Senior engineers who build. AI that multiplies.

Everyone has an agent demo. Almost nobody has an agent in production that they trust. We build tool-use agents using LangGraph state machines, MCP (Model Context Protocol) servers, and CrewAI multi-agent pipelines — with observability via LangSmith, human-in-the-loop checkpoints, and the kind of failure handling that turns a demo into a system you can actually operate.

Cursor and Copilot write code faster than manual QA can validate it. The flaky test problem gets worse as codebases grow. LLM features need eval harnesses, not just unit tests. We build AI-augmented QA infrastructure — AI-generated test suites, self-healing Playwright selectors, visual regression pipelines, and LLM evaluation harnesses — so your quality gates actually scale.

Most AI product failures are not engineering failures — they are strategy failures. The AI wrapper trap: you build a thin layer over GPT-4, your users love the demo, and then OpenAI ships the feature natively in ChatGPT. We help you find where AI creates durable advantage — proprietary data, workflow depth, network effects — not just capability you are renting from an API.

AI agents consume APIs as tools. Poorly described parameter names, inconsistent error responses, and undocumented edge cases cause agents to fail in ways that are hard to debug. We design APIs with OpenAPI 3.1 specifications and MCP-compatible tool schemas so your APIs work for both human developers and AI tool-calling architectures from day one.