RAG & Retrieval Engineering
Production retrieval-augmented generation pipelines that answer questions accurately from your data. We architect hybrid retrieval systems combining vector search, knowledge graphs, and SQL, with evaluation frameworks that measure answer quality beyond retrieval recall.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Production Retrieval Infrastructure
We design RAG systems that work reliably on real enterprise data: messy PDFs, conflicting source documents, multi-language corpora, and queries that require reasoning across multiple document chunks.
Professional services, legal, advisory, tax, research, and customer operations teams need retrieval that can explain source boundaries, preserve permissions, cite the evidence trail, and refuse when the corpus cannot support the answer.
What We Build
| Capability | What We Deliver |
|---|---|
| Hybrid retrieval pipelines | Vector similarity search (Pinecone, Weaviate) combined with knowledge graph traversal (Neo4j) and structured SQL queries in a single agentic reasoning loop |
| Professional knowledge systems | Retrieval for legal, advisory, tax, research, ticket, and policy corpora where source trails, permissions, and refusal behavior matter |
| Chunking and embedding optimization | Document-aware chunking strategies tuned per content type (contracts, technical docs, support tickets), with embedding model selection benchmarked on your actual queries |
| Re-ranking and filtering | Cross-encoder re-rankers, metadata filtering, and MMR diversity to eliminate the “same answer from 5 chunks” problem |
| Evaluation and monitoring | LLM-as-Judge pipelines measuring faithfulness, relevance, and completeness beyond cosine similarity scores |
| Self-correcting RAG agents | LangGraph-based pipelines that detect retrieval failures, reformulate queries, and route to alternative data sources automatically |
Engineering Standards
- Chunk overlap and boundary tuning benchmarked against your query distribution instead of arbitrary defaults
- Embedding model A/B testing (OpenAI ada-002 vs. Cohere embed-v3 vs. local models) on your actual retrieval tasks
- Retrieval metrics tracked in production: answer faithfulness, citation accuracy, latency p95, cache hit rate
- Context window budget management — dynamic chunk selection to maximize signal per token spent
- Fallback chains: vector search → graph traversal → SQL → “I don’t know” with source attribution
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Internal documents (PDFs, wikis, tickets) that employees need to query | Hybrid retrieval pipeline — vector search + metadata filtering |
| Structured data in databases that needs natural language access | Text-to-SQL pipeline with validation |
| Complex domain with entity relationships (legal, medical, engineering) | Knowledge graph + vector hybrid — Neo4j + Pinecone/Weaviate |
| Legal, advisory, tax, research, or customer operations teams need answerable source trails | RAG Engineering if the build is new; RAG Pipeline Audit if a retrieval system already exists |
| Customer-facing Q&A where wrong answers cause trust or legal risk | Self-correcting RAG with faithfulness evaluation and citation |
| Need agents that reason over retrieved data and act through tools | AI Agent Engineering — agentic RAG with tool use |
| Under 1,000 documents with simple keyword search needs | Full-text search (Elasticsearch) — RAG is over-engineering |
| RAG is deployed but retrieval quality, latency, or cost are not visible | AI Observability Engineering — instrument before optimizing |
Depth of Practice
We publish RAG engineering notes on the ActiveWizards blog, covering retrieval architecture, vector database benchmarks, and self-correcting retrieval patterns with LangGraph.
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