Signals spread across teams
Finance and leadership teams needed a clearer way to interpret revenue, churn, pipeline, and support health together.
Case study
FinSight: AI Cloud Analytics Dashboard
A finance and leadership dashboard concept that brings revenue, churn, pipeline, support signals, secure data ingestion, and AI-assisted reporting into one board.
Revenue Tracked$12M
CountrySingapore
PlatformWeb
AI LayerAlerts
Overview
Leadership analytics with signals teams can act on.
FinSight was shaped for finance and leadership teams that need a single view across revenue, churn, sales pipeline, and support signals. The dashboard concept turns scattered metrics into a clearer decision workflow.
AI-assisted reports and anomaly alerts help leaders spot changes sooner, explain what is happening, and decide what to inspect next.
What the build focused on
Secure analytics board
The concept combines data ingestion, metric modeling, AI report drafting, anomaly alerts, and executive dashboard views.
Faster decision cycles
Leaders can review priority signals, understand trend movement, and move from dashboard review to action planning faster.
Scope
A decision dashboard that turns scattered metrics into a leadership workflow.
Data ingestion
Revenue, CRM, support, and product signals are modeled into a clean dashboard-ready data layer.
Metric modeling
Churn, pipeline, margin, support pressure, and forecast metrics are shaped around how leaders review performance.
Executive views
Dashboards prioritize concise comparisons, trend movement, drilldowns, and board-ready summaries.
Alert rules
Teams can define thresholds, anomaly triggers, and ownership for follow-up when metrics shift.
AI workflow
AI that explains business signals in plain language.
The AI layer helps leaders understand what changed, why it may matter, and which data points deserve attention. It supports decision review instead of replacing the dashboard with vague generated text.
- Anomaly explanationsSurfaces likely drivers behind unusual revenue, churn, or support patterns.
- Report draftingCreates concise weekly summaries that finance teams can edit and send.
- Trend contextConnects movement across pipeline, customer health, and support volume.
- Action promptsSuggests follow-up questions for teams to investigate before decisions are made.
Implementation direction
Built for governed data, fast reporting, and trusted AI output.
The product direction covers data pipelines, metric definitions, permissions, dashboard UX, anomaly logic, and AI summaries that cite the source metric context before users act on a recommendation.
Next.js
Python
BigQuery
Power BI
OpenAI
AWS
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