AI Agents & Workflow Automation
Most AI demos impress and save nobody any time. The ones that work are narrow, wired into a real system of record, and measured in hours returned per week. We start by finding a process with a countable cost, and we tell you when a rule-based script would beat an agent.
Who this is for
Your team retypes data between two systems every day.
You process a queue of documents, emails or applications by hand.
You have an AI pilot that impressed everyone and changed nothing.
How we build it
Technology we use — and why
Not a logo wall. The reason behind each choice.
Tool-calling agents with strict schemas, not free-form prompts against production data.
Runs are retryable, logged, and auditable after the fact.
Every run, input and decision is stored so you can audit an outcome months later.
When the input is the open web, at volume, reliably.
Related work
Engagement models
We don't publish a price list. You get a band in the quote flow, and a real number after the call.
Written scope, milestone plan, fixed price per milestone. Best when you know what you need built.
A committed number of senior engineering days per month. Best when scope will keep evolving.
One or more engineers embedded in your team, in your standups, with architecture review from the founder.
Questions people actually ask
Will this replace my staff?
How do you stop it making things up?
What does it cost to run?
Could a simple script do this instead?
Do you sign NDAs?
Does our data go to a third-party model?
Tell us what you're trying to build.
Thirty minutes, no cost, no pitch deck. You'll leave with an honest read on scope, timeline and cost.
