A two-person team in São Paulo competing head-to-head with a hundred-person team in San Francisco.

Not because the AI is perfect. Because human oversight is surgical.

A five-person startup deploying agents that handle 85% of cases independently—and surfaces the 15% that needs judgment before customers ever see a problem. A small legal practice offering the same analytical depth as a white-shoe firm, with reviewers focused on what genuinely matters. A regional bank providing personalized service that matches the giants, without armies of humans babysitting every decision.

Intelligence as infrastructure. Human oversight that scales. The playing field, leveled.

This is the promise. AI agents that handle most cases independently. Humans who oversee edge cases and critical decisions efficiently. Small teams that punch far above their weight. Companies that scale to millions of customers without scaling the reviewers required to supervise them.

The technology exists. The models are powerful enough. The APIs are accessible.

But something is blocking it.

Here's what we see when companies deploy AI.

A team ships an AI product. Users love it. Growth accelerates. The agents handle most cases well. Then the cracks appear. A hallucination slips through. Customers escalate. Engineers drop roadmap work to investigate. The CEO says: "Add human review for anything high-stakes."

Now every decision gets reviewed. The oversight team drowns in false positives. 90% of what they're reviewing was fine. But they can't tell which 10% actually needed attention until after they've reviewed everything.

The team tries to hire their way out. More reviewers. More QA staff. More engineers on escalation duty. It doesn't work. Headcount scales linearly. Cases compound exponentially. The math never balances.

This is the wall. Almost everyone hits it.

And here's the cruel part: only companies with massive teams can afford to push through. They can hire the armies of reviewers required to supervise at scale. The startups can't. The small firms can't. The underdogs betting everything on AI can't.

The technology that should democratize capability instead concentrates it. High-autonomy agents require unscalable human oversight—which means only the giants can deploy them.

The promise is broken.

The industry treats oversight as binary: review everything or review nothing.

We've looked closer. Most agent decisions don't need human judgment. But today's systems can't tell you which ones do until after something breaks.

The failures that reach customers aren't random. They're systematic. Silent input problems—schema drift, stale context, encoding mismatches—poison agent decisions without triggering errors. The AI isn't broken. It's doing exactly what it was told with inputs nobody realized had changed.

Systematic problems have systematic solutions. We capture the full context of every agent decision, version it, and surface the patterns that predict failure before they reach customers. Human reviewers focus on the 15% that genuinely needs attention. The other 85% flows through cleanly.

This is what scalable oversight looks like. Not reviewing everything. Not reviewing nothing. Surgical intervention on what matters.

The result: more customers, same oversight team. That's leverage. That's what breaks down the barrier.

Every company scales AI-powered products without scaling support headcount.

For customers, we measure: customers onboarded per support FTE. When that ratio improves, we've delivered.

For us, we measure: engineering hours reclaimed from escalations. Did we give them time back? That's how we earn our place.

The companies building the future shouldn't be trapped maintaining it. The engineers capable of changing industries shouldn't spend their days triaging failures. The reviewers doing critical oversight work shouldn't drown in false positives.

The startups and small firms betting on AI deserve the same shot as the giants. High-autonomy agents with efficient human oversight shouldn't require massive teams.

The promise of AI—intelligence that scales, capability without endless headcount—should actually be true.

We're here to make it true.