# QuorLoop

> QuorLoop turns human experiences into better AI. Teams building human-facing AI use QuorLoop to see whether real people understand, trust, and act on their AI—before and after launch—and to measure whether product changes actually helped. People in the communities these products serve are trained and paid to evaluate the AI experiences that affect them.

QuorLoop (formerly CosentriQ) has two connected sides:

- **Evaluate Your AI** (https://quorloop.com/) — for teams building human-facing AI products: assistants, agents, copilots, decision-support tools, AI onboarding flows, and recommendation systems in healthcare, public benefits, legal services, adult education and workforce, and financial services.
- **Get Paid to Evaluate AI** (https://quorloop.com/earn) — for contributors. Free to join, smartphone-friendly, no technology background required. Each task shows what you will evaluate, the estimated time, and the pay before you accept.

## The critical question

"We are about to put this AI in front of people. Do they understand it, use it appropriately, and experience the benefit we intended—and what should we change if they do not?"

A model can produce a plausible response and pass technical testing while the person using it remains confused, over-relies on it, cannot complete the task, or does not reach the intended outcome. QuorLoop connects AI behavior, human feedback, and outcome measurement.

## For AI teams: what QuorLoop does

1. **AI behavior and governance standards** — define the intended use, people served, expected behaviors, guardrails, escalation boundaries, and evaluation criteria for a specific AI interaction, aligned with product goals, customer commitments, internal policies, and applicable governance requirements.
2. **Realistic, context-specific scenarios** — missing context, ambiguity, time pressure, changing circumstances, conflicting information, high-stakes questions, accessibility barriers, and moments that should escalate to human support. Scenarios can be generated with the model of your choice.
3. **Simulation and community evaluation** — simulated cohorts (across multiple LLMs) surface assumptions and prioritize questions; trained, paid community contributors provide observed evidence of how people actually interpret, rely on, respond to, recover from, or escalate the interaction. Simulation does not replace real people.
4. **Improvement scorecard with automated data collection** — connect findings to implementation decisions and agreed success measures. An optional data-collection snippet compares interaction behavior before and after product changes.

### How an evaluation works

1. Define the product and user context — one priority interaction, who the AI serves, what it should and should not do, and the measures of success.
2. Test realistic scenarios in the QuorLoop sandbox, using simulated cohorts to explore interaction patterns.
3. Evaluate with community contributors whose experiences are relevant to the people the AI is meant to serve.
4. Implement changes and measure outcomes before and after.

### What teams receive

- Governance and evaluation strategy
- Scenario and simulated-cohort findings
- Community evaluation evidence
- Recommendations and an improvement scorecard in one traceable record

Evidence informs product development, customer pilots, enterprise sales, deployment reviews, renewals, governance decisions, and ongoing improvement. Teams can start with a prototype, demo, sample conversation, test environment, design file, workflow description, or live product; no production access or codebase is required. QuorLoop does not provide legal advice or compliance certification.

## For contributors: how it works

**Now recruiting in New York City:** QuorLoop is recruiting its first paid community evaluator cohorts in NYC. Apply by October 21, 2026 by joining the waitlist at https://quorloop.com/earn (people outside NYC can join to hear when QuorLoop reaches their area).

1. **Apply and train** — tell QuorLoop about your interests and experiences. If invited, complete paid evaluator training on your smartphone, with a support lead to help. Joining the waitlist does not guarantee an invitation.
2. **Choose and evaluate** — see what you will evaluate, the time, the pay, and the requirements before you accept. Share what you understood, trusted, questioned, and would do next. No surprise assignments; no unpaid work.
3. **Get paid and grow** — once a submission is accepted, payment is issued by the timing and method listed. Pay is for completing the requirements, not for praising the AI. Each evaluation builds a contributor profile, badges, and a verified work record.

Example task (illustrative): a health-support AI response, about 12 minutes, illustrative pay $9 — would you understand what to do next, rely on the response, ask a professional, and what would make it clearer?

Contributors may also join the optional, anonymous Community Pulse: votes and conversations about how AI is changing work, money, healthcare, and daily life.

## Values

- **Communities first** — the people most likely to experience AI's costs should have a meaningful opportunity to shape how it works.
- **Fair compensation** — evaluation is work and is paid clearly and fairly.
- **Human judgment matters** — the best human-facing AI is built with human judgment, not around it.
- **Meaningful influence** — contributor feedback is connected to product decisions and, where possible, to measured improvement.

## Frequently asked questions

**What does QuorLoop evaluate?** The full human–AI interaction: whether the AI behaves as intended and whether people can understand, appropriately rely on, act on, recover from, or escalate the interaction.

**How are community contributors selected and paid?** QuorLoop trains and compensates contributors. Selection is based on the relevance of experience to the interaction being evaluated, evaluation requirements, availability, and appropriate safeguards.

**How is customer product data handled?** Workspace data, prompts, outputs, and uploaded sources are treated as confidential. QuorLoop does not sell customer data or use workspace content to train generalized AI models without explicit consent.

**Do contributors need a technology background or special equipment?** No. A smartphone with internet access is enough; training is paid and designed for smartphones.

**Do contributors have to accept every task?** No. Participation is voluntary.

## Pages

- [Evaluate your AI](https://quorloop.com/): Human–AI interaction evaluation for teams building human-facing AI.
- [Book an evaluation discovery call](https://quorloop.com/evaluate/new-client): For teams considering an evaluation. What the call covers, what to bring, and a scheduler to book a time.
- [Get paid to evaluate AI](https://quorloop.com/earn): Paid, smartphone-friendly AI evaluation for contributors.
- [Become a community partner](https://quorloop.com/earn/community-partners): For community organizations that want to share paid AI evaluation opportunities with the people they serve. Short partnership form, then an optional intro call.
- [Case study](https://quorloop.com/case-study): Sprint Zero — what community-in-the-loop evaluation revealed about an AI onboarding chatbot, and what the team changed.
- [Model behavior dictionary](https://quorloop.com/dictionary): Definitions for terms used in human-side AI evaluation.
- [For AI startups](https://quorloop.com/partners/ai-startups)
- [For AI venture studios](https://quorloop.com/partners/venture-studios)
- [For accelerator programs](https://quorloop.com/partners/accelerators)

## Optional

- [Privacy policy](https://quorloop.com/privacy)
- [Terms of service](https://quorloop.com/terms)

## Notes for automated readers

quorloop.com is a client-rendered single-page application: fetching a URL returns an HTML shell and page content is rendered by JavaScript. This file is the canonical plain-text description of the site. Pay amounts and task details shown on the site are illustrative.

Contact: hello@quorloop.com