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, not just retrieval recall.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Production Retrieval Infrastructure
We design RAG systems that work reliably on real enterprise data — not clean demo datasets. Our pipelines handle messy PDFs, conflicting source documents, multi-language corpora, and queries that require reasoning across multiple document chunks.
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 |
| 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 — not just 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, not 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 — not vector search |
| Complex domain with entity relationships (legal, medical, engineering) | Knowledge graph + vector hybrid — Neo4j + Pinecone/Weaviate |
| 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, not just retrieve it | 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 maintain an extensive RAG engineering library on the ActiveWizards blog, covering production pipeline architecture, vector database benchmarks, and self-correcting retrieval patterns with LangGraph.
Related Reading
Related articles
The RAG Pipeline Audit: How We Diagnose Retrieval Quality Problems in 5 Days
A structured 5-day RAG pipeline audit methodology: architecture review, retrieval testing, ingestion analysis, hallucination mapping, and a priority remediation matrix.
RAGVector Database Selection for Enterprise RAG: Pinecone, Weaviate, Qdrant, and the Operational Reality
A practical comparison of Pinecone, Weaviate, Qdrant, pgvector, Milvus, and Chroma across the dimensions that matter in production: filtering, multi-tenancy, cost, and migration paths.
RAGChunk Strategy Failures in Production RAG: When Your Chunking Works in Dev and Breaks in Production
Why RAG chunking that passes dev tests collapses in production: document diversity, table handling, size failures, overlap traps, and how to build quality metrics.
Discuss your RAG & Retrieval Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.