// The Problem //

Why general AI models fail on your data

General-purpose AI models are trained on public data. They have no knowledge of your internal policies, product documentation, contract history, pricing structures, or proprietary knowledge. When you ask them about any of that, they guess — and they guess confidently. That is hallucination, and it is a serious liability in any business context.

How RAG fixes this

Retrieval-Augmented Generation connects the AI model to a retrieval system that searches your actual documents. When a question comes in, the retrieval layer finds the most relevant passages from your knowledge base and surfaces them as context. The model then answers from that context — not from memory, not from guesswork.

The result is answers that are grounded in your documents, with the source material available for verification. You get the language fluency of a large language model without sacrificing accuracy on your specific domain.

Read more about production requirements for RAG systems and why the retrieval quality bar matters before you build.

Why most RAG projects fail

RAG looks straightforward on paper. In practice, getting retrieval quality right is hard. Bad chunking splits concepts across chunk boundaries and loses meaning. Weak embeddings surface passages that match surface words but not intent. Poor retrieval scoring returns documents that are topically related but not actually relevant to the question. A context window that is too small cuts off the answer before it reaches the model.

Each of these failure modes produces the same output: a system that gives confident, fluent, and wrong answers. That is worse than no system at all. We have written about the retrieval quality problem that kills most RAG projects if you want the detail.

The technical decisions we make at each stage of the pipeline are what determine whether your system actually works in production.

// Use Cases //

What we build RAG systems for

RAG is the right architecture when the quality of an AI answer depends on retrieving specific, accurate information from a defined corpus of documents. Here are the four patterns we build most often.

Internal knowledge base Q&A

Staff ask natural-language questions and get accurate answers drawn directly from internal documents, standard operating procedures, HR policies, and institutional knowledge. No more searching through SharePoint or emailing the person who remembers.

Document intelligence

Extract, summarise, and compare information across large document sets — contracts, board reports, financial filings, technical specifications. Ask questions across hundreds of documents and surface the specific clauses or figures that matter.

Product and support Q&A

Customer-facing systems that answer accurately from your product documentation, pricing sheets, and support policies. Eliminates the class of support query that is really a documentation search problem. Answers are grounded and citable.

Regulatory and compliance search

Find relevant regulations, internal policies, or precedents within large regulatory document corpora. Legal, compliance, and risk teams can surface applicable rules and cross-reference them against internal documents — without reading everything manually.

// Technical Depth //

The decisions that determine whether your RAG system works

Most vendors are vague here. We are not. These are the specific technical choices we make at each stage of the pipeline, and why each one matters for answer quality.

Chunking strategy

Fixed-size chunking is simple but breaks concepts across chunk boundaries. Semantic chunking preserves meaning but is computationally heavier. Recursive chunking can give you the best of both. Wrong chunking is the most common failure mode in RAG systems — it determines what information is even retrievable. We choose the strategy based on your document types and the questions your users will ask.

Embedding model selection

General-purpose embedding models work well for general language. Domain-specific models perform better when your corpus uses specialist terminology — legal, medical, technical, financial. Fine-tuned models can outperform both when you have enough labelled examples. We select the embedding model based on your domain and your retrieval quality requirements, not on what is easiest to integrate.

Vector store selection

The vector database you use matters when you are operating at scale or under latency constraints. Pinecone, Weaviate, Qdrant, pgvector, and Chroma each have different characteristics around query speed, hosting model, filtering capabilities, and cost at volume. We select based on your data volume, your hosting constraints, and whether you need managed infrastructure or prefer to own the stack.

Retrieval scoring: BM25, semantic, and hybrid

BM25 is keyword-based retrieval — fast and reliable when the query contains specific terms present in the documents. Semantic retrieval captures meaning even when vocabulary differs. Hybrid retrieval combines both signals. In most production RAG systems, hybrid retrieval outperforms either method alone. We design the retrieval scoring approach based on your query patterns, not on a default configuration.

Re-ranking

The initial retrieval pass returns candidates. A second-pass re-ranker scores those candidates more carefully — typically using a cross-encoder model that evaluates the query and passage together rather than as separate embeddings. Re-ranking improves precision meaningfully when retrieval returns a noisy candidate set. We include it when the precision gain justifies the additional latency.

Context window management

Retrieving relevant passages is necessary but not sufficient. You then have to fit the right passages into the model's context window in a way that enables a coherent, accurate answer. Too many passages and the model loses track. Too few and it lacks the context it needs. We manage context assembly — including passage ordering, deduplication, and token budgeting — as a first-class part of the pipeline.

Answer evaluation

We build a test set before launch and measure retrieval quality and answer accuracy against it. We measure retrieval recall, retrieval precision, answer faithfulness, and answer relevance — separately. Launching a RAG system without an evaluation framework means you do not know whether it is working. We build the framework before deployment, not after a problem surfaces in production.

// Our Process //

How we build RAG systems

Structured from document audit to production deployment. Each phase gates the next — we do not start building until we understand your documents, and we do not deploy until we have measured quality.

Phase 1

Document audit

Week one is spent understanding your document corpus before writing any code. What documents exist, in what formats, at what quality level, with what update frequency. This determines the entire architecture. Low-quality source documents are identified early so you can decide whether to clean them up or exclude them — not after the system is live.

Phase 2

Architecture design

We design the chunking approach, embedding pipeline, vector store selection, and retrieval strategy based on what the document audit revealed and what your users will be asking. The architecture decision document is reviewed before we build anything. Changes at this stage cost days. Changes after build cost weeks.

Phase 3

Build — 4 to 10 weeks

Iterative construction of the ingestion pipeline, vector store, retrieval layer, re-ranking where applicable, context assembly, and the answer generation layer. We build in phases with evaluation checkpoints at each stage, not as a single handover at the end. You see what is working and what is not as we go.

Phase 4

Evaluation framework

Before launch, we construct a test set of representative questions with known correct answers and measure retrieval recall, retrieval precision, answer faithfulness, and answer relevance. This is not a checkbox — it is how we know the system is production-ready. If a metric falls below threshold, we address the failure mode before deployment.

Phase 5

Production deployment and handoff

Deployment to your infrastructure or a managed environment, with full documentation of the system architecture, ingestion pipeline, evaluation framework, and operational runbook. Your team understands how to add documents, monitor quality, and escalate when the system produces a low-confidence answer.

// Deliverables //

What you receive

A production system you can operate, not a prototype you need to rebuild. Everything required to run the RAG pipeline, update your documents, and measure answer quality on an ongoing basis.

Production RAG pipeline

The complete retrieval and answer generation system — chunking, embedding, vector store, retrieval scoring, optional re-ranking, context assembly, and answer generation — deployed and running in production against your documents.

Ingestion pipeline

A pipeline for adding and updating documents without manual intervention. When your documents change, the ingestion pipeline reprocesses, re-embeds, and updates the vector store. You are not locked into the document set that existed at launch.

Evaluation framework

The test set and evaluation tooling we used before launch, handed over so you can re-run quality measurements as your documents evolve. Ongoing quality measurement is how you catch retrieval degradation before your users do.

Documentation and handover

Architecture documentation, operational runbook, and a handover session with whoever will own the system internally. We build systems your team can operate — not systems that require us to be on retainer to function.

// Honest Limitations //

What RAG cannot do

RAG solves a specific problem well. It is not a solution to every AI accuracy problem, and there are conditions under which it will underperform or fail. These are worth knowing before you commission a build.

Document quality is a ceiling, not a floor

If your source documents contain errors, are incomplete, or are poorly structured, RAG surfaces those errors accurately. The system retrieves what is there. Cleaning up source documents before building a RAG system is not optional if answer quality matters.

Cross-document reasoning has limits

Standard RAG retrieves relevant passages and answers from them. If your question types require synthesising information from a large number of documents simultaneously, or reasoning across complex dependencies between documents, standard RAG may not be sufficient. Different architectural patterns — multi-hop retrieval, graph-based retrieval, or agent-based approaches — may be more appropriate. We will tell you if that is the case.

Frequently updated documents require an ingestion pipeline

A one-time document load is only appropriate for static corpora. If your documents change — policies are updated, products are revised, regulations change — you need a running ingestion pipeline to keep the vector store current. We build this as part of the system, but it has operational implications you need to plan for.

RAG is not a substitute for access controls

If different users should see different documents, the access control logic needs to be built at the retrieval layer — not assumed to emerge from the AI. We design retrieval-layer access controls when your use case requires them, but this is a deliberate architectural decision, not a default behaviour.

// Get Started //

Ready to give your AI accurate access to your data?

We start with a document audit, not a sales call. If RAG is the right solution for your use case, we will tell you exactly how we would build it and why. If it is not, we will tell you that too.

Book an AI Audit View all services

RAG System Development