An LLM that 'remembers' will eventually lie to you. RAG makes it look things up first — retrieving the right passages from your knowledge and answering only from them, with citations. Current, private, and verifiable.
Retrieval-augmented generation
Retriever · your knowledge base
“Enterprise refunds are within 30 days.”
cited · policy.pdf p.4
hrs per report, grounded by RAG
every claim traces to a source
semantic + keyword retrieval
re-indexed as docs change
What a real RAG system needs
A vector database is 10% of it. These four pieces are what make RAG accurate enough to depend on.
Per-source pipelines for PDFs, wikis, tickets and databases — parsed, cleaned and chunked so retrieval actually works.
Semantic vector search plus keyword and re-ranking, so the right passage surfaces even when the question is worded differently.
Answers constrained to retrieved context, every claim cited, with an honest 'not found' instead of a confident invention.
Grounding and citation-coverage metrics, plus index re-sync so answers stay current as your documents change.
How we build RAG
Each step exists to protect one thing: that the answer is grounded in your truth and you can prove where it came from.
Connect and ingest your document sources, then chunk and embed them into a retrieval store built for your content.
Hybrid search and re-ranking tuned on real questions, so the passages the model sees are the right ones.
Constrain generation to retrieved context and attach citations, so every answer is checkable back to a source.
Measure grounding and accuracy, set up re-indexing, and monitor so quality holds as the corpus grows.
RAG, in production
A consultancy spent 80 hours hand-building each client report. We used retrieval-grounded generation to draft narrative tied to the actual figures — every number traceable to its source — so analysts edit instead of assemble.
Northbridge Advisory
Internal product / tooling team at a consultancy · EU
Per report (was 80)
Throughput
On-brand, grounded output
Headcount, more clients
“The report used to be where our best thinking went to die under formatting. Now the tool hands an analyst a grounded draft and they spend the day on the insight. We took on more clients without taking on more people.”
Straight answers
Retrieval-augmented generation. Instead of trusting the model to remember, the system first retrieves the relevant passages from your documents, then asks the model to answer using only those passages — and cite them. It's how you get current, private, verifiable answers instead of confident guesses.
Fine-tuning bakes knowledge in at training time, so it goes stale and can't cite sources. RAG keeps knowledge in a live index you can update instantly, and every answer points back to the document it came from. For knowledge that changes or needs to be auditable, RAG wins; often we combine both.
Answers are constrained to the retrieved passages, every claim carries a citation, and the system is built to say 'not found in the documents' rather than fill the gap. We evaluate grounding and citation coverage as first-class metrics.
Yes — handling the mess is most of the engineering. We build ingestion and chunking per source type, normalise it, and use hybrid retrieval (semantic + keyword) so the right passage surfaces even when the wording differs.
The index re-syncs on a schedule or on change events, so new and updated documents are searchable quickly. No retraining, no stale answers — update the source and the system reflects it.
Point us at your documents and the questions you need answered. We'll design a RAG approach and prove the grounding on a real sample.
2000+ vetted engineers · 3 global hubs · 98% client retention
for project discussion
Once you fill out this form, our sales representatives will contact you within 24 hours.
We guarantee to get back to you within a business day.