Halley's AI Guide · Glossary

Conversational Reporting Agent.

Definition — A Conversational Reporting Agent turns natural-language questions into real-time charts, KPIs, and insights — no SQL, no BI dashboards, just chat.

Also called: AI data analyst, chat-based analytics, NLQ (natural-language query) agent

In short Ask your data

Type "Top 5 events by paid attendance" and get the chart back — no analyst queue, no spreadsheet export.

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How It Works

From plain English to a chart.

A Conversational Reporting Agent connects to your data warehouse or cleaned CSVs and, using LLM-powered parsing, turns a question into an answer.

The flow

  • Detects metrics, entities, and time frames in plain English.
  • Generates optimized SQL or API calls on the fly.
  • Returns answers as tables or auto-rendered visuals (bar, line, pie, heat-map).
  • Caches frequent queries and learns preferred visual styles over time.

Key Features

  • Natural-language querying: "Top 5 events by paid attendance" in one message.
  • Adaptive visualization: picks the best chart type automatically.
  • Live data sync: queries your latest tables or warehouse views in real time.
  • Predictive insights: suggests follow-up questions (e.g., YoY trends).
  • Role-based access: obeys row- and column-level security you define.

Why It Matters

Teams waste hours exporting CSVs and tweaking pivot tables. A Conversational Reporting Agent closes that gap — non-technical users get instant answers, analysts focus on deeper work, and leadership sees fresh numbers anytime. Expect faster decisions, higher data adoption, and lower BI licensing spend.

Best-fit use case: Give business teams a governed way to ask questions like "Top 10 events by paid attendance in 2024" without waiting on a custom dashboard or spreadsheet export.

Ready to unlock self-serve analytics?

See a Conversational Reporting Agent answer questions on your own data.

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