Services

Retrieval-augmented generation that earns trust.

We build retrieval-augmented generation systems that ground language models in your own documents — accurate, cited answers, from embeddings to evaluation.

RAG is what you reach for when the answer lives in your own documents, not in a model's training data. Done well it gives grounded, citable answers; done badly it confidently quotes the wrong paragraph. The difference is engineering — how you chunk, embed, retrieve, re-rank and evaluate — and that is the part we obsess over.

Where we help

Document ingestion and chunking tuned to your content, not a default setting
Embeddings and vector search over pgvector, or a dedicated vector store
Hybrid keyword-plus-vector retrieval with re-ranking for precision
Answer generation with inline citations back to the source passage
Permission-aware retrieval so users only ever see what they may see
Freshness and re-indexing pipelines as your source documents change
Retrieval evaluation — measuring recall and answer faithfulness, not vibes
On-premise and local-model RAG where confidentiality is non-negotiable

Example builds

Private document Q&A. Our privacy-focused CRM pairs local language models with retrieval over an organisation's own records, so staff can ask questions in plain language while the data stays entirely on-premises.

Retrieval over a news firehose. Our automated media monitoring system retrieves and summarises the relevant items from a continuous content stream, then routes and alerts on what each subscriber cares about.

Watchlist-scoped retrieval. StockTrack narrows a flood of announcements and articles down to each user's holdings before summarising — retrieval as the filter that makes the output worth reading.

How we engage

We start by being honest about whether you even need RAG — sometimes a smaller corpus belongs in the prompt, or the real problem is fine-tuning. When retrieval is the right answer, a two-week sprint builds a thin slice end to end and measures retrieval quality on your actual documents. Only then do we scale the corpus and harden the pipeline. See our services for engagement shapes.

Common questions

What vector database do you use?: Often pgvector, because keeping embeddings beside your relational data in PostgreSQL is simpler to operate and reason about. For very large or specialised corpora we will reach for a dedicated vector store, chosen on the numbers.
How do you keep answers grounded and citable?: Generated answers carry citations back to the source passages, so a reader can verify them. We measure answer faithfulness against the retrieved context as part of evaluation, rather than trusting the output blind.
Can retrieval respect our access controls?: Yes. We make retrieval permission-aware so the model can only surface documents a given user is allowed to see — essential when one index spans content with different sensitivity levels.

AI & machine learning LLM applications Document AI & OCR

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Where we help

Document ingestion and chunking tuned to your content, not a default setting
Embeddings and vector search over pgvector, or a dedicated vector store
Hybrid keyword-plus-vector retrieval with re-ranking for precision
Answer generation with inline citations back to the source passage
Permission-aware retrieval so users only ever see what they may see
Freshness and re-indexing pipelines as your source documents change
Retrieval evaluation — measuring recall and answer faithfulness, not vibes
On-premise and local-model RAG where confidentiality is non-negotiable

Example builds

How we engage

Common questions

What vector database do you use?

Often pgvector, because keeping embeddings beside your relational data in PostgreSQL is simpler to operate and reason about. For very large or specialised corpora we will reach for a dedicated vector store, chosen on the numbers.

How do you keep answers grounded and citable?

Generated answers carry citations back to the source passages, so a reader can verify them. We measure answer faithfulness against the retrieved context as part of evaluation, rather than trusting the output blind.

Can retrieval respect our access controls?

Yes. We make retrieval permission-aware so the model can only surface documents a given user is allowed to see — essential when one index spans content with different sensitivity levels.