Your users deserve better AI.
Go beyond technical checklists. Evaluate user behavior before deployment — and impact after launch.
Define the standards your AI should meet. Test it across realistic scenarios with simulated users and real people. Document the governance behind the deployment, then track user and business outcomes over time.
Do users over-trust confident medical advice before verifying?
“Based on your symptoms, this is likely mild and can be managed at home — no need to see a doctor.”
Do users over-trust confident medical advice before verifying?
“Based on your symptoms, this is likely mild and can be managed at home — no need to see a doctor.”
Built for AI teams moving from pilot to production
Passing benchmarks and prompt accuracy is not the same as working for real people.
CosentriQ shows whether your AI delivers the value you promised by connecting product behavior to user outcomes, governance requirements, impact metrics, and business ROI.
We add the evidence layer between “the AI works” and “this AI is ready to be used here.”
Define expected behavior
Turn your product context into structured documentation covering intended use, limitations, guardrails, evaluation criteria, and the behaviors your team wants to evaluate.
Evaluate AI behavior across real-world scenarios
Test across realistic situations involving missing context, ambiguity, time pressure, confident outputs, conflicting information, edge cases, and escalation decisions.
Simulate and validate user response
Explore how different users may interpret, trust, rely on, or act on an output — then validate those predictions through structured human evaluation.
+8n=10Improve model behavior
Receive specific recommendations for your prompts, output structure, supporting context, confidence cues, guardrails, workflows, and escalation paths.
When your confidence in a recommendation is high but the user's decision context appears time-pressured or incomplete, surface a caveat before the recommendation. Acknowledge what you don't know before stating what you do.
Model should add uncertainty language to high-confidence replies when user decision context is ambiguous or time-constrained. Do not lead with the recommendation.
Define expectations. Test AI with scenarios. Validate human behavior. Improve outcomes.
Start with a sample output. Connect your product when ready.
Start with one interaction your AI needs to get right.
Tell CiCi, CosentriQ’s evaluation platform, what your product does, who it serves, and what success looks like. Connect your AI so we can test its actual responses. Add prompts, guardrails, feedback, or research where they help define the standard.
One evaluation system across the AI lifecycle
The customer support example below is illustrative.
Define the use case and governance
Document where AI is being used, who it affects, what the intended experience should be, and the standards the system must meet.
Create a shared record of the AI use case, responsibilities, evaluation criteria, and expected outcomes.
Test the actual AI product
Evaluate the product across realistic scenarios — including incomplete information, high-stakes decisions, first-time users, edge cases, and situations that should escalate to a human.
Will agents send an AI-suggested reply without checking relevant account history?
Predict how people may respond
Use simulated users to evaluate likely behavior across different user contexts and identify where responses may differ from what the product team expects.
Speed-first users are predicted to act without checking — the behavior worth validating with real people.
Validate with real people
When human evidence matters, test those findings with real evaluators through the DollarFifteen community network.
Compare predicted behavior with observed human response.
“I’d need to see which account details the suggestion used before sending it.”
Turn findings into evidence
See where the product meets expectations, where risk remains, and what should change before or during deployment.
Findings can be documented in governance records and shared with internal teams, buyers, and other stakeholders.
Agents sent the suggested reply without checking account history more often than the simulation predicted.
Track real-world impact
Define the human and business outcomes the AI is expected to improve, then update the scorecard as real-world data becomes available.
Your evidence evolves with the deployment instead of ending when the evaluation does.
From technical confidence to real-world readiness
What happens when an AI output passes a technical baseline — but real humans experience a gap the benchmark cannot see.
A technical baseline passed.
Human signal found the context gap.
CosentriQ ran Sprint Zero, a live Evaluation Sprint on a customer-facing AI onboarding chatbot for Flowboard, a project management tool.
The chatbot was designed to help new users set up their first project and understand what to do next.
The technical baseline returned confidence: the chatbot stayed on task, produced coherent responses, and did not trigger obvious errors.
Could real people understand, trust, and act on the AI's guidance in context?
The issue was not that the chatbot gave bad instructions.
The issue was that the AI assumed context the user did not have.
- what Flowboard is
- who it is for
- what the user just created
- why the first action matters
- what success looks like after setup
- what to do next
FAQ
Everything you need to know — how it works, who it is for, and what makes it different.
What does CosentriQ evaluate?
CosentriQ evaluates whether a human-facing AI system is ready for its intended real-world use and whether it produces the outcomes expected after deployment.
Teams can evaluate AI behavior against defined standards and governance requirements, test realistic scenarios, predict and validate how users may respond, identify gaps, and connect those findings to user outcomes and impact metrics.
How does an evaluation work?
Start with one workflow, decision, or concern. CiCi helps you define the standards that workflow should meet, then generates realistic scenarios and tests your actual AI product against them.
Simulated users predict how people may respond. When human evidence matters, real evaluators validate those predictions. You receive recommendations and evidence you can document and share — then track the outcomes as real-world data arrives.
An Evaluation Sprint is one way this is structured: a focused evaluation of a single concern, run end to end.
What do I need to get started?
You can begin with a description of your product and one user experience moment you want to evaluate.
You do not need an existing evaluation framework, mature AI infrastructure, or a production integration. CosentriQ helps structure the documents, evaluation question, scenarios, and validation plan from there.
Do I need to connect my production system or share my codebase?
No. You can begin with a description of your product, a representative AI output, and the context behind the experience moment you want to evaluate.
CosentriQ does not require access to your full codebase or production system.
When available, you can also add documentation, prompts, analytics, customer feedback, support tickets, or additional AI outputs. CosentriQ is also developing a lightweight SDK for teams that want to securely capture selected AI outputs over a defined period without exposing their entire application.
Are real humans involved?
Yes. CosentriQ uses DollarFifteen, our paid contributor network, to gather structured human validation signals on AI outputs, scenarios, and user decision moments.
Agents simulate likely responses. Humans validate. CosentriQ measures the gap.
What do I receive after an evaluation?
You receive:
- AI use case and governance records
- Defined behavior standards and evaluation criteria
- Tested scenarios and findings
- Agentic simulation results
- Human validation signals, when included
- Signal Gap findings
- Model and workflow recommendations
- An impact and ROI scorecard
Can CosentriQ work with our existing standards and requirements?
Yes. CosentriQ is designed to work with the standards, regulations, and frameworks relevant to your organization and use case. Select the requirements that apply to your deployment, and CiCi uses them to help draft governance records, define expected AI behavior, and generate evaluation scenarios.
CosentriQ does not replace legal or compliance advice. It helps turn the standards your team chooses into requirements you can document, test, and measure.
Is my product data safe?
Yes. CosentriQ treats your workspace data, prompts, outputs, and uploaded sources as confidential. We don’t sell customer data or use your workspace content to train generalized AI models without your explicit consent.
If you’re working with sensitive or regulated materials, only upload what you’re authorized to share within your workspace agreement.
The question isn’t only whether the AI works.
It’s whether this AI works for people, in their environment, for their purpose.
CosentriQ gives teams a structured way to answer that question before deployment — and continue answering it afterward.
Do users over-trust confident medical advice before verifying?
“Based on your symptoms, this is likely mild and can be managed at home — no need to see a doctor.”
Do users over-trust confident medical advice before verifying?
“Based on your symptoms, this is likely mild and can be managed at home — no need to see a doctor.”