each user's permissions. Instead of returning a list of files to read, it gives a direct, grounded answer with links to the source, so teams and customers find accurate information in seconds rather than searching.
Standard RAG chunks your files and retrieves passages per query, rediscovering knowledge every time. We add a compounding layer: an LLM compiles sources into a maintained, interlinked knowledge library, so the system reasons over distilled knowledge. Retrieval still grounds time-sensitive facts. The result is more accurate on complex, cross-document questions.
A scheduled pipeline recompiles changed sources and versions the knowledge library, so updates flow through without a rebuild, and an evaluation harness scores freshness and citation correctness continuously. Where a source is stale or confidence is low, the system links to the source or defers to a human rather than answering with outdated information.
Yes. Role-based access control and per-source permissions are enforced at retrieval time, not bolted on after. A user only receives answers built from sources they are cleared to access, and the system never leaks restricted content through a generated summary. Permission boundaries are an architecture decision, scoped at ingest.
Last reviewed · Reviewed by Metaborong engineering team
Tell us what you are building.
We build what large agencies under-deliver and freelancers can't architect, across Web3 protocols, AI agents, and SaaS products. Tell us what you are building. We will tell you how we would approach it, no pitch deck, no fluff, no commitment required.