Answers from your own documents, with the source attached

Retrieval-augmented generation (RAG) grounds every answer in your own content, with a citation back to the source — instead of the model’s guesswork.

Groundwell is that capability, built for you as a finished service. The tooling market is crowded with frameworks, vector databases, and half-built starter apps.

The hard part is not any single component but wiring them into something accurate, fast, and maintainable on your data. That is the work we take on.

What we build

• Ingestion that respects messy reality. Connectors for your PDFs, wikis, tickets, transcripts, and databases, with parsing that survives tables, forms, and awkward layouts, then chunking tuned to how your content is actually structured — so retrieval works from the whole document, not just the parts a naive pipeline could parse.
• Retrieval that finds the right passage. Embeddings and vector search combined with keyword matching (hybrid search) and metadata filtering, plus reranking so the strongest evidence rises to the top — not just the closest match on paper.
• Grounded chat with receipts. A conversational interface that answers from your corpus and shows the exact passages it drew on, so users can verify rather than take it on faith.
• Evaluation and guardrails. We measure answer quality, groundedness, and latency against a reference set before launch, and put tracing and monitoring in place so quality is watched in production, not assumed.
• An architecture that fits. Whether retrieval belongs beside your operational database or in a dedicated vector store, self-hosted or managed, we choose on your constraints — cost, scale, data residency — not on hype.

How we work

We stay vendor-neutral. There is no single stack we push; we select the models, retrieval strategy, and infrastructure that suit your accuracy targets, budget, and compliance needs, and we hand over something your team can own and extend.

Because features and pricing across the AI landscape shift constantly, we treat each build as a point-in-time decision and design it to be swapped out as the ground moves.

You can engage us to prove the concept on a single high-value use case, or to take a proven prototype all the way to a production system your organisation depends on.

Contact our sales team to scope a RAG application for your data — a short, practical conversation about your content and your accuracy bar, not a hard sales pitch. If RAG isn’t the right fit yet, we’ll tell you that too.