What We Build

Retrieval-Augmented Generation (RAG) Systems for Enterprises

Retrieval-augmented generation (RAG) is a technique that grounds a large language model in a specific body of source documents: before answering, the system retrieves the passages most relevant to the question and requires the model to answer from those passages alone, citing them.

The failure mode that kills most enterprise AI pilots is not that the model is not clever enough. It is that the model is confidently wrong about the organisation's own facts — quoting a superseded policy, inventing a clause, citing a contract that does not exist. A general-purpose model has never read your contracts, your standard operating procedures, or last quarter's board pack, so when asked about them it produces something plausible instead of something true.

RAG closes that gap. Rather than relying on what the model absorbed during training, the system searches your corpus at question time, hands the model the specific passages that bear on the question, and constrains it to answer from those passages with a citation attached to every claim. The answer becomes checkable: a reviewer can click through to the source paragraph and confirm it in seconds.

That checkability is the whole point in a regulated setting. An answer nobody can verify is not usable evidence in a compliance review, a credit file, or a regulatory submission — no matter how good it sounds.

What we build

A production RAG system is considerably more than a vector database and an API call. The retrieval layer determines the ceiling on answer quality, and most of the engineering effort goes there rather than into prompting.

Source-agnostic ingestion across PDFs, scanned documents, databases, SharePoint, wikis, ticketing systems and internal APIs
Structured chunking that preserves document hierarchy, tables and metadata rather than splitting on a fixed character count
Hybrid retrieval combining dense vector search with keyword and metadata filtering, so exact identifiers and part numbers are found alongside semantic matches
Re-ranking and query rewriting to lift the genuinely relevant passages above the merely similar ones
Citation enforcement, so every sentence in an answer resolves to a specific source passage a human can open
Permission-aware retrieval that respects the access controls already in your source systems — users only ever see answers built from documents they could have opened themselves

How we keep it accurate

Accuracy is measured, not asserted. Before a system reaches users we build an evaluation set from real questions and human-verified answers, and every change to chunking, retrieval or prompting is scored against it. That set becomes a regression suite: it tells you whether last week's tuning actually helped, and it catches silent degradation after go-live when the corpus grows.

We also instrument what happens in production — which questions retrieved nothing useful, which answers users rejected, which documents are never retrieved because they were ingested badly. Those signals drive the optimisation phase, and they are the reason a well-built system improves after launch rather than decaying.

Where it runs

RAG systems can be deployed against a frontier model API, against an open-weight model hosted inside your own infrastructure, or as a hybrid where sensitive corpora stay in-house and general queries go to a commercial endpoint. The right answer depends on your data classification, your latency requirements and your cost profile, and it is a decision we make with you during the architecture phase rather than assuming up front.

For organisations that cannot send document contents to a third party at all, this pairs directly with private LLM deployment — the retrieval layer and the model both sit inside your boundary.

02Where It Applies

Typical applications of Retrieval-Augmented Generation.

Policy and standard-operating-procedure assistants that answer with the current version and cite the clause

Contract and obligation search across a full agreement portfolio

Regulatory change detection — surfacing which internal documents a new rule affects

Engineering and maintenance knowledge bases built from manuals, incident reports and field notes

Internal support deflection for finance, HR and IT queries that currently reach a human

Natural-language querying of ERP, financial and operational data

03Questions

Retrieval-Augmented Generation, answered.

The questions that come up on almost every first call about this capability.

How is RAG different from fine-tuning a model?

Fine-tuning adjusts a model's weights to change its behaviour, style or format. RAG changes what the model knows at the moment it answers, by retrieving source documents and requiring the answer to come from them. For enterprise knowledge the distinction matters: documents change weekly, and retraining a model every time a policy is updated is neither practical nor auditable. RAG picks up a change the moment the document is re-indexed. The two are complementary — a fine-tuned model can be the generator inside a RAG system.

Does RAG actually eliminate hallucination?

It changes the problem from an unbounded one to a bounded one. A model answering from memory can invent anything; a model constrained to cited passages can only misread what it was given, and a reviewer can check it in seconds by opening the citation. Combined with retrieval evaluation, answer-grounding checks and an explicit refusal path when nothing relevant is retrieved, the residual error rate becomes measurable and manageable rather than unknown. Any vendor promising literally zero errors without an evaluation harness behind the claim is selling something.

What does our data have to look like before we start?

Messier than you think is usually fine. Scanned PDFs, inconsistent templates, documents scattered across several systems, and no metadata is the normal starting condition rather than a blocker. What genuinely matters is that the documents are the authoritative ones and that someone can tell us which version supersedes which. The data audit in the discovery phase exists to establish exactly that, and it occasionally concludes that a corpus needs cleaning before it is worth indexing.

Can it work across systems that do not talk to each other today?

Yes, and this is often where most of the value sits. Retrieval does not require you to migrate or consolidate anything — the ingestion layer reads from each system where it already lives and unifies the answers rather than the storage. A single question can draw on a contract in one repository, an email thread in another, and a table in the ERP.

How long before it is in front of users?

Discovery and architecture typically run a few weeks, and a scoped first deployment against a single well-defined corpus follows from there. We deliberately keep the first release narrow — one department, one document set, one question type — because a narrow system that is right is far more useful than a broad one that is occasionally wrong, and it produces the evaluation data that makes the second release better.

04Sectors