RAG Implementation Services
Ground AI answers in your own knowledge with retrieval-augmented generation. Ingestion, search, reranking and citations, evaluated so the answers are accurate and traceable.
What retrieval-augmented generation is
Retrieval-augmented generation connects a language model to your own knowledge so it answers from your documents rather than its training data. Done right, it is the difference between a plausible answer and a correct, sourced one.
We build the full pipeline: ingestion, chunking, search, reranking and citation, and we evaluate retrieval quality directly, because a RAG system is only as good as what it retrieves.
- Grounded, cited answers from your own knowledge.
- Retrieval quality evaluated directly, not just the final answer.
- Freshness and access control handled from the start.
Why RAG instead of a plain chatbot
A language model on its own answers from what it learned during training. It does not know your contracts, products or internal policies, and it can sound confident when it is wrong. RAG fixes that by retrieving the relevant passages from your own sources first and asking the model to answer only from them.
The result is a system that answers from current company knowledge, cites where each answer comes from and says when the documents do not contain an answer. Updating knowledge means updating the documents, not retraining a model.
Graph RAG and advanced retrieval
Standard RAG works well when the answer sits in one or two passages. Some questions need more: how products, contracts and suppliers relate to each other, or how a requirement in one document affects another. Graph RAG adds a knowledge graph of entities and relationships extracted from your content, so the system can follow those connections.
We use it where relationships matter, for example in contract portfolios, technical documentation with many cross-references or regulatory requirements. For most knowledge bases, good chunking, hybrid search and reranking deliver more value for less effort, so we start there and add a graph only when the evaluation shows it helps.
Access control and citations
A company knowledge assistant must not show people documents they are not allowed to see. We apply document-level permissions from your source systems, such as SharePoint, Confluence or your DMS, at retrieval time, so each user only gets answers from content they can already access.
Every answer comes with citations that link to the source passage. Users can check the answer in seconds, and you can trace any output back to the documents behind it, which also helps with audits and EU AI Act transparency.
Our RAG implementation process
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Ingestion and indexing
Pipelines that ingest your documents and data, extract text from PDFs, tables and scans, and keep the index fresh as content changes.
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Chunking
Splitting content along its structure, such as headings, sections and table rows, so each passage keeps its meaning.
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Hybrid search and reranking
Keyword and vector search combined, then reranked, so the model gets the right context for each question.
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Evaluation
A test set of real questions with known answers, scoring retrieval and answer quality before launch and in production.
Frequently asked questions
What is RAG?
Retrieval-augmented generation. The system finds relevant passages in your documents and gives them to the language model, so answers are based on your content.
How accurate is a RAG system?
Accuracy depends on retrieval quality. We measure it on a test set of real questions before launch and track it in production.
Can users only see documents they have access to?
Yes. Retrieval respects the same permissions as your source systems.
Ground your AI in what your business knows
Tell us the knowledge you want AI to draw on. We will scope a RAG system that answers accurately and cites its sources.