AI that actually knows your business — RAG pipelines built for accuracy
Retrieval-augmented generation connects AI to your real documents, databases, and knowledge. Every answer is grounded in your data, cited to its source, and free from hallucinations. Production-ready in 2–4 weeks.
How we de-risk this: free demo → paid discovery (half up front, half on spec delivery - you own the spec) → then the full build. No black boxes.
The AI stack we build on
95%
Accuracy with RAG vs 60% without
Real-time
Data access & sync
Zero
Hallucination guarantee
2-4wk
From kickoff to production
Why Businesses Choose RAG Pipelines
RAG pipelines give your AI grounded, accurate access to your actual business knowledge — not guesswork. Every answer is traceable, every source is cited, and your data stays private.
Document Ingestion & Chunking
We ingest your documents — PDFs, Word files, emails, Confluence pages, Notion databases — and break them into optimally-sized chunks for retrieval. Smart chunking preserves context so answers are coherent, not fragmented.
Semantic Search
Your knowledge base is indexed with state-of-the-art embeddings that understand meaning, not just keywords. Users find exactly what they need even when they don't use the right terminology — because the system understands intent.
Citation & Source Tracking
Every AI-generated answer links back to the exact document, page, and paragraph it came from. Users can verify claims instantly, building trust and ensuring accountability across your organisation.
Multi-Format Support
PDFs, Word documents, emails, spreadsheets, Slack threads, HTML pages — your RAG pipeline handles them all. We normalise content from any source into a unified, searchable knowledge base.
Real-Time Data Sync
Your knowledge base stays current automatically. When documents are updated, added, or removed, the pipeline re-indexes in near real-time — so your AI always has the latest information.
Access Control & Permissions
RAG pipelines mirror your existing access controls. Users only see answers derived from documents they're authorised to access. Role-based permissions, SSO integration, and audit logging come standard.
See RAG search your actual documents — in a free 30-min demo.
Book demoKnowledge Base Sizing Calculator
Estimate the right RAG architecture, indexing time, and running costs for your knowledge base. Adjust the inputs to match your data.
Recommended architecture
Simple RAG
Single vector store with basic chunking. Ideal for smaller document sets with straightforward queries.
Indexing time
8.2 hrs
Initial ingestion
Accuracy estimate
91%
With source citations
Estimated monthly cost
£220 – £535
Infrastructure + embedding + LLM inference
Estimates based on typical RAG pipeline configurations. Actual costs depend on document complexity, query patterns, and hosting choices.
How We Build RAG Pipelines
We follow a proven four-step process to deliver RAG pipelines tailored to your data, your security requirements, and your team's workflows.
- 1
Data Audit & Strategy
We map your document landscape — formats, volumes, update frequency, access patterns, and sensitivity levels. This phase defines the chunking strategy, embedding model selection, and retrieval architecture before any code is written.
- 2
Pipeline Architecture
Our engineers design your RAG stack: vector database selection, embedding pipeline, retrieval strategy (dense, sparse, or hybrid), re-ranking layers, and query routing. We choose the right architecture for your scale and accuracy requirements.
- 3
Build & Fine-Tune
We build the ingestion pipeline, configure chunking and embedding, set up the retrieval chain, and fine-tune retrieval quality against your real queries. Access controls, citation tracking, and error handling are built in from the start.
- 4
Deploy & Monitor
Post-launch, we monitor retrieval accuracy, latency, and usage patterns through analytics dashboards. We continuously optimise chunking strategies, re-ranking models, and data sync pipelines based on real-world performance.
RAG Pipeline vs Fine-Tuning vs Prompt Engineering
There are several approaches to making AI work with your data. RAG pipelines offer the best balance of accuracy, freshness, and cost for most enterprise use cases.
| Capability | Prompt Engineering | Fine-Tuning | RAG Pipeline |
|---|---|---|---|
| Data Freshness | Static — limited to prompt context | Frozen at training time | Real-time — always current with your latest data |
| Accuracy | ~60% on domain-specific questions | ~75% but degrades over time | 90-97% with source citations |
| Hallucination Risk | High — no grounding in real data | Medium — can still confabulate | Near-zero — answers grounded in retrieved documents |
| Source Citations | None | None | Every answer linked to exact source |
| Cost to Update | Cheap but limited | Expensive retraining required | Automatic re-indexing at low cost |
| Data Security | Data may be sent to third-party APIs | Data used in training pipeline | Data stays in your infrastructure |
| Scale | Limited by context window | Limited by training data size | Millions of documents, no context limit |
| Time to Deploy | Hours | Weeks to months | 2-4 weeks for production-grade pipeline |
Industries Using RAG Pipelines
RAG pipelines deliver measurable results wherever teams need fast, accurate access to large document collections. These are the sectors seeing the strongest returns.
Legal Knowledge Bases
Financial Document Search
Technical Documentation
Customer Support Knowledge
Compliance & Regulatory
Research & Analysis
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Frequently Asked Questions About RAG Pipelines
See RAG search your documents — free demo
In a free 30-minute session, we'll connect a RAG pipeline to a sample of your actual documents and show you cited, accurate answers in real time. See exactly how retrieval-augmented generation transforms your team's access to knowledge.
- Free demo on your real data - no commitment
- Paid discovery phase - half up front, half on spec delivery. You own the spec.
- Only then do we commit to the full build