Until now.
Every decision. Captured automatically.
She speaks for 20 seconds. She decides — with OkAI.
She earns money for every decision.
Remove any flywheel and the model breaks. All three are structurally necessary.
Voice in. Decision out. 20 seconds. Every time.
Months to minutes.
OkAI ships with 1,000+ decision scenarios per occupation. Pre-built from O*NET's 1,016 occupation database.
Stack Overflow launched empty. OkAI ships full.
Income increase with OkAI at 90 days.
OkAI puts $450 to $625 a month — roughly $5,400 to $7,500 a year — into a frontline worker's pocket for doing what she was already doing, now just slightly better documented.
The loop that can't be replicated.
Every loop: worker paid. Decision logged. Model sharpened. Corpus grows. This data can't be scraped or simulated.
The core pitch: OkAI is generating the kind of data AI needs but can't easily get — verified human decision-making in high-stakes physical environments, structured as preference signals that map directly to RLHF and DPO training pipelines.
This is not a concept.
OkAI is live at TLCH Foods.
OkAI is a voice-first AI copilot for physical and frontline workers. It helps workers make better decisions on the job — in real time, hands-free, in noisy environments — while they earn money for the expertise they contribute.
Most AI tools are built for people at desks with keyboards. OkAI is built for people on their feet — in kitchens, on factory floors, at job sites. It's voice-first because physical workers can't stop to type.
Unlike enterprise software that tracks workers for management, OkAI is designed for the worker. They own their data and earn from it.
Download the OkAI app from the App Store or Google Play. Open it, type your phone number, and you're in. No email, no password, no complicated setup.
Just your phone. OkAI is voice-first — you talk to it the way you'd talk to a coworker. Use earbuds if it's noisy. Built for loud environments.
English and Spanish, with more coming. Speak naturally — no settings to change.
You inherit the decision stack — over 1,000,000 real decisions made by workers like you. Think of it like having a mentor with decades of experience in your ear from minute one.
Training (days 1–90): $100–300/month. Production (days 91+): $400–600/month total. All on top of your regular wages.
Venmo and Cashpay. Cashout 24x7 with a $5 minimum. You can't lose money you've already earned.
No. OkAI doesn't track your location, record you without your knowledge, or report your performance to your boss. It only activates when you speak to it. The data belongs to you. The money goes to you.
Structured decision traces from physical work environments. Each trace captures: what the AI suggested, what the worker did, whether they overrode the suggestion, and the measured real-world outcome. Maps directly to DPO training pairs and reward model inputs.
Standard RLHF: A desk-based annotator clicks a preference button. Zero stakes. Binary signal.
OkAI traces: A frontline worker overrides an AI suggestion because they noticed something the AI missed. Twenty minutes later, the outcome is measured. Context, suggestion, override, rationale, and verified outcome. Fundamentally richer training signal.
~50 decision traces per worker per shift. At full scale across food manufacturing alone: tens of millions of verified traces annually.
Per-trace pricing for gold-tier data. Revenue split: 40% worker, 40% enterprise, 20% OkAI. Three-party alignment is why the data exists and quality stays high.
Reduces errors, accelerates onboarding, improves compliance — because workers actually want to use it. Up to 30% fewer escalations, faster time-to-productivity, structured knowledge capture.
Traditional training happens before the job. OkAI works during the job. Workers earn from participation, so adoption is pull-based, not push-based.
Per-worker monthly subscription. You also share in revenue when anonymized decision traces are licensed to AI labs — a new revenue stream from operational data you're currently not capturing.
Worker consent is baked in. All traces anonymized before external sharing. Building to EU AI Act compliance. Data architecture separates operational data (yours) from training data (anonymized, worker-consented).