Asteri Analytics embeds an AI analytics layer into casino and sportsbook operators' existing data warehouses, letting anyone ask business questions in plain English within Slack and receive answers with caveats and context. The system encodes each operator's house rules, metric definitions, and data quirks into the AI, ensuring answers follow the same logic a senior analyst would apply. It connects directly to existing warehouses like BigQuery, Postgres, or Redshift, with no migration or new BI tool required.
Funding
Funding not disclosed
Founders
Product
Problem
Casino and sportsbook operators face a bottleneck between business questions and data answers, with analysts queuing requests that can take days to resolve. This delay forces decisions to be made on gut instinct rather than data, leaving the warehouse underutilized despite holding the answers.
Solution
Asteri Analytics provides an AI analytics layer that connects directly to a casino or sportsbook operator's existing data warehouse, enabling anyone to ask questions in plain English via Slack and receive answers in minutes. The AI writes and runs SQL against the warehouse, with the operator's business rules, metric definitions, exclusions, and known-bad columns encoded into the system so answers follow the same logic a senior analyst would apply. Each response includes its caveats—which table, window, and exclusions were used—and ambiguous questions are asked back rather than guessed at. The system plugs into existing infrastructure without migration or new BI tools, and can also clean up messy underlying data if needed.
Target Audience
Primary customers are casino and sportsbook operators—including CMOs, CRM leads, product teams, and sportsbook managers—who need fast, reliable answers from their data warehouse without waiting on analyst queues.
Features
- Slack-native interface where users ask questions in plain English and receive SQL-generated answers in minutes, with thread-based follow-ups that carry context
- Business rule encoding that captures metric definitions, player exclusions, VIP scoring, and known-bad columns so the AI follows house logic
- Warehouse-agnostic MCP-based connections to BigQuery, Postgres, Redshift, and other data platforms with no migration required
- Automatic caveat reporting on every answer, including table sources, time windows, and exclusions, with empty results flagged as probable wrong filters
- Ambiguity handling that asks clarifying questions about metrics or time windows rather than making assumptions
- Data quality remediation for messy underlying tables, with experience working on top of a thousand raw tables