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Querri

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(12)4.0/5

Querri is your whole data stack in one platform: storage, preparation, analysis, and dashboards. Business teams ask questions in plain English and get answers grounded in their own data, with no SQL to write, no warehouse to provision, and no separate semantic layer to license. Querri manages every layer rather than sitting on top of one. A managed data lake and warehouse are built in, so there's nothing to provision. Cleaning, joins, dedupe, and type casting run as versioned, re-runnable work. Metrics, entities, and relationships are defined once, so a definition means the same thing in a chat answer, a dashboard, and an export. Connectors pull from databases, warehouses, CRMs, SaaS apps, files, and APIs on a schedule, including HubSpot, Salesforce, NetSuite, Google BigQuery, Amazon Redshift, PostgreSQL, MySQL, and Microsoft SQL Server. Querri can read from APIs where no native connector exists. The data library and semantic layer build themselves. Querri's agents connect your sources, clean and model the data, and define what your metrics mean, so the semantic layer is shaped by the people who know the business instead of waiting on an engineering project. It sharpens with every question your team asks. Results are inspectable. Every question generates and saves the underlying Python code, so an analysis can be refreshed, reused, and scheduled rather than rebuilt. You can read a step-by-step explanation of any answer, see the generated code, and check which calculations were applied. Dashboards stay connected to the analysis underneath, meaning any dashboard can become the starting point for a new question rather than just a fixed report. Governance is enforced everywhere. Querri is certified and audited against SOC 2, ISO 27001, and HIPAA. Access policies apply at the row and column level to every answer, including AI queries, so the assistant only ever sees exactly what the person asking it can see. Zero rows of customer data are ever used to train models, and your data, models, and business logic stay yours. Querri also works inside the AI-tools teams already use. A built-in MCP server brings governed Querri data into Claude, ChatGPT, or Gemini without moving data or the need to rebuild the analysis. For product teams and enterprises, Querri can be white-labeled and embedded inside your own application, with your branding, your context, and your access controls. Querri fits best for: - Business, finance, operations, and marketing teams - Organizations without a large data engineering function - Teams whose data lives across several disconnected systems - Buyers replacing a warehouse, a BI tool, and a semantic layer with one platform - Anyone who needs analyses to be transparent, governed, and repeatable

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