Discover
Understanding goals, users and constraints.

From the first question about where AI fits, through to a system running in production.
AI Strategy
Finding where AI genuinely helps, and where it plainly does not.
LLM Systems
Retrieval, prompting and tool use, built into a working system.
AI Agents
Agents that take real actions, with the guardrails to be trusted.
Evaluation
Test sets and metrics that show whether a system is improving.
Full-Stack
Web and mobile apps, APIs, and the infrastructure underneath.
Automation
Removing the manual steps that quietly consume a team’s week.
A structured approach to building AI systems that survive contact with real use.
Discover
Understanding goals, users and constraints.
Design
Shaping the system and how it behaves.
Build
Writing it, wiring it, making it real.
Evaluate
Measuring whether it actually works yet.
Ship
Releasing it, then watching how it behaves.

Most AI projects fail on the parts nobody demos: the data, the evaluation, and knowing when to stop.
— Caden Holland
A quick overview of the process, and working together.
AI strategy and LLM systems — retrieval, agents and evaluation — plus the full-stack engineering to put them into production.
Teams that have decided AI belongs somewhere in their product and want help working out where, and teams that need an existing system made reliable.
Whatever the problem calls for. Usually TypeScript and Python, a hosted model API rather than a self-hosted one unless there is a reason, and an evaluation suite from the first week.
It depends on the scope, and anyone quoting a number before discovery is guessing. Expect a written estimate after the first conversation, and a smaller first piece of work if the shape is not clear yet.
Yes — remotely, across time zones, with a regular meeting slot that suits your working hours.
Send a short description of the problem through the contact form. If it is a fit, the next step is a call to scope it; if it is not, I will say so and point you somewhere better.