Custom agents for the work
that drains your team.
Production agents that do the repetitive, judgment-light volume — built with evals, human escalation, and your team in control. We build and run them. Augment, never replace — and we'll tell you plainly what not to hand to AI.
Most agent projects fail the same way — too broad, never measured.
The headlines say agents replace teams; the data says 74% of companies pull them back, often because they cost more human-hours to babysit than they save. That's not an argument against agents. It's an argument for scope discipline, evals, and a human seatbelt — the things we build in by default.
An agent pointed at "handle support" or "run ops" is set up to fail — the surface is infinite, the edge cases are endless, and nobody can tell whether it's working. An agent pointed at one repetitive, high-volume, judgment-light workflow — triage this queue, draft this first-pass, reconcile these records — ships and keeps shipping.
The other failure is invisibility. Without an eval harness, "it worked in the demo" quietly becomes "it's wrong 15% of the time and no one noticed." We grade against your real cases before and after deploy, and route the ambiguous minority to a person.
Narrow scope, measured behaviour, human escalation, operated by us. That combination is why the agents we build stay deployed — and why we'll say no to the ones that shouldn't be built.
The agents that survive are narrow, measured, and have a person to hand the hard cases to.What 74% pulled back taught the field
A scoped agent, gated by evals, with a seatbelt.
Four parts of one operated system — a single workflow, an eval harness, human escalation, and the running and maintenance most teams underestimate.
One scoped workflow, not a 'do-everything' agent
We pick a single repetitive, high-volume, judgment-light workflow — the kind that drains a person's week — and build an agent that does exactly that, reliably. Narrow scope is why it ships and stays shipped, instead of joining the 74% that get pulled.
Scoped · high-volume · reliableEvals before it touches production
Every agent is graded against a test set that mirrors your real cases before it runs on anything live, and continuously after. "It worked in the demo" is not a deployment criterion here; a measured pass rate is.
Eval harness · measured pass rateHuman escalation by design
The agent handles the confident majority and hands the ambiguous minority to a person, with context, instead of guessing. You decide the confidence threshold. The point is leverage with a seatbelt, not unattended autonomy.
Human-in-the-loop · escalation pathsBuilt, run, and yours
We build it, operate it, and keep it healthy as models and your processes change — the maintenance most teams underestimate. It's 100% your IP; take it in-house whenever you want.
Operated · maintained · your IPWhat we will — and won't — let an agent do.
A studio that says AI helps everywhere is selling. Here's the honest boundary, because that boundary is the product as much as the agent is.
Repetitive & judgment-light
High-volume triage, first-pass drafting, classification, reconciliation, research gathering — work that's tedious and well-defined. Leverage lands here.
Judgement & sensitivity
Anything ambiguous, brand-defining, or customer-facing at a hard moment is escalated to a person — the agent prepares it, a human decides.
Legal, claims, real risk
Compliance calls, regulated claims and high-stakes decisions stay with accountable people. We'll tell you so plainly — that's the trust copy, not a caveat.
For regulated catalogs, the claim boundary is specified in detail on Wellness & Supplement DTC.
Questions, answered straight.
Are you selling 'digital employees' that replace my staff?
No. We build agents that augment a team — they take the repetitive, judgment-light volume off people so the people do the work that needs judgement. We avoid "replace your staff" framing entirely; it's both wrong about how this technology actually performs and bad for trust.
Leverage with a person accountable, not autonomy you hope behaves.
What would you NOT use an agent for?
Anything that needs real judgement, carries legal or compliance weight, or touches a customer at a sensitive moment — those keep a human accountable, full stop. Anyone telling you "AI helps everywhere" is selling. We'll say plainly which parts of your operation are a fit and which aren't, in the audit.
I've heard agents need more maintenance than they save. Is that real?
It's real for agents deployed too broadly with no evals — about 74% of companies pull them back. We counter it with narrow scope, an eval harness, and operating the agent ourselves, so the maintenance is our job, not a surprise that lands on your team. Scope discipline is the whole difference between leverage and a liability.
How do I know it's actually working and not quietly failing?
Because it's measured. The eval harness reports a pass rate, escalations are logged and reviewable, and you see the same operating signals we do. An agent you can't observe is one we won't ship.
Do you actually run this kind of thing yourselves?
Yes — PlanePaper runs on its own fleet of agents day to day, which is where the scope discipline and the eval-first habit come from. We build what we operate. More on that on About.
Find the one workflow worth automating first.
Then we build and run it.
The $1,500 AI Brand Audit maps where AI genuinely fits your operation — and, just as honestly, where it doesn't. You leave knowing the highest-ROI agent to build first.