Software that thinks, and acts on its own.

There’s no published tier — every build is scoped to the work. We start with discovery so the outcome is clear, then send an estimate that reflects what your agent actually needs. Ongoing work runs on retainer, billed to the minute. And if a simpler workflow would solve the problem for less, we’ll tell you that first.

A chatbot answers questions. An agent does the task: it executes multi-step work against your actual systems — your APIs, your databases, your CRM — and hands off cleanly to a human when it should. We build the agent plus the production scaffolding around it: tool access, guardrails, and monitoring, so you can bet a process on it.

Yes — that’s usually the point. We embed the intelligence inside the tools your team already opens every day: your CRM, your ERP, your systems of record. We’ve been building software and data integrations since long before the AI hype cycle, so wiring a model into an existing stack is familiar ground.

We build across Claude, Gemini, GPT, and more, and pick the right model for each job. We’re pragmatic about models, not loyal to a vendor. The model is one component — the engineering around it is what makes the system reliable, and it’s built so a better model can be swapped in when one ships.

With the unglamorous engineering that turns a demo into something dependable: evaluation suites that test the agent before it touches real work, guardrails that constrain what it’s allowed to do, failure modes handled on purpose, and monitoring that tells you when it drifts. Work that needs human judgment gets handed to a human.

That’s the most common way companies find this service. The gap usually isn’t the model — it’s everything around the model: tool access, guardrails, monitoring, and plain engineering discipline. We assess what your pilot proved, then build the production scaffolding it was missing, so it ships and stays shipped.