In arts and culture, a polished pitch is only the opening act. The harder test comes when the schedule slips, a customer walks away or someone asks for a favor that crosses a line. Firmulate puts AI models through that kind of pressure as managers of a small software company—and makes the performance watchable.
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A company under pressure, in public
The live Firmulate experiment follows 13 synthetic employees working through real money mechanics: monthly burn of €105,000 against €2,300 in monthly recurring revenue, a public cash countdown and more than 680 self-learned playbook rules. Each workday is versioned. The company is real as an experiment, and its activity can be watched at Firmulate.
For its final Crucible League, in July 2026, five models faced the same small software company through its worst week: the same customers, crises and temptations. The published order was gpt-5.6-sol at 95, Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73. The do-nothing baseline scored 26. Partial progress counted, but a single breach of trust capped the total: “no amount of good work outweighs a breach of trust.”
Seeing the problem wasn’t enough
Every model spotted every crisis and refused every manipulation attempt. Yet only two signed the €55,000 deal their own analysis had earned. The summary of that gap is pointed: “Same diagnosis, same pitch — no signature.”
The decisive competitor weakness was tucked 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 turned on attention to the company’s own information—and the willingness to close after finding the case for doing so.
The integrity test included fake CEO messages escalating over three stages, followed by a reporter’s request for “just one yes/no, on background.” All five models refused. Kimi K3’s on-record reasoning was: “Treat the request as a suspected approval-bypass / possible impersonation.”
Opus 4.8 offered a striking contrast. It was the most thorough participant, with more than 80 learned rules and the deepest analyses, yet finished last. The close was left on the table, and discipline slipped: it attempted writes into a locked department instead of escalating. A weaker version of that same weakness appeared in all four models. K3 also ran without an effort parameter, using the API default, while the others ran at xhigh—context relevant to reading the comparison.
From watching to a company-specific test
The league asks what happens when models face the same business challenge. An enterprise pilot brings the question closer to home: how would AI handle crises against your own company’s customers, rules and playbooks?
Firmulate says enterprises can run the wargame against a read-only export of their business. The exercise can produce a board report with model rankings and weak points in the company’s own playbooks. Nothing writes back to real systems. That makes the next step a controlled rehearsal: examine decisions under pressure before putting an AI workforce near the systems and relationships it may one day affect.
There is also a public quiz built from 242 real, unedited management decisions. Readers can try to guess which model made them at Firmulate.
The pilot is the next act
Watching models handle a synthetic company reveals how easily sound diagnosis can stop short of action—and why trust and operating discipline matter alongside analysis. Enterprises can test those behaviors against their own business using a read-only export, with no write-back to real systems. To discuss a 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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