AskMyData for BigQuery
A Slack- and Teams-based natural-language analyst that lets marketing, sales, and ops staff query their company's BigQuery warehouse in plain English and get trustworthy charts back.
marketing and ops managers at mid-sized companies who know their data lives in BigQuery but can't write SQL
- Plain-English questions like 'what was our signup rate by channel last month?' return a chart plus the SQL it generated for auditing
- Saved queries with scheduled Slack delivery so weekly metrics go out without anyone touching a dashboard
- Guardrails that block expensive queries and route sensitive tables through an approval workflow
- Onboarding wizard that maps the warehouse's tables to business terms so non-technical staff don't need to know the schema
The agent-skill ecosystem around BigQuery has matured enough that wrapping it for non-engineer business users is the obvious next move, and the BigQuery user base keeps growing inside companies whose actual decision-makers can't write SQL.
Multiple Medium/GitHub write-ups and enterprise solutions confirm growing interest in Slack-native NL-to-SQL for BigQuery; BigQuery base is expanding and decision-makers still lack SQL fluency.From Plain English to BigQuery: Architecting an AI-Powered Analytics Assistant (SQL+Slack) ↗How I Built an AI Data Assistant in Slack ↗
Crowded: Hex ships an Agent in Slack today, ThoughtSpot Spotter/Sage targets the exact persona, plus BlazeSQL, AI2SQL, Scoute_AI, hex-analytics-bot, and dozens of Medium/LinkedIn DIY builds.Hex & Slack Integration | Slack Marketplace ↗ThoughtSpot Agentic Analytics Platform ↗Top 10 AI Database Query Assistants Tools in 2026 ↗
Proven willingness to pay: ThoughtSpot lists $25/user/mo (Essentials) up to custom Enterprise tiers; Hex charges for Team/Enterprise with AI agents included — mid-market BQ shops already budget for self-serve analytics.ThoughtSpot Plans and Pricing ↗Hex Pricing: Plans for Every Data Team ↗
NL data access is a durable need, but the wrapper layer is being commoditized by foundation models and absorbed into incumbents (Hex, ThoughtSpot, Google's own Gemini in BigQuery), eroding standalone durability.Text to SQL Comparison 2026: Enterprise Solutions Evaluated ↗
Buildable with LangChain/Slack SDK + BQ client in weeks, but hard parts are schema grounding, hallucinated SQL, row-level security, and trust — all of which incumbents have already solved.Scout: AI-Powered Slack Assistant (Proof of Concept) ↗Building a Natural Language Data Interface with LLM Agents: Breaking the SQL Barrier ↗