Until now.
Developers got
Stack Overflow in 2008.
Then Copilot.
Then Claude Code.
Physical workers got
nothing.
Learn from a million decisions before your first real one.
Voice in, decision out, 20 seconds flat.
Scenario → Video → Quiz → Cert → Career.
Remove any flywheel and the model breaks. All three are structurally necessary.
Voice in. Decision out. 20 seconds. Every time.
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.
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.
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.
Talk to Us →Gemini integration active. Production facility. Real workers. Real decisions. Real payouts.
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. That's it — you're ready to start earning.
Just your phone. OkAI is a voice-first app — you talk to it the way you'd talk to a coworker. Use your regular earbuds if it's noisy on the floor. OkAI is built for loud environments — factory floors, kitchens, warehouses.
OkAI works in English and Spanish, with more languages coming. Just speak naturally in whatever language you're comfortable with — no settings to change, no buttons to press.
You inherit the decision stack — over 1,000,000 real decisions made by workers like you, from workplaces like yours. Think of it like having a mentor with decades of experience in your ear from minute one.
The decision stack is a library of decisions from experienced workers — what they did, why, and what happened. On your first day, all of that knowledge is available to you through OkAI. You don't start from zero.
Each day, OkAI gives you a few quick challenges based on real situations from your workplace. You answer by voice. OkAI tells you what experienced workers did and what the outcome was. You earn $1–3 per completed scenario — starting from Day 1. Build a streak and your multiplier grows (up to 2.5x by day 90).
When you're uncertain about something on the job — a quality issue, a safety call, whether to escalate — open OkAI and speak for 20 seconds. Describe what you see. OkAI responds in under 20 seconds with a recommendation. You decide what to do. Every decision generates a verified decision trace, and that's where the real earnings start.
Override it. That's the whole point. When you reject OkAI's suggestion and make a different call, that override is the most valuable signal in the system. You're never penalized for overriding. In fact, overrides with good outcomes generate the highest-value traces.
As you build skills, OkAI offers short certification paths: a scenario, a video, a quiz, then a cert. Each certification gives you a permanent bonus multiplier on your future earnings. They also show up on your profile — proof of what you know, portable to any job.
During training (days 1–90): $100–300/month from daily practice scenarios. In production (days 91+): $400–600/month total, including verified decision traces. All on top of your regular wages.
Your earnings accumulate in the app. We use Venmo and Cashpay for cashout. You can cashout 24x7 when you have a minimum of $5 in your account. You can't lose money you've already earned.
No. There are no penalties. During training, "wrong" answers are learning opportunities. In production, every decision has value because it captures real human judgment. Your employer doesn't see a score. OkAI is here to help you get better, not to grade you.
You own your decision traces. They're anonymized — your name and identity are stripped before anything leaves your phone. Your employer sees operational improvements, not a report card on you individually. You get paid for the value your expertise creates. No surveillance, no extraction, full transparency.
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. That's the deal.
Structured decision traces and corresponding evals from physical work environments. Each trace captures: what the AI suggested, what the worker actually did, whether they accepted or overrode the suggestion, and the measured real-world outcome.
This maps directly to DPO training pairs and reward model inputs. Unlike synthetic benchmarks, every trace originates from a high-stakes physical environment where the decision had immediate, observable consequences.
Standard RLHF: A desk-based annotator reads two outputs and clicks a preference button. The stakes are zero. The signal is binary: A > B.
OkAI traces: A frontline worker gets an AI suggestion about a quality anomaly. They override it because they noticed something the AI missed. Twenty minutes later, the outcome is measured. The trace contains: context, suggestion, override, rationale, and verified outcome. That's a fundamentally richer training signal.
Current RLHF data comes almost entirely from desk-based knowledge workers. OkAI's traces come from multilingual, non-desk workers making high-stakes decisions with immediate physical consequences. This broadens the alignment surface in ways lab settings can't replicate. If you're training a model for physical-world tasks, you need physical-world preference data. We're the only source.
Each deployed worker generates approximately 50 decision traces per shift. At full-scale across food manufacturing alone: tens of millions of verified traces annually. Cross-vertical expansion multiplies this further.
Pilot: An initial batch of anonymized gold-tier traces. Evaluate format compatibility and signal quality. No commitment.
Subscription: Ongoing delivery, filtered by domain, language, decision type. Volume discounts.
Co-development: Joint work on trace format standards optimized for your pipeline.
Per-trace pricing for gold-tier data. Revenue split: 40% to the worker, 40% to the enterprise, 20% to OkAI. This three-party alignment is why the data exists and why quality stays high.
OkAI reduces errors, accelerates onboarding, and improves compliance — because workers actually want to use it. Enterprises see up to 30% fewer escalations, faster time-to-productivity for new hires, and structured knowledge capture from experienced workers before they leave.
Traditional training happens before the job. OkAI works during the job — real-time guidance when it matters. And because workers earn from their participation, adoption is pull-based, not push-based. You don't have to mandate it; workers choose it.
Enterprises pay a per-worker monthly subscription for the copilot. You also share in revenue when your workers' anonymized decision traces are licensed to AI labs — creating a new revenue stream from operational data you're currently not capturing.
We start with a design partnership — working with your team to configure OkAI for your specific workflows, safety protocols, and decision points. Deployment is lightweight: workers use voice-enabled devices they're already familiar with. No infrastructure overhaul required.
Worker consent is baked into the product. All traces are anonymized before any external sharing. We're building to EU AI Act compliance standards, and our data architecture separates operational data (yours) from training data (anonymized, worker-consented). No information asymmetry.