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What we build

What you get

  • Retrieval-augmented (RAG) search over contracts, policies, filings and manuals
  • Text-to-SQL assistants that answer plain-English questions with a table or chart
  • Multi-agent workflows with tool calling, approvals and hand-offs
  • Document extraction from PDFs, scans, transcripts and video
  • Evaluation, monitoring and cost controls for models in production

Tools we often use

  • Vertex AI
  • Anthropic Claude
  • Google Gemini
  • Agent Development Kit
  • Discovery Engine
  • ChromaDB
  • BigQuery
  • Python

Approach

How we do it

  1. Ground it in your data

    Layout-aware parsing, semantic chunking and metadata so answers cite the right page.

  2. Keep a person in the loop

    Agents propose, people approve. Every step is logged for review.

  3. Measure accuracy

    A test set built with your experts, run on every change before it ships.

  4. Balance cost and speed

    We tune retrieval, model choice and reasoning depth against latency and token spend.

Considering AI and agentic solutions?

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