RAG & Agent Build

A RAG system is a search engine with a language model attached — and most underperforming RAG systems have a search problem, not a model problem. We build the retrieval layer as an engineering artifact: measured, tuned, and testable.

Retrieval pipelines

• Chunking that fits the corpus — structure-aware splitting for contracts, tickets, wikis, and tables; parent-document and small-to-big patterns where the retrieval unit should differ from the generation context.
• Embeddings and hybrid search — dense retrieval combined with keyword/BM25 where vocabulary matters, metadata filtering that is verified rather than assumed, and reranking when the candidate set is good but the ordering is not.
• Grounding and citation — answers that quote and cite retrieved passages, with unanswerable queries handled explicitly instead of improvised.

Agent workflows

For multi-step work — research flows, extraction pipelines, operations tooling — we build LangGraph state machines rather than free-running loops:

• Explicit graph structure with typed state, so behavior is inspectable and testable.
• Checkpointing for durable execution: workflows survive restarts and can pause for human approval mid-run.
• Human-in-the-loop steps where the cost of a wrong action is real: approvals, edits, escalation paths.
• Structured output with schema validation and repair, so downstream systems consume typed data instead of parsing prose.

How we work

We build in your repository, on your infrastructure, alongside your engineers. Every retrieval change lands with an eval delta attached — if we cannot measure that a change helped, it does not ship. Engagements are hourly time & materials, scoped through an initial conversation.