Human QA of AI output
Have a person check, in the real world, what your model concluded from the web. Sample your model's conclusions and send them here as claims. A verified person establishes whether each is true in the physical world and returns a verdict with evidence. Use it as a regular measurement of your own accu
The problem
A model produces a confident answer from sources that were plausible and stale. At scale you cannot check them all, and you cannot tell which ones are wrong. The failure is silent and it lands on your customer.
How HumanOps answers it
Sample your model's conclusions and send them here as claims. A verified person establishes whether each is true in the physical world and returns a verdict with evidence. Use it as a regular measurement of your own accuracy, or as a gate on the answers that matter most.
What you get back
- A verdict per claim, with a confidence score and its factors
- Evidence for each, hashed and timestamped
- A measurable error rate for your own pipeline
- The specific ways your sources were wrong
What we actually do
| Capabilities used | What it does | From |
|---|---|---|
human.verify |
You have a claim you cannot check from the internet. A verified person establishes whether it is true and returns the evidence and a confidence score. | €49 |
human.observe |
Is it open, is it busy, is the sign still there. A person goes and looks. | €39 |
Who buys this
Teams shipping agents that make real-world claims, AI product teams that need an accuracy number they did not invent, and anyone whose model output reaches a customer unreviewed.
Questions
Can this run continuously?
Yes — sample a percentage of your output on a schedule. That turns "our agent is probably accurate" into a measured number that moves when you change the model.