Senior engineers who build. AI that multiplies.

Most agent demos work once, in a controlled environment, with no failure handling. We build tool-use agents with LangGraph state machines, MCP servers, and CrewAI pipelines — with LangSmith observability and human-in-the-loop checkpoints so you can actually operate them.

AI-assisted development ships code faster than manual QA can validate it. We build QA infrastructure — LLM-generated test scaffolding, self-healing Playwright suites, Chromatic visual regression, and LangSmith eval harnesses — so your quality gates scale with output. Built for teams using Cursor, Copilot, or any LLM-in-the-loop workflow.

Most AI product failures aren't engineering failures — they're strategy failures. We help you identify which AI investments build on proprietary data or workflow depth versus which ones you're renting from an API provider who'll ship the same feature in six months.

AI agents fail at the API layer more often than the model layer — ambiguous schemas, inconsistent errors, and undocumented edge cases are the usual culprits. We design APIs spec-first using OpenAPI 3.1 and MCP tool schemas so they work reliably for both agent tool-calling and human developers from day one.