> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cosentriq.com/llms.txt
> Use this file to discover all available pages before exploring further.
# CosentriQ: Product Decision Intelligence for AI-Enabled Products
### CosentriQ helps AI-enabled product teams make better product decisions by combining AI agentic reasoning with structured human signal.
CosentriQ is a Product Decision Intelligence platform for AI-enabled product teams.
We help teams understand what is working, what is breaking, and what to do next after a product has been built, shipped, piloted, or prepared for pilot.
CosentriQ combines **AI agentic reasoning** with **structured human evaluation** to turn messy product, model, market, and human signals into clear decisions about what to refine, validate, rebuild, pivot, or scale.
We do not evaluate AI models in isolation. We evaluate AI-enabled products as products — how the model behaves, how humans experience it, whether the workflow makes sense, whether users trust the output, and whether the product is strong enough to support the decision it was built to improve.
## The Problem CosentriQ Was Built to Solve
AI product teams do not fail only because they lack data.
They fail because the signals they rely on are often incomplete, disconnected, or hard to act on.
The symptoms are familiar: product analytics show where users drop off, model evals show how outputs perform in controlled settings, customer calls reveal partial feedback, and internal testing catches obvious issues. But even with all of that, teams still struggle to answer the questions that determine whether an AI-enabled product will actually work in the market:
* Do users understand what the product does?
* Do they trust the AI output enough to rely on it?
* Does the product fit into the workflow it was built for?
* Is adoption stalling because of the model, the UX, the positioning, or the use case?
* Are the right humans being asked to evaluate the product?
* What should the team refine, validate, rebuild, pivot, double down on, or scale next?
Surface metrics show what happened. Model evals show how a system performed under controlled conditions. Customer conversations reveal pieces of the story.
But AI-enabled product teams need a way to connect those signals into a decision they can act on.
CosentriQ was built for that gap.
## The Core Thesis
**AI-enabled products should not be judged only by model outputs or performance metrics. They should be judged by whether humans understand, trust, use, and rely on them in real conditions.**
CosentriQ operationalizes that thesis through a closed product decision loop.
The platform helps teams diagnose the decision, design the right evaluation, collect structured human signal, synthesize the evidence, and decide what to do next.
## The CosentriQ Loop
CosentriQ works across five steps:
| Step | What Happens |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------- |
| **Diagnose** | The platform identifies the product decision the team needs to make and the signals currently available. |
| **Evaluate** | CosentriQ designs a structured human evaluation based on the product, use case, workflow, and uncertainty. |
| **Collect Signal** | Matched contributors evaluate the product, AI outputs, workflow, messaging, or decision scenario. |
| **Synthesize** | AI Agentic reasoning combines product context, available metrics, model behavior, and human signal into a structured report. |
| **Decide** | The team receives a decision-ready recommendation on what to refine, validate, rebuild, pivot, or scale. |
The output is not a generic summary.
It is a product decision report grounded in available evidence, structured reasoning, and real human signal.
CosentriQ is built for AI-enabled products that have something real to evaluate: an MVP, prototype, product surface, demo, AI workflow, sample output, pilot, internal tool, or live product. If there is nothing real to test, the team is likely too early for CosentriQ.
## What CosentriQ Evaluates
CosentriQ evaluates the AI-enabled product as a whole.
That includes the product experience, the AI output, the user or buyer’s trust, and the decision the product is supposed to support.
| Dimension | What CosentriQ Looks At |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------- |
| **Product Experience** | Onboarding, UX friction, comprehension, positioning, workflow fit, and adoption risk. |
| **AI Output Quality** | Whether outputs are useful, understandable, reliable enough for the use case, actionable, and aligned with the product promise. |
| **Human Trust** | Whether users or buyers trust the product and AI output enough to rely on it. |
| **Workflow Fit** | Whether the product fits how people actually work, decide, evaluate, or complete the task. |
| **Industry Language** | Whether the product speaks in terms the target buyer, user, or domain audience understands and trusts. |
| **Decision Usefulness** | Whether the product helps the user make a better decision or complete a meaningful task. |
| **Next Product Move** | Whether the team should refine, validate, rebuild, pivot, or scale. |
CosentriQ does not replace product analytics, model evals, customer interviews, or internal research.
Those tools are inputs.
CosentriQ connects the inputs, fills critical gaps with structured human evaluation, and turns the evidence into a product decision.
## How CosentriQ Gets Signal
CosentriQ does not claim to know everything automatically.
The platform works by structuring multiple signal sources:
| Signal Source | What It Provides |
| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| **Founder / Team Intake** | Product goals, target users, current uncertainty, business model, product stage, and decision context. |
| **Product Artifacts** | Product links, screenshots, demos, onboarding flows, AI outputs, dashboards, reports, sales materials, and pilot materials. |
| **Available Metrics** | Activation, conversion, completion, repeat usage, churn, support tickets, usage logs, model evals, or other data the team provides. |
| **Structured Human Evaluation** | Human feedback on comprehension, trust, usefulness, friction, language fit, workflow fit, and reliance risk. |
| **Domain-Matched Contributors** | Feedback from people with relevant lived experience, workflow familiarity, industry exposure, or buyer/user context. |
| **AI Agentic Synthesis** | Structured reasoning that interprets the available evidence and recommends the next product decision. |
Every decision report makes source confidence clear.
When a signal is strong, CosentriQ names it.
When a signal is limited, CosentriQ names that too.
## How CosentriQ Produces Recommendations
Every CosentriQ recommendation is structured around a decision taxonomy.
The system does not produce generic insights. It produces a recommendation tied to what the product team should do next.
| Verdict | Meaning |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Refine** | The direction is sound, but the product experience, language, workflow, or AI output needs sharpening. |
| **Validate** | The signal is promising but insufficient. More evidence is needed before committing. |
| **Rebuild** | The foundation is not working. A deeper product, workflow, or system change is required. |
| **Pivot** | The current direction is not supported by the evidence. The team should shift segment, use case, positioning, or product approach. |
| **Double Down** | The current direction is showing strong enough signal to increase focus, resources, or execution — but not necessarily broad enough evidence to fully scale yet. |
| **Scale** | Confidence is high enough to expand usage, distribution, or investment. |
This taxonomy gives teams a shared language for action.
The verdict is not meant to replace human judgment. It gives the team a structured decision frame grounded in evidence, human signal, and source confidence.
## Who CosentriQ Is For
CosentriQ is built for **AI-enabled product teams with something real to evaluate**.
This includes teams that are:
* Preparing for a pilot and need to know whether the product is clear, credible, and useful enough to test
* Running an active pilot and trying to understand what is working or breaking
* Post-launch and unsure why adoption, trust, or retention is stalling
* Embedding AI into an existing product and unsure whether users understand or trust the feature
* Building B2B AI products and needing to test workflow fit, buyer comprehension, and industry language
* Evaluating whether AI outputs are useful, understandable, actionable, and reliable enough for the use case
* Deciding whether to refine, validate, rebuild, pivot, or scale
CosentriQ is especially useful when a team has multiple signals but no clear decision.
## Who CosentriQ Is Not For
CosentriQ is not designed for teams that only have an idea and no product surface, prototype, workflow, demo, AI output, or pilot material to evaluate.
If a team only needs idea validation, they are likely too early.
If a team has built something and needs to understand what is working, what is breaking, and what to do next, they are in the right place.
## Where to Start
If you are exploring market direction, start with CoDomain.
If you have a built, piloting, or in-market AI-enabled product and need to decide what to do next, start with CosentriQ.
View market spend, adoption trends, and field sentiment across AI product categories.