Dot

Dot is an AI-powered data analyst built by Snowboard Software GmbH (d/b/a GetDot), designed to make data insights instantly accessible to every team member regardless of technical background. It connects to your existing data warehouse, BI tools, and communication platforms, then answers natural-language questions with SQL-backed charts, deep analysis reports, and scheduled PowerPoint presentations. The product is powered by OpenAI and Anthropic models and is SOC 2 Type II certified and GDPR compliant.

What It Is

Dot sits at the intersection of conversational AI and business intelligence. Instead of requiring analysts to field ad-hoc data requests or stakeholders to learn SQL, Dot accepts plain-English questions—typed directly in Slack or Microsoft Teams—and returns charts, tables, and narrative summaries within seconds. It covers four core capabilities: Chat (instant Q&A), Deep Analysis (multi-dimensional investigations with full methodology), Automated Reports (scheduled PowerPoint decks delivered on a calendar), and a Context Agent (which pulls metric definitions from Tableau, Snowflake, Confluence, dbt, and other sources to keep answers consistent and governed).

How the Workflow Works

A typical interaction starts with a team member mentioning @Dot in a Slack channel or Teams message. Dot identifies the relevant tables, writes and executes SQL against the connected data source, and returns a chart or table in the thread. For more complex requests, the Deep Analysis mode runs multi-step reasoning across multiple data dimensions and produces an executive-ready report. Automated Reports let teams schedule recurring PowerPoint decks—weekly revenue summaries, Q4 sales reviews—that are delivered to Slack channels or email inboxes without manual intervention. The Context Agent continuously syncs metric definitions and business logic from connected BI tools and documentation systems, resolving naming conflicts and enriching a shared data dictionary.

Integrations and Data Sources

Dot offers no-code connectors to a wide range of data infrastructure:

• Warehouses & databases: Snowflake, BigQuery, Redshift, PostgreSQL, MySQL, Microsoft SQL Server, Databricks SQL, AWS Athena, ClickHouse, Oracle Database, SAP HANA, Firebolt, MotherDuck/DuckDB, Microsoft Fabric, Supabase
• BI & semantic layers: Looker, Tableau, Power BI, Metabase, dbt, Cube, Steep
• Communication & delivery: Slack, Microsoft Teams, Email
• Other: GitHub, CSV/Excel, MCP

An API is also available for custom integrations.

Security and Governance Model

Dot is SOC 2 Type II certified with annual independent audits and is GDPR compliant with full Data Processing Agreement support. The platform enforces role-based access control (RBAC) and row-level security so different roles—Analyst, Executive, Viewer, Admin—see only the data they are permitted to access. SSO is supported via Okta, Azure AD, and Google. Zero data retention is applied on all LLM provider calls, meaning query data is not stored by OpenAI or Anthropic. Enterprise deployments support self-hosted options and full audit logging.

Benchmark Performance and Adoption Signals

The vendor publishes benchmark results on the DABStep dataset—450+ multi-step financial analysis tasks created by Adyen and HuggingFace—claiming 96% accuracy for Dot versus a 62% human baseline on easy tasks (with no human baseline available for hard tasks). The vendor also compares Dot against Google's Data Science Agent, which it claims scored approximately 52% on the same benchmark. The homepage states the product is trusted by 100+ teams and lists Duolingo, Airbyte, BlaBlaCar, Babbel, Choco, and Flix as customers. Published impact studies include vendor-attributed claims of 12,000+ hours saved at Duolingo, 18x ROI at Choco, 99% faster time-to-insight at Emerge, and €800K in identified savings at KRY.