"What made revenues spike last week?"
ClarityQ answers open-ended questions across any data, like where product meets finance, without compromising context or accuracy. It catalogs billions of tables, metrics, and events into a unified contextual layer in days, then delivers answers with visuals, insights, and a clear action plan.
Go from messy tables to meaningful discovery in just days. Ask any question, drill into any metric, and pivot between dimensions to generate in-depth analyses and build a comprehensive understanding of root causes. Turn a single conversation into a validated set of insights that your team can align and act on, fast.
ClarityQ’s agent is built for complex analytics, autonomously managing the entire journey: planning, reasoning, and validating every answer to ensure every insight and visualization is reliable and grounded in your specific data.
ClarityQ automatically structures your data semantics, updates them, and flags gaps - building on your existing BI definitions and business logic. It applies guardrails to ensure every answer is grounded in your specific context, and stops for clarification when needed rather than hallucinating or guessing - delivering transparent, well-grounded results you can trust, always.

Schedule any question to run on its own - daily, weekly, or monthly. Set it once, and ClarityQ delivers fresh results automatically via email, Slack, or the dedicated Results tab. Create an automated task straight from the chat or from the Automations tab, so insights keep flowing even when you're not in the room.
Context-Driven data streamlining & mapping
Our proprietary GenAI-based tech performs entity resolution, context mapping, and schema change analysis to create a comprehensive Event Catalog enriched with useful, automatically-created metadata.
Company-specific AI model
Using company-specific customizations to the model, including example-based learning, schema linking, and automatic dataset creation ensures an enhanced, tailor-made model performance.
Accuracy as a key to success
For robust quality assurance of its accuracy, ClarityQ employs a range of advanced, academically-supported mechanisms for ensuring the accuracy of its outputs, incorporating various verification and validation techniques as well as ensemble methods for quality assurance.
Connect your data
Use a secure, guided wizard to connect ClarityQ to your data warehouse using a service account or your preferred auth method, and optionally connect BI tools and semantic layers
Read-only access to your warehouse
Additional integrations are optional and help enrich definitions and accelerate Context Layer readiness
Review and approve your Context Layer
ClarityQ generates business definitions, metrics, and contextual catalogs - you review, adjust, and approve what becomes your source of truth.
Start using ClarityQ
Start asking questions and get answers - make data-driven decisions faster than ever.
Faster, more confident decision-making - Product managers, analytics, growth teams, and executives can move faster with self-serve data answers and insights.
4x analyst productivity - Analysts use ClarityQ to accelerate SQL generation, automate repetitive queries, and free time for deeper analysis and strategy.
Fewer ad-hoc data requests - ClarityQ handles daily data questions across product, growth, and business metrics, reducing the load on data teams and helping them focus on high-impact work.
Organization-wide data access - Makes data accessible to everyone, not just technical users, while preserving accuracy and trust.
Just like when you communicate with a human analyst, ClarityQ is built to handle ambiguity. It interprets your intent, asks for clarification if needed, and flags errors such as unknown event names, while suggesting fixes based on your data. You don’t need perfect phrasing to get to the right answer.
How It Works: The "Translator" in the Middle
Think of the ClarityQ agent as a highly skilled translator combined with a data analysis expert.
When someone asks a question like, "How are our best customers doing?", the agent does not guess what "best" means. Instead, it follows a 3-step process to make sure the answer is accurate:
1. Checking your hidden logic
The agent first looks at your company’s Context Layer, created during onboarding. Think of it as a digital dictionary for your business. It contains the logic, definitions, and internal terminology your team uses - including terms like "Best Customer" (for example, a customer who spent more than $500 this year).
2. The think-first step
Next, the agent determines exactly which tables, metrics, and relationships it needs to answer the question correctly. If there is ambiguity - for example, two possible ways to calculate a metric - it pauses and asks for clarification instead of making assumptions.
3. The self-correction loop
Before presenting an answer, the agent runs the calculation in the background and checks the result. If the query returns an error or the data looks inconsistent, it revises the logic and tries again until the output meets a high quality standard.
The technical explanation
For teams managing the pipeline, ClarityQ operates as an agentic reasoning engine built on top of an accurate Semantic Layer, not just a raw metadata scrape.
The engine uses a Multi-Step Reasoning Loop (MSRL) to break natural language questions into a series of intermediate relational abstractions before generating the final SQL dialect.
By grounding its reasoning in the Semantic Catalog, the agent can recursively handle errors by interpreting database feedback and execution plans to resolve issues such as join-path ambiguities or schema mismatches.
This means the final output is not a probabilistic guess from an LLM. It is a deterministic result, validated against your specific data constraints.
Read more about how we built a reliable AI agent:
https://www.clarityq.ai/blog/how-we-built-a-reliable-ai-agent
Yes. ClarityQ suggests relevant questions based on your role, data context, and recent activity so you don’t have to start from scratch. Its autonomous AI agent uses context understanding to surface smart, real-time suggestions across areas like engagement, feature adoption, revenue, and more.
ClarityQ acts like a built-in analyst, helping you ask better questions and uncover what matters most.