RAG Pipeline Development
Turn your existing documents, knowledge base, and internal data into an AI-searchable resource. Staff ask questions in plain English and get accurate answers drawn from your own documents, with source citations.
Quick Verdict
RAG works best when you have hundreds or thousands of documents where manual search is impractical — policy docs, past proposals, training materials, client files. Staff stop Slacking “does anyone know...” and start querying the knowledge base directly. Most businesses estimate 30–60 minutes saved per knowledge worker per day. If you have fewer than 50 documents, a simpler approach (just including them as context in an AI prompt) is cheaper and works fine.
What a RAG Pipeline Actually Does
Document Ingestion. Documents are collected from wherever they live — SharePoint, Google Drive, Confluence, Notion, local file servers, email archives, databases. Multiple formats: PDFs, Word documents, PowerPoints, spreadsheets, HTML pages, plain text, scanned documents with OCR. Each document is processed, cleaned, and split into semantic sections that preserve context.
Embedding and Indexing. Each chunk is converted into a mathematical representation (an “embedding”) that captures its meaning, stored in a vector database. When someone asks a question, the database finds document chunks with the most similar meaning — “What’s our policy on remote work?” matches the flexible working section even without the exact words.
Retrieval. The pipeline retrieves relevant chunks and applies re-ranking, filtering, and context expansion. Good retrieval is the difference between a useful RAG system and a frustrating one. Most failed implementations fail at retrieval, not generation.
Generation. Retrieved chunks are combined with the question and sent to a language model. The answer is grounded in your documents, not hallucinated from general training data. If the answer isn’t in your documents, the system says so.
Citation and Verification. Every answer includes links to the specific documents and sections it was drawn from. Staff can click through to verify. This builds trust and makes the system genuinely useful.
Where RAG Pipelines Deliver Value
Internal Knowledge Base — Onboarding time drops because new starters can find answers themselves. Institutional knowledge stops being trapped in individuals’ heads.
Client-Facing Support — Your support team queries the knowledge base to answer customer questions accurately and consistently, grounded in actual documentation.
Legal and Compliance Research — Faster research with full source citations for audit trails.
Sales Enablement — Search past proposals, case studies, competitive intelligence, and product documentation to prepare for calls and build proposals faster.
Technical Documentation — Query technical docs, API references, architecture decisions, and incident reports. Reduces the “ask the person who built it” bottleneck.
What We Build With
Vector Databases (Pinecone, Weaviate, Qdrant, pgvector)
Semantic search and retrieval at scale
Language Models (Claude, GPT-4, Mistral, Llama)
Generation with configurable model selection
Embedding Models (OpenAI, Cohere, open-source)
Document and query embedding
Document Processing (Unstructured, LlamaIndex, LangChain)
Parsing, chunking, metadata extraction
Cloud Infrastructure (Azure, AWS, GCP)
Hosting within your environment or ours
Interface (Web app, Slack bot, Teams bot, API)
However your team wants to interact
We’re not locked to any single stack. The architecture is chosen based on your requirements. The AI Audit evaluates your documents, identifies the best architecture, and gives you exact costs.
What RAG Can’t Do
Reason about information it doesn’t have. If the answer isn’t in your documents, the system can’t generate it. This prevents hallucination but means document coverage determines usefulness.
Replace expert judgement. RAG surfaces the relevant policy, precedent, or data point. It can’t tell you whether to apply it in this specific situation.
Handle documents that change hourly. RAG pipelines update on a schedule — typically daily. For most business documents, that’s more than sufficient.
Work well with extremely small document sets. Under 50 documents totalling fewer than 200 pages, a RAG pipeline is overkill.
Guarantee 100% accuracy. Users should treat RAG answers as very good first drafts of research, not infallible oracle responses. Citations let users verify.
Typical Costs and ROI
| Scope | Typical Cost | Ongoing Cost |
|---|---|---|
| Small knowledge base (under 1,000 documents) | £5,000 – £10,000 | £50–£200/month |
| Medium knowledge base (1,000–10,000 documents) | £10,000 – £20,000 | £150–£500/month |
| Enterprise (10,000+ documents, multiple sources) | £20,000 – £40,000 | £300–£1,000/month |
Ongoing costs cover hosting, AI model API calls, and vector database operations, scaling with query volume and document count.
Frequently Asked Questions
Find Out If RAG Is Right for Your Business
The AI Audit assesses your document landscape, estimates query patterns, and recommends the right architecture — including whether RAG is actually the best solution or something simpler would work.
Book an AI Audit