Why we changed how Supaboard’s AI works
Why we rebuilt Supaboard’s AI around one conversation: a founder’s account of carrying context from an investigation into dashboards and recurring reports.
31 stories
Why we rebuilt Supaboard’s AI around one conversation: a founder’s account of carrying context from an investigation into dashboards and recurring reports.
Comparing Sisense alternatives in 2026? See how Supaboard, ThoughtSpot, Power BI, Looker, Metabase, Domo, and Qlik Sense stack up on setup, AI, and price.
An AI data analyst answers questions against governed definitions instead of raw schema. How it works, what it automates, and what it cannot.
A revenue cycle management company cut per-client analysis from four hours to two minutes, with every piece of patient data masked before it ever moved.
A founder's field guide to the semantic layer in 2026: what it is, why AI agents fail without one, and how dbt, Cube, AtScale, Looker, and Malloy compare.
Most AI BI demos are rehearsed against prepared data. Twelve questions that separate a working product from a well-practised sales presentation.
Text-to-SQL turns a question into a query. AI-native BI understands the business. Here's the real difference, with a worked NRR example and a 5-min test.
Which data visualization tool fits your team, how they price at scale, and the chart choices that decide whether anybody reads the result at all.
How teams with no analysts build a single source of truth, agree on shared metric definitions, and answer their own data questions without joining a queue.
Positive correlation means variables move together, negative means apart. How to read strength and direction, and how to avoid the dual-axis trap.
Spend analytics turns scattered procurement data into savings you can act on, and the use cases that reliably pay back before all the others do.
Static AI rules either over-restrict or over-expose. How contextual governance scores each request, and why fabricated insights fail silently.
ThoughtSpot pioneered search-style analytics at enterprise prices. How alternatives compare on setup cost, modelling and time to first answer.
Healthcare analytics is constrained first by what data may legally move. How masking, prediction and explainability fit together in practice.
Manufacturing analytics joins machine telemetry to commercial outcomes. Where predictive maintenance pays back, and where it quietly does not at all.
Retail KPIs work in four layers: sales, inventory, customer, margin. How to read them together so that a revenue drop always gets the right response.
Most database performance problems are query plans, not hardware. How to find them, what indexing really costs you, and when to scale up instead.
Traditional BI tells you what happened. Agentic analytics runs the investigation. How the two differ, and three demo tests that separate them.
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