The decision, written down
What inputs matter, what the rules are, where judgement enters, and what the escalation cases look like. This is the longest phase and the one that determines whether the rest works.
Services
The standard agents cover the workflows every finance function shares. What they do not cover is the review your industry requires, the allocation your contracts specify, or the exception pattern that only appears in your data. Those are buildable, and the interesting part is how you know it works.
Describe the judgement your team makes repeatedly. We will say whether an agent fits.
The situation
Most of the work is not the model. It is establishing what correct means and proving the thing meets it.
What inputs matter, what the rules are, where judgement enters, and what the escalation cases look like. This is the longest phase and the one that determines whether the rest works.
Several hundred real historical cases with known correct outcomes, built before any agent is written. Without it there is no way to distinguish a working agent from a plausible one.
Built against your data model with access to the context it needs — contract, history, budget, policy — rather than to the single document in front of it.
It has to know when it does not know. An agent that is confidently wrong ten percent of the time is worse than no agent, because the errors are the ones nobody checks.
Deployed at observe, promoted only when the shadow record justifies it, and bounded by the same ceiling as every other agent — no payment release, no permission changes.
Accuracy, escalation rate, and reversal rate reported monthly. An agent whose numbers deteriorate gets its authority lowered rather than defended.
Anybody can build something that produces plausible output. The question that matters is whether it is right, and answering it requires several hundred real cases with known correct answers — assembled before the agent exists, so the agent cannot be tuned to the test.
Building that set is genuinely tedious. It is also the only thing separating an agent you can grant authority to from one you have to check, and checking everything is the same cost as doing everything.
High volume, recurring, and judgement-based with a knowable correct answer. Reviewing purchase requests against a policy that has seventeen exceptions. Classifying claims by recovery likelihood. Checking submissions against a specification before they go out.
The pattern is a decision a competent person makes in under two minutes, several hundred times a month, using information the system already holds.
Decisions with no verifiable correct answer, because there is nothing to evaluate against. Decisions made twice a month, where the build cost never returns. And anything where being wrong is catastrophic and unrecoverable — those we decline, because bounded authority is not a sufficient control when the bound itself is the risk.
Our published benchmarks are the right expectation. Structured comparison work reaches the low nineties. Contextual classification lands in the eighties. Anything requiring information not present in the system — because it lives in someone’s head or in an email — plateaus in the sixties regardless of how good the agent is.
We will tell you which band yours falls into during scoping. A workflow whose ceiling is sixty-five percent may still be worth automating, but you should decide that knowing the number rather than discovering it.
Where to start
What the decision is, whether correct is knowable, and what accuracy band to expect. Free, and it sometimes concludes an agent is the wrong tool.
Several hundred historical cases with known outcomes, assembled with your team. Tedious, unavoidable, and the reason the rest can be trusted.
Agent built, scored against the set, iterated until it clears the threshold you agreed rather than until it looks convincing in a demo.
Deployed watching only. Authority raised when the shadow record justifies it, and lowered instantly if the numbers move.
Questions
We will tell you whether an agent fits and what accuracy to realistically expect.