RAG & Agentic AI Development
Retrieval-augmented generation systems, agentic workflows, and private LLM applications for knowledge work.
RAG systems that actually answer correctly
We build retrieval-augmented generation systems that don't hallucinate — grounded answers with citations, evaluation harnesses, and retrieval pipelines tuned for your specific corpus.
What we build
Internal knowledge assistants ("what does our documentation say about X"), customer support copilots that draft responses from your policies, document Q&A over contracts/specs/pdfs, and agentic workflows that take multi-step actions across your tools.
The hard parts
Good RAG isn't just "embed and search." It's chunking strategy, hybrid retrieval (keyword + vector), re-ranking, prompt engineering, evaluation datasets, and observability. We've shipped this in production and we know where it quietly fails.
Agentic when it fits
Agents are powerful when a task genuinely needs multi-step reasoning with tool use. We build agentic systems with clear guardrails — every action is logged, every tool call is sandboxed, and a human is in the loop for high-stakes decisions.
Frequently asked questions
What vector database do you use?
We default to pgvector when you already have Postgres (one less system to operate), and recommend Pinecone / Qdrant / Weaviate for higher-scale standalone deployments. We help you choose based on your existing infra and scale projections.
How do you prevent hallucination?
Three layers: retrieval-grounded prompts that force the model to cite sources, an evaluation harness that flags answers lacking grounding, and a confidence threshold below which the system declines to answer instead of guessing.
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