What happens when your data tool expects more from you than you expect from it?
Traditional BI expects you to be a data engineer.
Standard business intelligence platforms force operators to navigate complex database schemas, write custom SQL joins, and write fragile calculations. When a formula fails or a query times out, decision-making grinds to a halt.
Instead of getting instant answers, operators spend their days resolving calculation error squiggles, debugging ambiguous table columns, and waiting on database response cycles.
IF [Segment] = "Enterprise" AND [Category] = "Technology" THEN
(SUM([Sales]) - SUM([CAC])) / SUM([Quantity]) * AVG([Margin %])
ELSEIF [Region] = "Americas" AND [Sub-Category] = "Phones" THEN
SUM([Sales]) * [LTV] / [Churn Rate]
ELSE
SUM([Profit]) / SUM([Sales]) * (1 - AVG([Discount]))
ENDSELECT c.country, r.region, SUM(o.sales) AS revenue,
(SUM(o.profit) - SUM(o.cac)) / COUNT(DISTINCT o.customer_id) AS net_ltv
FROM orders o JOIN customers c ON o.customer_id = c.id
GROUP BY c.country, r.region;Solving the high cognitive load for modern BI tools
Imagine a BI tool that never asks you to understand SQL or wrestle with data schemas, but prompts you to ask the right question. The system's job is to deliver the answer.
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Research was done by a human, insights were synthesized with AI. Design was crafted in Figma with AI as a thinking partner. The HTML was built using Antigravity and Figma.
