RAG systems that answer from your documents, and show you where the answer came from.
Retrieval-augmented generation is a search system with a language model on the end of it. Instead of relying on what a model already knows, it retrieves the relevant passages from your own documents and generates the answer from those, citing where each part came from.
No retrieval system removes the possibility of a wrong answer. The design goal is that a wrong one is traceable to the passage that produced it, and therefore quickly caught.
- Vector Knowledge Store
- Hybrid Semantic Retrieval
- Smart Chunking & Reranking
- Private Hosted Models
Typical timeline: around three weeks to index a full corporate knowledge base.
The answer is in a document nobody can find
Most organisations do not have a knowledge problem. They have a retrieval problem.
The answer exists, but nobody can find it
Procedures, contracts and internal documentation accumulate faster than anyone indexes them. Retrieval makes the corpus searchable by meaning rather than by remembering the filename.
Keyword search misses the paraphrase
Someone asks in their own words and the exact term never appears in the document. Hybrid retrieval matches both the terminology and the meaning.
Experts answering the same question repeatedly
When only two people know how something works, their time becomes the bottleneck. A retrieval system answers the recurring questions from the same source they would quote.
A general model that does not know your business
A model trained on the public internet cannot answer questions about your contracts or your SOPs. Retrieval is what makes the answer specific to your organisation.
No way to check whether an answer is right
Answers carry citations to the passages they were generated from, so a reader can verify rather than trust.
Documents that cannot leave the building
Privately hosted models mean sensitive material can be indexed and queried without its content going to an external provider.
From a document to a cited answer
- 01
The corpus is chosen
Company documents, standard operating procedures, ERP data and contracts. Deciding what belongs in the index — and what must not — is the first real decision.
- 02
Documents are chunked deliberately
Splitting at a fixed size breaks meaning across boundaries. Chunking follows the structure of the document so a retrieved passage is self-contained.
- 03
Chunks are embedded and stored
Passages are converted to vectors and held in a knowledge store alongside the lexical index, so both kinds of matching are available at query time.
- 04
A question triggers hybrid retrieval
Lexical matching catches exact terminology; vector similarity catches paraphrase. Candidates come from both rather than one.
- 05
Candidates are reranked
The first set of matches is re-scored for relevance before anything is generated, which is what keeps a loosely related passage out of the answer.
- 06
The answer is generated and cited
The model writes from the retrieved passages and attaches the citations, so the answer can be traced to its source rather than taken on faith.
What people ask it
Retrieval is worth building where the same questions recur and the authoritative answer already exists somewhere in writing.
Internal SOP and policy search
Staff asking how a process works in their own words, and getting the answer from the current procedure rather than an outdated memory of it.
Contract and clause lookup
Finding what a specific agreement says about a specific situation, with the clause cited rather than paraphrased.
Technical and operational questions
Answering the recurring questions that currently go to the two people who know the system.
Grounding an agent
Giving an AI agent a retrieval tool so its decisions are made from your documented reality rather than its general knowledge.
How we approach the build
Most of the work is not the model. It is deciding what goes in the index, how it is cut up, and who is allowed to retrieve which part of it.
- The corpus and its access rules agreed before indexing starts
- Chunking strategy chosen against the document structure, not a default
- Retrieval evaluated on real questions from the people who will use it
- Private hosting where documents cannot leave your infrastructure
- Around three weeks to index a full corporate knowledge base
Cited answers
Every answer points at the passages it was generated from, so it can be checked.
Private hosting
Where documents cannot leave your estate, the models run inside it.
Frequently asked questions
What is a RAG system?
Retrieval-augmented generation is a search system with a language model on the end of it. Instead of relying on what a model already knows, it retrieves the relevant passages from your own documents and generates the answer from those, citing where each part came from.
What can it index?
Company documents, standard operating procedures, ERP data and contracts. The point is that answers come from your own material rather than from general knowledge, so the system is useful on questions only your organisation can answer.
Does our data leave our infrastructure?
It does not have to. Privately hosted models are part of the stack precisely so that sensitive documents can be indexed and queried without their content being sent to an external provider. Where that constraint applies, it shapes the architecture from the start.
How do you reduce the risk of a wrong answer?
By grounding every answer in retrieved passages and attaching the citations, so an answer can be checked against its source rather than trusted. No retrieval system removes the possibility of a wrong answer entirely — the design goal is that a wrong one is traceable and quickly caught.
How does it find the right passage?
Hybrid retrieval combines lexical matching with vector similarity, so both exact terminology and paraphrased meaning are found. Documents are chunked deliberately rather than split at a fixed size, and candidate passages are reranked before an answer is generated.
How long does indexing take?
Around three weeks to index a full corporate knowledge base. Most of that is not compute — it is deciding what should be indexed, how it should be chunked, and who is allowed to retrieve which parts of it.
Tell us the question your team keeps asking
Twenty minutes, with an engineer rather than a salesperson. If the answer already exists in a document somewhere, we will show you what it takes to make it findable — and say so if your corpus is too thin to be worth indexing yet.