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.
Our stance
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 three industries, including HIPAA-architected healthcare retrieval. Our focus is retrieval quality, grounding, and evals, not embedding everything and hoping the model figures it out.
Capabilities
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.
Infrastructure that holds up when traffic arrives
Tooling
Hard lines
Saying no to these is part of the service. Each one is a production incident we would rather not relive.
Quality drops sharply at scale. Re-ranking is non-negotiable for production.
Documents have structure. Headers, sections, and tables need different chunking.
Users need to verify. No citation = no trust = no adoption.
A vector DB is a cache. The source of truth lives in Postgres or a document store.
RAG quality degrades silently. Without evals, you find out from your users.
Query-document length asymmetry hurts retrieval. Adjust the strategy.
Medical knowledge retrieval and clinical guideline lookups.
Regulatory document search and compliance lookups.
Brand knowledge bases and competitor research engines.
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
We built the technical foundation for a SaaS platform that automates complex legal document creation with usage-based pricing system
We built a minimum viable product to connect disparate advertising APIs into a single, intuitive user interface.
Adjacent stacks
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.
ExploreNeed to extend your team with engineers who have shipped production RAG? We embed senior engineers who think in retrieval quality, grounding, and evals, not just embeddings.
Production engineering
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.