Works today
One agent run or output
Send it through the order form. We reply with one price, the expert's price plus a 25% fee, and a US expert checks, fixes or finishes it.
Send one runAI teams × US experts
Human experts behind reliable agents.
Today you can send a single agent run or output for a US expert to check, fix or finish, priced before you pay. Evaluation programs and the API are on the roadmap.
No card to send a request. Card payment opens only after we set the price and an expert accepts.
What works today and what does not
By brief
Evaluation programs
Recurring review, rubrics or failure analysis. Send a detailed brief and we reply by email.
Send a briefNot live yet
API access
There is no public API today. The developer page shows what is planned; nothing there can be called yet.
See the planWhy now
Models improved faster than proof systems.
A benchmark score cannot tell you whether one customer’s spreadsheet, browser workflow, or client deck is actually right. Production reliability needs task-level verification.
Production validation
Route risky or sampled agent runs to humans who check the result against task-specific criteria.
Failure operations
Triage stalled runs, resolve ambiguous cases, and return reproducible failure evidence to product teams.
Evaluation programs
Author realistic tasks, ground truth, rubrics, and weighted partial scoring for long-horizon workflows.
Consented learning loops
With the right permissions, structure recurring failure modes, repairs, and verifier decisions into better evals.
Built from the evaluation loop
Realistic task. Ground truth. Rubric. Expert judgment. Failure replay.
That operating system already powers serious agent evaluation. FindYourHuman would apply the same loop to each production outcome, then make the human work discoverable, measurable, and scalable.
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Imperfect or conflicting inputs
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Explicit asks + professional conventions
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Objective checks + weighted partial scoring
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Visual, structural, and source validity review
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Failures converted into new eval cases
Public research example: Halluminate
Human expertise is not the fallback. It is part of the system.
Public work from Halluminate illustrates the point: reliable agents depend on realistic environments, domain experts, ground truth, rubrics, and careful failure analysis. The work is won after the model call too.
Explore Halluminate’s public researchHalluminate is a public industry example. No customer relationship, affiliation, or endorsement is implied.
Governed data loop
The moat is consented failure data, not private customer work.
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Private: used only to complete the task
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Aggregated: anonymous operational metrics
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Deidentified: approved eval or training examples
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Named partnership: explicit separate agreement
Build your route
Design the first workflow your agent cannot get wrong.
Scope one agent run with a written total.
Seeking founding design partners