Querri
AI data analyst that connects, cleans, models, and analyzes business data through plain-English questions.
About Querri
Querri combines ingestion, storage, data preparation, a semantic layer, dashboards, and conversational analysis in one platform. Teams connect production databases, CRM systems, spreadsheets, warehouses, SaaS applications, files, and APIs, then ask questions in plain English against cleaned, modeled data and shared business definitions. Querri can turn a conversation into an insight, a live dashboard, or a presentation, and saved Skills make recurring analytical workflows repeatable across teams. Its built-in MCP also brings governed data into Claude, ChatGPT, Gemini, or a CLI. The platform supports row- and column-level access controls and states that it is compliant with SOC 2, ISO 27001, and HIPAA, while customer data is not used to train its models. Querri is suited to organizations that want a managed data stack and self-service analysis without assembling separate warehouse, semantic-layer, BI, and chat tools. It can also be embedded and white-labeled inside another product. Fixed public prices are not listed; access is offered through a start-for-free flow and a sales conversation.
Best for
Best use cases
A team connects scattered operational sources, cleans and models them, then answers recurring KPI questions in plain English.
A leadership meeting needs a current dashboard and presentation created from the same governed business definitions.
A product company adds white-label analytics inside its application without building a separate data platform from scratch.
Compare before choosing
Compared with a chatbot pointed at raw tables, Querri grounds answers in cleaned data, a semantic layer, and business context.
Compared with assembling separate warehouse, transformation, semantic, BI, and chat tools, Querri manages those layers together.
Compared with fixed-price self-serve analytics tools, Querri requires a sales conversation to establish commercial terms.
Features
Customer segmentation
Group customers by behavior and lifetime value so sales, marketing, and customer-success teams can act from the same analysis.
Revenue forecasting
Analyze revenue, pipeline coverage, and quota attainment to support planning for the coming quarter.
Embedded analytics
Add a branded analytics experience inside a product while keeping data models, business logic, and access controls together.
Pros
- Combines ingestion, storage, preparation, semantic definitions, dashboards, and AI chat in one managed workflow.
- Plain-English questions let business teams create insights, dashboards, and presentations without writing SQL.
- Row- and column-level permissions, stated SOC 2, ISO 27001, and HIPAA compliance support governed organizational use.
Cons
- Commercial terms are not published as fixed prices, so cost comparison requires contact with the sales team.
- Adopting the full managed stack is a larger workflow commitment than adding a lightweight chat layer to an existing setup.
- Teams must connect data sources and establish business definitions before governed answers can reflect their operating context.
Pricing Plans
Querri
- Managed ingestion, storage, transformation, and semantic layer
- Plain-English analysis, dashboards, presentations, and reusable Skills
- MCP access from Claude, ChatGPT, Gemini, and the CLI
* Pricing information is for reference only. Please refer to the latest announcement on Querri's official website.