AI product development
Turn a product problem into a production-oriented AI feature: user flow, data boundaries, model interactions, integrations, and delivery plan.
How we build AI productsPractical AI engineering, from the product decision to the shipped system
We work with founders and engineering leaders when the hard part is no longer trying a model. It is deciding what belongs in the product, making it dependable, and keeping a small team moving. The work can start with a focused technical discovery or continue through design, implementation, and iteration.
Agents and LLM applications
Design workflows where models retrieve context, call tools, route work, and return control to people when judgment matters.
How we engineer agentsAI-assisted engineering
Improve how a lean team plans, implements, reviews, and ships software without treating speed as a substitute for engineering discipline.
How we improve workflowsWhat production-ready means here
We design around the real operating conditions of an AI feature: useful context, clear tool and data boundaries, failure paths, evaluation criteria, observability, human review where appropriate, and the ordinary software engineering needed to maintain it. The aim is a useful product system, not a demo with a model attached.
Evidence from product engineering
Our work includes engineering leadership, team workflows, and client-facing applications for WeatherXM, a globally distributed Web3 and IoT platform.