RAG development and knowledge systems

We build systems that answer from your data, with citations and permissions, so users can trust the answer and see where it came from. Retrieval quality is treated as an engineering problem, not a prompt.

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Documents and data are ingested, chunked, and indexed; questions are answered through graph and vector retrieval with permissions enforced, producing answers with citations.

What RAG development and knowledge systems involves

Retrieval-augmented generation (RAG) lets a language model answer from information it was never trained on: your documentation, policies, contracts, tickets, product data, or research. Instead of relying on what the model remembers, the system retrieves the relevant passages at question time and asks the model to answer from them. Done well, the result is an assistant that knows your domain and can show its sources.

Done poorly, it is a confident system that is wrong in ways that are hard to spot. Most RAG failures are retrieval failures: the right passage was never found, was split in the wrong place, or was outranked by a near miss. That is why we treat retrieval as an engineering discipline with its own measurements, separate from the quality of the final answer. When an answer is wrong, we want to know whether retrieval or generation was at fault, and fix the right one.

Production RAG also has to respect who is asking. Permissions are enforced at retrieval, so answers are built from the documents a user is allowed to open, and every answer carries citations so users can check it. We describe how we measure this in Evaluating grounded answers in RAG systems.

Problems we solve

Answers need to come from private or domain data, not the model's general knowledge.
Users cannot trust an answer they cannot trace back to a source.
Different users should only see what they are permitted to see.

What's included

Ingestion pipelines
Chunking and indexing
Retrieval design
Graph and vector retrieval
Permissions
Citations
Evaluation and observability

How we approach it

01
Ingest
Build reliable pipelines for the sources that matter.
02
Retrieve
Design chunking, indexing, and graph or vector retrieval for the domain.
03
Ground
Enforce permissions and attach citations to every claim.
04
Evaluate
Measure grounded-answer quality and watch it in production.

Decisions that matter

Chunking follows the content

Contracts, support tickets, and API references have different structures, so one splitting rule does not fit them all. We chunk along the content's own structure, keep the metadata retrieval needs, and test those choices against real questions.

Hybrid retrieval where it pays

Vector search captures meaning but can miss exact terms such as product codes and names; keyword search does the opposite. Many domains need both, plus re-ranking, and some need a graph to follow relationships between entities. We choose based on the questions users actually ask.

Ingestion is a pipeline, not a script

Sources change. We build ingestion that tracks what was indexed, re-processes what changed, and removes what was deleted, so the knowledge base stays in step with the source of truth.

When it is the right fit

A good fit when

Users need answers from private or specialised content: documentation, policies, contracts, research, or product data.
Answers must be traceable to a source, for trust, compliance, or support quality.
Different users may see different content, and the system must respect that.

Probably not when

The questions are answered by structured data in a database. A well-designed query or report is usually more reliable than retrieval over text.
The content is small and stable enough to fit in a single prompt. A full retrieval pipeline would add complexity without benefit.

Representative technologies

Vector databasesGraph databasesLlamaIndexEmbeddingsHybrid retrievalEvaluation tooling

RAG and Knowledge Systems FAQs

How do you stop the system from making things up?

We ground answers in retrieved sources, attach citations, and evaluate whether responses are actually supported by those sources.

Can it respect our access controls?

Yes. Permissions are enforced at retrieval, so answers are drawn from the content each user is allowed to access.

Do we need a graph database or is vector search enough?

It depends on the data and questions. We use vector, graph, or hybrid retrieval based on what the domain actually needs.

Which content sources can you ingest?

Documents and PDFs, wikis, help centres, ticketing systems, databases, and internal tools with an API. Each source gets a connector that keeps it in sync and carries its permissions into the index.

How do you keep the knowledge base up to date?

Ingestion runs as a pipeline that detects new, changed, and deleted content and re-indexes only what changed. Freshness is monitored, so a stale source is noticed rather than silently answered from.

Proof

Industry