Retrieval-augmented generation with grounding signals, citation handling, and the architecture decisions that determine whether RAG actually works at scale.
Where it fits in our builds
Patient triage is the shape of system where RAG earns its keep: deterministic red-flag rules fire first, then the model answers grounded in retrieved clinical guidance, with sources attached. The same retrieval discipline carries finance and legal products.
RAG is the dominant pattern for production LLM applications today, but most implementations break because of poor retrieval, not poor LLMs. We've shipped RAG across commerce, operations and healthcare, including HIPAA-architected clinical retrieval. Our focus is retrieval quality, grounding, and evals, not embedding everything and hoping the model figures it out.
Pinecone, Weaviate, pgvector (Postgres), Qdrant. Chosen per project based on scale, cost, and hybrid search needs.
OpenAI embeddings, Voyage AI, sentence-transformers. Domain-specific selection, batching, and cost optimization.
Hybrid search (vector + keyword), re-ranking, multi-step retrieval, and query rewriting.
Source attribution, confidence scoring, and hallucination detection patterns.
PDF/HTML/markdown chunking strategies, metadata extraction, versioning, and deletion handling.
Ground truth datasets, eval frameworks (Ragas, custom), and regression testing.
Ingestion, indexing, retrieval and grounding are listed above as capabilities. In practice they belong to different people, and the handoffs are where grounding is lost.
Medical knowledge retrieval and clinical guideline lookups.
Regulatory document search and compliance lookups.
Brand knowledge bases and competitor research engines.
A custom checkout and payment platform for Shopify merchants: gateway integration, Apple Pay and Google Pay, multi-store installs, disputes and settlement reporting.
9 stalled pull requests and a 3-week bug backlog cleared; 3 held features then shipped.
A Shopify app that inverts order entry for live sellers: scan the product, and the draft order builds itself.
30+ SKUs scanned into one Shopify draft order; the old version choked on large ones.
A 60-second skin scan on a pharmacy shelf that resolves into one of more than a thousand personalised formulas, handed to the customer the same visit.
1,000+ skincare formulas resolved from a 60-second phone scan in 55+ San Pablo pharmacies, before the customer leaves the store.
LLM applications, AI agents, and computer vision. The broader AI stack RAG sits inside.
ExploreMulti-agent systems and tool use, with retrieval as a tool inside autonomous workflows.
Explorepgvector-backed retrieval with your source of truth and embeddings in one database.
ExploreA senior team that owns retrieval work alongside yours, thinking in retrieval quality, grounding and evals rather than embeddings alone.
We pick tools because they ship, not because they trend. Every technology we list is one we have deployed to production, debugged under load, and would choose again for the right problem.
Straight answers on vector databases, re-ranking, and retrieval quality.
It depends on scale and what you already run. pgvector when you have Postgres and under ~10M vectors; Pinecone for large hosted workloads; Weaviate for built-in hybrid search; Qdrant when self-hosting matters. We choose per project, not by default.
No account managers. No discovery theatre. A direct conversation about your retrieval architecture and what it takes to make RAG actually work in production.