RAG Implementation Services

Retrieval-Augmented Generation for Enterprise Knowledge

RAG (Retrieval-Augmented Generation) connects AI to your documents and knowledge bases. Instead of hallucinating, AI retrieves relevant information from your content and generates accurate answers with source citations. We build production RAG systems that actually work.

Getting Started

1. Knowledge Assessment (Free consultation) Discuss knowledge sources, access control needs, use cases, user count. Estimate complexity and ROI.

2. RAG Design & Planning (2-3 weeks, £5k-£10k) Review document sources, test embeddings with samples, plan architecture, estimate costs (initial + ongoing).

3. Implementation (10-15 weeks, £30k-£70k) Build full RAG pipeline: ingestion, indexing, retrieval, reranking, generation, access control, deployment, monitoring.

Get Started

Frequently Asked Questions

RAG: Retrieves knowledge from documents at inference time, citations, always current, cheaper at scale. Fine-tuning: Bakes knowledge into model weights, no citations, frozen in time, expensive retraining. Use RAG for dynamic knowledge, fine-tuning for static tasks (style, format, domain language).
Retrieval: 85-95% correct documents in top 5 with good embeddings + reranking. Answer quality: 80-90% correctness (evaluated by domain experts). Better than pure LLM hallucination, not perfect. Human review recommended for critical use cases.
Inherit permissions from source systems (SharePoint ACLs, AD groups, database roles). Store permission metadata in vector index. Filter search results by user permissions before retrieval. Users only see answers from authorized documents. Tested with security reviews.
Automated re-indexing: daily/weekly sync with source systems, or webhook-triggered updates when documents change. Old chunks removed, new chunks indexed. Typical sync: nightly for most orgs, hourly for fast-changing content. Monitor index freshness.
10-15 weeks typical. Simple (1-2 sources, basic access control): 8-10 weeks. Complex (5+ sources, complex permissions, custom integrations): 14-18 weeks. Includes knowledge audit, integration, indexing, pipeline build, testing, deployment.
Initial build: £30k-£70k depending on complexity. Ongoing: £500-3k/month (embeddings, vector DB, LLM generation, reranking). More cost-effective than fine-tuning for dynamic knowledge. ROI typically 6-18 months for organizations with 100+ knowledge workers.
Yes. Use multilingual embeddings (Cohere Embed v3 supports 100+ languages, OpenAI ~50 languages). Query in one language, retrieve documents in any language. Generation model needs multilingual support (GPT-4, Claude, Gemini). Quality varies by language (English best, major languages good).
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