Ingestion that respects structure
Documents are parsed, chunked, versioned, and indexed around how their information is actually organised.
AI systems / Retrieval
Answers grounded in your data.
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RAG systems
Retrieval pipelines that cite their sources, measure quality, and know when evidence is missing.
What we build
Useful from
day one.
PythonPostgrespgvectorOpenAI
Retrieval measured on real questions
Search, reranking, and confidence gates are evaluated against representative queries.
Interfaces people can verify
Answers expose citations and uncertainty instead of hiding the source material.
The route to production
Clear steps.
Visible progress.
Discover
We map the real workflow, risks, users, and success measure before choosing an implementation.
Build
Working software lands in visible increments, with the hardest assumptions tested first.
Prove
We validate against production-shaped data, failure paths, performance, and real use.
Ship
Deployment, monitoring, documentation, and a clean handover are part of the build.
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In their words.
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Before you ask
Questions,
answered.
What is RAG, and when is it the right tool?
Retrieval-augmented generation puts your own documents in front of the model at answer time instead of hoping the model already knows them. It is the right lever when the problem is that the model does not know something — your policies, your contracts, your product data — and when that knowledge changes often enough that baking it in would go stale.
Should we use RAG or fine-tune a model?
Start with RAG. RAG changes what the model knows; fine-tuning changes how it behaves. If the complaint is that the model does not know X, that is a knowledge problem and retrieval is the lever. If it knows enough but answers in the wrong shape or tone, that is behaviour and a fine-tune helps. Fine-tuning does not reliably teach facts or keep them current, which is why teams who fine-tune on their docs watch the model invent details a month later.
How do you stop it making things up?
Four things, in order. Chunk on the document's own structure rather than a character count, so a retrieved passage is a complete thought. Retrieve wide and then rerank, so the right passage is not lost at the cut-off. Gate on confidence, so the system is allowed to refuse. And cite everything, with citations a reader can actually open and check. A system that cannot say no will always answer, correctly or not.
How do you know retrieval is actually working?
By measuring it separately from generation. A golden set of representative questions — including ones whose answer is deliberately absent — is scored for whether the right passage was retrieved at all, before anything is judged on the wording of the answer. Most RAG systems that feel wrong are failing at retrieval, and grading the finished answer hides which half broke.
Our documents are messy — spreadsheets, scans, tables. Does that work?
It changes the ingestion, not the idea. Information inside a table, a chart or a scanned page is invisible to a pipeline that only extracts running text, so those sources need parsing that preserves structure and metadata before anything is indexed. It is usually the largest part of the work on a real corpus, and it is the part that decides whether retrieval ever finds the answer.
Which vector database do you use?
Usually pgvector inside Postgres, because most products already run Postgres and keeping retrieval next to the application data removes a moving part. A dedicated store such as Qdrant is worth it at scale or where the index needs its own operational life. Either way the index parameters get tuned against your corpus, and semantic search is combined with lexical search where exact terms, codes or names matter.
Can the system tell us when it does not know?
That is the point of the confidence gate. When retrieval returns nothing that clears the bar, the right output is an honest refusal with a pointer to where a human can look — not a fluent paragraph assembled from the closest available text. Systems used for anything consequential are far more valuable when their silence is trustworthy.
Do we need an agentic RAG loop?
Only when single-shot retrieval genuinely cannot answer the question — typically multi-hop questions whose answer lives between documents rather than inside one. If you build the loop, route before you loop, give it three well-chosen tools rather than thirty, and treat the stop condition as the whole design. For a corpus where one good retrieval answers the question, the loop adds latency and cost and nothing else.
Will you test it on our real data before we commit?
Yes, and we would rather you insisted. Hand us a slice of the real documents and a handful of genuinely hard questions, including some the answer is not in, and we will show where retrieval holds and where it breaks. A demo built on a cherry-picked corpus tells you nothing about the corpus you actually have.
What happens as our documents change?
Ingestion is versioned and re-indexed so an edited document changes the answer instead of leaving a stale copy in the index, and the evaluation set is re-run so a change that quietly degrades retrieval fails visibly. Without that, a RAG system does not break — it drifts, and you hear about it from a customer.
From the journal
Further reading.
How to build a simple RAG system that holds up on real data8 min readHow to build an agentic RAG system without burning your latency budget8 min readHow to build a RAG system on Excel files without shredding the numbers6 min readHow to build a multimodal RAG system that finds the chart6 min readHow to build a graph RAG system (and when to skip it)8 min readHow to hire a RAG developer who ships more than a demo8 min read
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