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 reference materials, 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
| Standard | What It Protects |
|---|---|
| Chunk overlap and boundary tuning benchmarked against your query distribution | Avoids arbitrary defaults that work only in demos |
| Embedding model comparison on actual retrieval tasks | Keeps model choice tied to corpus behavior, not vendor preference |
| Retrieval metrics tracked in production | Makes faithfulness, citation accuracy, latency, and cache behavior visible |
| Context window budget management | Maximizes signal per token spent |
| Fallback chains across vector search, graph traversal, SQL, and refusal | Gives the system a safe path when the corpus cannot support an answer |
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 plus 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 plus vector hybrid: Neo4j plus Pinecone or 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 |
| Small corpus with simple keyword search needs | Full-text search may be enough; avoid RAG if retrieval complexity is not justified |
| 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.
Related Paths
| If You Need To | Read |
|---|---|
| Check production readiness | The Production-Ready RAG Pipeline: An Engineering Checklist |
| Move beyond the demo | Enterprise RAG Beyond the Demo |
| Handle multi-hop retrieval | Graph RAG: Why Vector Search Alone Fails Multi-Hop Agent Queries |
| Reduce retrieval latency pressure | Streaming RAG: Real-Time Retrieval for Agents That Can’t Wait |
| Compare retrieval against fine-tuning | RAG vs. Fine-Tuning: A CTO’s Cost-Effective Guide |
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