We spent 7 years building a gym. Then 107 days building 9 AI agents to run it.
The output was not what we expected.
The product is not data. It is the engine that produces verifiable data. That engine has a name: MAFBE.
What is MAFBE?
MAFBE (Multi-Agent Fitness Behavior Engine) is not a dashboard. Not a SaaS. Not "AI for gyms."
It is 9 autonomous agents, one physical store, 107 consecutive days. Every action recorded, every error traced, every recovery logged.
The output: behavior data that is provably real — because the engine that produced it is auditable end-to-end.
The Industry vs. MAFBE
The industry sells "AI-powered fitness." What does that mean? Usually a smartwatch step count + a generic workout plan = "AI."
MAFBE does not guess. It operates. 9 agents managing member data, IoT sensors, content calendars, infrastructure health — in a physical gym that has been running since 2015.
The difference between a demo and 107 days of real operations is the only gap that matters.
What MAFBE Produces
- Every data point has a provable production chain — agent to action to timestamp to hash
- Every error has a permanent prevention rule — not a human memo, a constitution amendment
- Every recovery is structural, not manual — 6/7 critical bugs auto-resolved in 34 days
This is not AI that helps you work out. This is AI that runs a business.
The Business Model
Closest analogy: Palantir for physical retail.
Not because of the technology. Because of the business model. Palantir charges for the protocol that makes data usable and verifiable. MAFBE charges for the engine that makes behavior data provably real.
Protocol fee, not data sale. Infrastructure, not software. Verification layer, not analytics tool.
Who is MAFBE For?
- Fitness brands that want to own their member data instead of giving it to platform giants
- Investors who understand that verifiable behavior data is a new asset class — not a feature, not a dashboard
- Builders who know that 9 agents running a real business for 107 days is a stronger signal than any whitepaper
Two links to understand MAFBE:
- The engine: github.com/ZWISERFIT/retroonto — 11 production constraints governing all 9 agents
- The proof: dev.to/zwiserfit/7-infrastructure-bugs-our-ai-agents-auto-recovered-in-34-days-the-full-breakdown-2eo5 — 7 bugs, 34 days, zero human intervention
This is not a theory. This is a running system.
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