RAG Implementation Services

RAG implementation is building the full technical pipeline that connects AI language models to your organizational knowledge. Not just"upload PDFs to ChatGPT"—production-grade retrieval, reranking, and generation.

It's not: - Using ChatGPT with file upload (doesn't scale, no customization) - Simple keyword search with AI wrapper - Off-the-shelf knowledge base software (we build custom RAG for your needs)

It is: - Index your documents (SharePoint, Confluence, PDFs, databases, wikis) - Vector embeddings for semantic search - Reranking for precision (20-40% accuracy improvement) - Generation with source citations - Access control (users see only their authorized content) - Production deployment with monitoring

Up-to-Date Knowledge RAG uses current documents. Add new doc today, AI knows about it tomorrow. Fine-tuned models are frozen in time—knowledge from training data only.

Source Citations RAG answers cite sources ("based on Q3 Financial Report page 12"). Users verify accuracy. Fine-tuned models can't cite sources—you trust or don't trust.

Cost-Effective at Scale RAG: One-time indexing cost, cheap embedding inference. Fine-tuning: Expensive training ($1k-10k+), re-train when knowledge changes.

Flexibility RAG: Add/remove documents anytime, update index. Fine-tuning: Re-train entire model for new knowledge (expensive, slow).

Reduced Hallucination RAG: AI grounded in retrieved documents. Still hallucinate but much less. Fine-tuning: No grounding, higher hallucination risk.

1. Document Ingestion & Chunking - Connect to knowledge sources (SharePoint, Google Drive, databases, wikis) - Extract text from PDFs, Word docs, HTML, Markdown - Chunk documents (500-1000 tokens per chunk for optimal retrieval) - Preserve metadata (author, date, access permissions)

2. Embedding & Indexing - Generate embeddings with Cohere Embed v3, OpenAI text-embedding-3, or custom models - Store in vector database (Pinecone, Weaviate, Qdrant, pgvector) - Index updates automatically when documents change

3. Semantic Search (Retrieval) - User asks question - Generate query embedding - Retrieve top 20-50 candidate chunks from vector DB - Fast (sub-100ms retrieval)

4. Reranking (Precision) - Pass query + 20-50 candidates to reranker (Cohere Rerank 3) - Reorder by true relevance to query - Select top 3-5 chunks for generation context - 20-40% accuracy improvement vs embeddings alone

5. Generation with Citations - Send query + reranked chunks to LLM (GPT-4, Claude, Llama) - Generate answer grounded in retrieved context - Include source citations (document name, page, link) - Return answer + sources to user

6. Access Control - Inherit permissions from source systems - Users only see answers from documents they're authorized to access - Row-level security in vector database

Embeddings: - Cohere Embed v3 (best retrieval performance, multilingual) - OpenAI text-embedding-3 (good, simpler if using GPT-4) - Custom embeddings (domain-specific, self-hosted)

Vector Databases: - Pinecone (managed, scales easily, good for production) - Weaviate (open source, self-hosted option, feature-rich) - Qdrant (fast, self-hosted, cost-effective) - pgvector (PostgreSQL extension, integrate with existing DB)

Reranking: - Cohere Rerank 3 (20-40% accuracy improvement, essential for production) - Custom cross-encoder models (more expensive inference)

Generation: - GPT-4, Claude Opus/Sonnet (complex knowledge, high quality) - Llama 3 70B (cost-effective for high volume, self-hosted) - Mistral, Mixtral (open source alternatives)

Connectors: - SharePoint, Confluence, Google Drive (via APIs) - Databases (SQL, MongoDB, Elasticsearch) - Custom APIs and internal systems

Internal Knowledge Q&A

Employees ask HR policies, IT procedures, product specs. RAG retrieves from internal docs (SharePoint, Confluence), generates answer with citations. Impact: 40-60% reduction in "where do I find..." questions, 2-5 hours saved per employee per month

Customer Support Knowledge Base

Support agents search help docs, troubleshooting guides, product manuals. RAG provides answers with sources faster than manual search. Impact: 30-50% faster ticket resolution, more consistent answers

Legal Document Search

Lawyers search contracts, case law, precedents. RAG finds relevant clauses, summarizes with citations. Verify in source documents. Impact: 50-70% faster legal research, better coverage

Sales Enablement

Sales reps search case studies, competitive intel, product specs, pricing. RAG provides accurate answers with sources during customer calls. Impact: 50-70% faster access to sales materials, better-prepared conversations

Technical Documentation

Engineers search API docs, architecture specs, runbooks. RAG retrieves with code examples and citations, accelerating development. Impact: 40-60% faster technical knowledge access

Phase 1: Knowledge Audit & Integration (2-3 weeks) - Identify knowledge sources (SharePoint, wikis, databases) - Review access controls and permissions - Set up connectors and data pipelines - Extract and clean sample documents

Phase 2: Embedding & Indexing (2-3 weeks) - Choose embedding model (Cohere, OpenAI, custom) - Set up vector database (Pinecone, Weaviate, etc.) - Chunk documents optimally (test chunk sizes) - Index documents with metadata and permissions

Phase 3: RAG Pipeline Build (3-4 weeks) - Implement retrieval (semantic search) - Add reranking (Cohere Rerank 3) - Build generation with citations - Implement access control (inherit from sources)

Phase 4: Testing & Optimization (2-3 weeks) - Test with real user questions - Measure retrieval accuracy (correct documents in top 5?) - Optimize chunk size, rerank threshold, generation prompts - Gather user feedback and iterate

Phase 5: Deployment & Monitoring (1-2 weeks) - Deploy to production (web interface, Slack, Teams, API) - Monitor usage, accuracy, costs - Set up auto-updating index (sync with source systems) - Train users

Typical Timeline: 10-15 weeks for production RAG system Typical Cost: £30k-£70k depending on knowledge sources and complexity

Retrieval Quality: - Recall@5: Are relevant documents in top 5 results? (Target: 85-95%) - Precision@5: Are top 5 results actually relevant? (Target: 75-90%) - Rerank improvement: How much does reranking improve accuracy? (20-40% typical)

Answer Quality: - Citation accuracy: Do cited sources support the answer? (Target: 90-95%) - User satisfaction: Thumbs up/down on answers (Target: 75-85% positive) - Correctness: Evaluated by domain experts (Target: 80-90%)

Business Impact: - Time saved: Faster knowledge access (hours → minutes) - Adoption: Daily active users, queries per user - Efficiency: Reduction in escalations to colleagues or experts

Challenge: Poor Retrieval Accuracy Solution: Use reranking (Cohere Rerank 3), optimize chunk size (test 256/512/1024 tokens), improve query formulation, use hybrid search (semantic + keyword).

Challenge: Hallucination Despite RAG Solution: Explicitly instruct LLM to answer ONLY from context, cite sources, flag low-confidence answers, have humans review critical responses.

Challenge: Access Control Complexity Solution: Inherit permissions from source systems (SharePoint ACLs, Active Directory), filter vector search results by user, maintain security metadata in index.

Challenge: Outdated Index Solution: Automated re-indexing (daily/weekly sync with sources), webhook triggers on document updates, monitor freshness, notify users of stale content.

Challenge: Cost at Scale Solution: Use cheaper embeddings (Cohere vs OpenAI), optimize reranking (don't rerank all queries), self-host LLM (Llama 3) for generation, cache frequent queries.

You need RAG if: - Large knowledge base (100+ documents, growing) - Knowledge changes frequently (RAG vs fine-tuning) - Need source citations for trust/verification - Multiple knowledge sources (SharePoint, wikis, databases) - Access control required (not all users see all content)

You might not need RAG if: - Small, static knowledge base (<20 documents) - simpler solutions exist - Fine-tuning sufficient (knowledge rarely changes, no citations needed) - Just need search, not Q&A (traditional search may suffice) - Budget doesn't support RAG implementation cost

How is RAG different from fine-tuning?

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).

What's the accuracy of RAG systems?

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.

How do you handle access control?

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.

What if documents are updated?

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.

How long to implement RAG?

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.

What does RAG implementation cost?

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.

Can RAG work with multilingual content?

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).

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.

Book consultation to discuss RAG implementation for your enterprise knowledge.

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