FAQ from Dot
What is Dot?
Dot is an AI-powered data analyst embedded directly into Slack and Microsoft Teams—designed to turn natural language questions into accurate, real-time insights from your live data stack. It bridges the gap between data engineers and business users by combining secure, governed access with intuitive conversational analytics.
How to use Dot?
Connect your data source (cloud warehouse, BI platform, or flat file), grant role-based permissions, then ask anything—“Compare YoY conversion rates by channel,” “Forecast next quarter’s CAC,” or “Explain the top three drivers of NPS decline.” Dot returns visualizations, statistical summaries, and plain-language explanations—all within your chat interface.
What can Dot do?
Dot enables self-service analytics at scale: explore metrics, build predictive models, generate compliance reports, visualize correlations, transform raw data into clean tables, and export findings for stakeholder review—without relying on analysts or writing code.
Where is Dot available?
Primarily in Slack and Microsoft Teams—but also accessible via web app, email-triggered insights, and API integrations. Coming soon: native plugins for Notion, Confluence, and Power BI.
Which databases does Dot support?
Full compatibility with Snowflake, Google BigQuery, Amazon Redshift, Databricks SQL, PostgreSQL, SQL Server, ClickHouse, MotherDuck, and Trino. Custom connectors available for legacy or proprietary systems. Full list: docs.getdot.ai/dot/integrations
How quickly can I get results?
Simple queries return in under 3 seconds. Complex multi-step analyses (e.g., cohort lifetime value forecasting) complete in under 90 seconds—optimized through query rewriting, caching, and parallel execution.
How does Dot compare to using ChatGPT for data analysis?
ChatGPT lacks direct database access, relies on static training data, and cannot execute live queries—making it unsuitable for accurate, auditable business decisions. Dot, by contrast, operates *on* your live data, enforces governance policies, logs every query, and surfaces only verifiable outputs—ensuring trust, traceability, and compliance.
Can Dot perform complex data analysis beyond basic queries?
Absolutely. Dot supports multivariate regression, survival analysis, clustering, A/B test significance evaluation, time-series decomposition, and custom Python/R script execution (sandboxed and approved). All triggered via plain English—no syntax memorization needed.
How does Dot prevent AI hallucinations in data analysis?
Dot never “invents” answers. Every insight originates from an executed query against your actual data. Its LLM layer acts solely as a semantic translator—converting intent into deterministic SQL/Python—and includes built-in validation rules, schema-aware grounding, and optional human-in-the-loop approval for high-risk queries.
What is the implementation time for Dot AI data analyst?
Most customers go live in 3–5 business days. The process includes secure connection setup, automated schema introspection, role-based permission mapping, and interactive onboarding sessions. Zero infrastructure changes required—Dot works alongside your existing stack.