The work
We build AI agents that earn their keep — and stay to keep them earning it.
We come in, learn the one job worth doing, set the targets it has to hit, and build the agent and the scaffold around it. Then we stay — measuring results, tuning for accuracy, and keeping it cost-efficient as your business and the models keep evolving.
The path
From a real problem to an agent that earns its keep.
- 1
Learn the need
We come on-site and watch how the work really happens — the refund, the quote, the RFP that ties up three people for two days — and we pick the one job worth automating first. Not the cathedral. The one job.
- 2
Set the targets
Before any code, we write down what good looks like: what the agent can read, what it can only draft, and what it must never touch — records, money, anything it can't take back — plus the numbers it has to hit: accuracy, turnaround, and cost per run. Targets first, so there's something honest to measure against later.
- 3
Build the agent and its scaffold
The agent is the worker; the scaffold is the workbench around it. We build the smallest agent that does the real job, then the scaffold that makes it trustworthy: written permissions for what it can and can't touch, a source trail behind every answer, and a defined point where it stops and asks a person. The agent is the easy half; the scaffold is why you can put your name on it.
- 4
It runs, and it shows its work
In production it produces what a launch usually lacks: logs and an audit trail. Proof is a trail a person can inspect — the tickets, the sources it checked, and the ones it couldn't reach, plus the tokens each run spent — not the agent's say-so. If anyone asks “what did it do, and why,” there's a straight answer.
- 5
Stay and keep it sharp
An unmaintained agent doesn't fail loudly. It keeps working on stale truth until it costs you. So we stay — tuning the agent and its scaffold as the work and the models shift, keeping the answers accurate, and keeping cost per run efficient. Sometimes that means taking tools away, not adding them: the cheapest way to make an agent more trustworthy is to remove what it shouldn't touch.
Where the value compounds
A tended agent keeps getting better.
An agent isn't software you ship and forget. It's closer to a boat — it lives in motion, and it needs tending.
Two things move under every agent. Your business drifts — a process changes, a document goes stale — and the model itself gets better. The second one surprises people: a guardrail that protected a weaker model can trap a stronger one. Maintenance is keeping the agent fit between those two moving things.
Cost is part of that craft. Handed exactly what the job needs, an agent stops re-reading what it already knew on every run — the rediscovery Pinecone estimates can consume up to 85% of an agent's compute. Keeping context tight keeps cost per run low and predictable: real value at a price that makes sense.
So we measure what it reads, what it touches, whether its answers hold up, and what each run costs — and we tune as it goes, so the agent keeps earning its place.
The first step is a conversation about where an agent would actually earn its keep.
Discovery and build are fixed-scope; keeping it alive is a monthly retainer.