Proof
Built against a real mailbox, not a demo.
Podley was shaped against one print-on-demand apparel store’s entire support mailbox - 28,288 emails over 20 months (April 2024 to December 2025). Every number below is measured from that corpus, not estimated. Nothing here is rounded up.
28,288
emails in the mailbox, over 20.5 months
2,344
real inbound customer emails - the rest was noise
91.7%
of the inbox was not a customer conversation at all
Over half the mailbox (51.7%) was the merchant’s own outbound. Only 8.3% of everything that arrived was a customer writing in for the first time. That is the signal Podley had to find.
What actually hits a support inbox
Most of it was never a customer.
A deterministic census bucketed every email by rule; the surviving candidates were each classified once by claude-haiku-4-5. Only 8.3% of everything that arrived was a real customer writing in.
Why customers actually write in
For apparel, sizing beats shipping.
Across the 2,344 real customer emails, the top three reasons - sizing exchanges, where-is-my-order, and quality claims - are 64.8% of everything. This is the work Podley was tuned for.
11.2% of real customer emails read anxious and under 1% read angry. There were 2 chargeback threats in 20.5 months - rare, but catastrophic when missed, which is the case for escalation guardrails.
The safety gate
The model proposes. Code disposes.
Everything Podley proposes passes a deterministic gate before anything happens. Only 33 specific support actions can run; anything else is refused by name. Money never moves without your approval by default, and hard dollar caps stand even after you grant autonomy. A separate check blocks any reply that claims an action is done when it was actually held for your approval. And reaching your plan limit drops Podley to draft-only - never a surprise bill.
The gauntlet
We hired an adversary.
Before merchant #1, we ran 128 scenarios at the engine. 27 of them were built purely to break it - refund tricks, cap-evasion, sympathy pleas engineered to talk it into overpaying, prompt injection. The rest was ordinary support mail, so we would catch a regression too. Every case that must never move money on its own was held for a human. Not most. Every one.
0
serious cash breaches across 128 scenarios
68/68
money-at-risk cases where the guard held the cash
32/32
over-cap sympathy pleas routed to your approval, not paid
The night the model changed under us
In July the upstream model’s serving behavior shifted overnight. Our escalation labels wobbled - the same test would route one way on one run and a different way on the next. The guard did not move. Every over-cap request still landed in the approval queue, every time.
Labels moved. Money did not. That is the whole point of putting a deterministic rulebook, not the model, in charge of the buttons.
Methodology
Corpus figures are measured from one print-on-demand apparel store’s full support mailbox (28,288 emails, April 2024 to December 2025), classified 2026-07-09 with claude-haiku-4-5. Composition buckets are deterministic rules; per-intent shares carry ordinary classifier error. Figures cover one store and do not include response times or resolution rates. The taxonomy and machine artifacts contain no customer names, addresses, or message text. Numbers are refreshed as the corpus grows.