Cubegle
Data • AI • Cloud
Case Study

AutoInsights: AI-Driven Insight Layer for Dashboards

Turning dashboards into proactive assistants — AutoInsights detects anomalies, highlights trends, and generates narrative summaries for business users.

Overview

Dashboards are powerful but often passive: users must interpret visuals themselves. AutoInsights aims to surface key changes and explanations automatically.

Problem

  • Stakeholders missing critical trends hidden in dashboards.
  • Analysts spending time writing commentary manually.
  • No consistent way of sharing insights across teams.
AutoInsights Architecture
Snapshot

Type: Internal product / reusable accelerator
Scope: AI + analytics layer
Outcome: Proactive, narrative insights on top of dashboards

🤖 AI-Powered ⚡ Real-time

Solution

Cubegle built AutoInsights as a pluggable AI layer that connects to curated datasets and dashboards, runs ML/statistical checks, and generates human-friendly insight text.

Key Capabilities

  • Anomaly detection and threshold-based alerts.
  • Trend analysis over time (e.g., week-over-week, month-over-month).
  • Segment comparisons (e.g., top vs bottom regions/customers).
  • Short narrative summaries attached to dashboards.

Architecture (High Level)

AutoInsights connects to semantic models or curated tables, runs scheduled or on-demand insight jobs, and writes outputs to a table or API that dashboards and apps can consume.

Tech Stack

Python · ML/Statistical Libraries
BI Semantic Models · Cloud Functions
Optional: LLMs for NLG

Impact

  • Stakeholders receive "what changed" summaries without deep analysis.
  • Analysts focus on decisions rather than manual commentary.
  • Consistent, repeatable insight generation across dashboards.
Where This Applies

Ideal for organisations with mature dashboards who want an AI layer that surfaces insights automatically on a regular cadence.