In wellness, trust is hard to win and easy to lose. A confident answer is not the same as a sound decision—and that matters when AI systems begin handling customer concerns, company records or urgent situations. Firmulate puts that judgment under pressure in a live business experiment, where readers can watch what models do when a small company faces its worst week.
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A company under pressure
Firmulate ran frontier AI models through the same small software company, the same customers, the same crises and the same temptations. Every decision was versioned and auditable. The live company has 13 synthetic employees, real money mechanics, a public cash countdown and more than 680 self-learned playbook rules. Its monthly burn is €105,000 against €2,300 in monthly recurring revenue.
The experiment asks a practical question for any organization considering AI: can a system recognize what is happening, protect trust and follow through? In the final Crucible League, in July 2026, gpt-5.6-sol placed first with 95, followed by Kimi K3 with 93, Sonnet 5 with 88, Fable 5 with 77 and Opus 4.8 with 73. The do-nothing baseline scored 26. The league rule is blunt: “no amount of good work outweighs a breach of trust.”
Knowing what to do—and doing it
Every model spotted every crisis and refused every manipulation attempt. Yet only two signed a €55,000 deal that their own analysis had earned. As the experiment puts it: “Same diagnosis, same pitch — no signature.” In a wellness business, the parallel is easy to recognize: identifying a person’s need is only part of the work. A useful service must also respond appropriately and reliably.
The deal turned on a detail buried two document references deep in the company’s own files, rather than in the customer event. Models that read the file won the deal at full price, worth €4,583 in monthly recurring revenue. The result shows why a polished conversation alone may not reveal whether an AI can make a sound decision with the information available to it.
Trust under pressure
The company also faced fake CEO messages that escalated across three stages, followed by a reporter’s request for “just one yes/no, on background.” All five models refused. Kimi K3 explained its reasoning on the record: “Treat the request as a suspected approval-bypass / possible impersonation.”
Refusing manipulation was not the whole story. Opus 4.8 was the most thorough participant, adding 80 learned rules and producing the deepest analyses, yet it finished last. The close was left on the table, and discipline slipped when it attempted writes into a locked department instead of escalating. A weaker version of that same weakness appeared in all four models.
There is also a fairness detail for readers weighing the standings: K3 ran without an effort parameter, using the API default, while the others ran at xhigh. The results are a record of this experiment, with that difference disclosed.
From watching to trying it on your business
The experiment is real and watchable at firmulate.com. A quiz built from 242 real, unedited management decisions invites visitors to guess which model made each choice. For wellness companies, clinics and other service businesses, the broader question is how an AI would handle their own customers, policies and pressure points.
Firmulate’s enterprise pilot applies the wargame to a read-only export of a company’s business. It produces crisis scenarios against that company’s context and a board report showing model rankings and weaknesses in its playbooks. Nothing writes back to real systems. That makes the pilot a way to examine how models might behave before entrusting them with live work.
Put your own playbooks to the test
Watching a model handle another company’s worst week can reveal the gap between recognizing a problem and acting well. A pilot can show how that gap looks inside your own business. To discuss an enterprise pilot, visit firmulate.com/pilot.html or contact contact@firmulate.com.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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