Agents on the work that's ready. People on the work that matters.
You've seen the demos, and you may have run a pilot that never reached production. We help you deploy agents across business workflows and the SDLC, guided by what your data is ready to support today.
AI-native engineering comes next. Deploy agents across planning, building, testing, and shipping, calibrated to the control your codebase requires.
Identify high-ROI business processes, build secure custom agents around your existing systems, and hand control to your team to run independently.
Know which one you need
FAWM decides which agents to build.
FINE decides how we build them.
Need both? Most of our clients end up there.
How we evaluate and deploy AI
We replace "trust us" with clear frameworks at every stage, starting with a short initial step
Most teams already have coding assistants. The hard part is knowing what agents can safely own. We map where context lives across your repos, specs, and teams to determine what agents can take on and where humans need to stay in control.
The harness for each level: Context files, tests, evals, and review gates.
Most agent projects start with a use case and a deadline. FAWM starts with the job. We read your systems and SOPs, sit with the people doing the work, and break it down into jobs an agent could own.
Every job faces three tests: Value, data readiness, and clear ROI.
Find your starting point. Leave with a blueprint.
See the end state before you commit. We look at your systems, talk to your people and prototype what you're about to build.
We map the jobs your teams do, score each one, and show which ones agents should own and how much oversight they need.
A working version of the end state, so people react to something real instead of a slide.
How it fits your systems, data and integrations, and what has to change before you build.
Which jobs agents should own and how much oversight each needs, when there's a real agentic opportunity.
The case for the investment, tied to the KPIs that move: cost, cycle time, error rate or delivery speed.
From the products you build to the systems you run
Pick the workflow that hurts most. Quote-to-cash, incident triage, claims intake. We map it job by job, decide what agents own and what people keep, and build the agents with the approval screens and exception paths your team needs to trust them. Then we measure it against the number you named at the start.
Building a new product or platform? This is where agents can do the most, because the context starts clean. We write the specs first, let agents build against them, and keep people on the decisions that shape the product. You see working software early and often, not a status report.
Your oldest systems hold rules nobody wrote down. Rushing agents into that code is how production breaks. So agents read it first. Your engineers correct it. Then changes move faster.
We design for both users of your system: the people and the agents.
Agents fail quietly when people can't see what they're doing. And we still design products the way we always have, with research, flows and interfaces people like using.
WHAT WE'VE LEARNED SO FAR
Here's what we believe after building agents into real systems in energy, hi-tech and pharma.
"Automate customer onboarding" sounds like a plan. It's really forty jobs, and maybe six of them are ready for an agent. Find those six first.
Coding assistants help teams ship more. Google's 2025 DORA research found they also go hand in hand with more failed changes and rework. Speed without review gates just moves the problem to Friday night.
It answers questions after the fact. An agent needs to act on it in the moment. That gap is usually the real project.
Regulation, liability and judgment decide that. Enthusiasm doesn't.
Results we're proud of
Our clients love what we do

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Deep industry expertise meets intelligent execution.
Neither were most of our clients. Give us thirty minutes and bring one workflow or one system you're worried about. We'll tell you honestly which practice you need, whether you need both, or whether agents belong there yet.
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