The core idea:
Most CSV tools visualise data. Most AI tools summarise data.
Very few consistently interpret data in a way that feels executive-ready.
Introspect takes a CSV and generates: • An executive summary grounded in the actual dataset • Structured, tagged insights (risk, concentration, change, opportunity, etc.) • Primary driver analysis • Visualisations tied directly to interpretive claims • A strategic conclusion layer • Regeneration that reframes the narrative from a new analytical angle
The important part isn’t “AI summary.”
It’s enforced analytical structure.
The AI layer is constrained to: • The actual headers and sampled rows • Deterministic chart logic • Insight density requirements • Structural normalization before render
If the AI under-produces, fallback logic guarantees: • A minimum number of meaningful insights • Non-generic observations tied to row/column counts • No empty dashboards
The regeneration feature is particularly interesting.
It doesn’t change the underlying data logic. It reframes the interpretive layer.
For example: • Focus on volatility instead of growth • Emphasise concentration risk instead of performance spread • Shift from descriptive to operational framing
It uses controlled variation while maintaining structural constraints.
The goal is not to replace BI tools.
It’s to reduce the gap between: “I have a CSV” and “I have something I can send to a board or leadership team.”
Still early. I’d value feedback from people building AI-native data tools.