Interactive demo
Catching a bad invoice is the easy half. Someone still has to pull the contract, check the price history, call up the receiving record and decide what to do. This agent does the digging and comes back with a recommendation and its reasoning — then waits for you to say yes.
This demo investigates fictional flagged invoices against fictional records, with scripted results, right in your browser. In a real deployment it's your contracts, your history, your systems.
Watch the step count. Each exception takes a different number of steps and a different set of tools, because the agent decides what to check next based on what it just found.
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Under the hood
The agent can read contracts, pull price history, query receiving records, search the check register and draft correspondence. Nobody wrote the order it uses them in. It picks the next one based on what the last one returned.
What it checked, why it checked it, and what came back — all visible. When it recommends a short-pay, you can see the contract clause it's relying on rather than taking the recommendation on faith.
When the records genuinely don't settle the question, the agent stops and hands you a specific question rather than inventing an answer. Nothing is paid, sent, or posted without a person clicking approve.
The invoice pipeline runs the same sequence every time — read, match against the PO and receiving, check for duplicates, flag. It's deterministic on purpose: auditable, cheap, and it fails predictably. That's the right design for money leaving the building.
This agent picks up where that stops. The path isn't fixed, because the question isn't fixed — a price variance sends it to contracts, a short shipment sends it to the carrier record, a suspected duplicate sends it to the check register. Pipeline catches, agent investigates, person approves.
Both are useful, and most operations want the pipeline first. An agent is worth building where the follow-up work is genuinely open-ended — which is exactly where a person is currently spending twenty minutes clicking between four systems.
If someone on your team spends their morning chasing answers across four systems, that's the shape of a problem worth automating. We'll tell you honestly whether it needs an agent or something simpler.