Metrics, anomalies, and answers — powered by AI.

Founders and operators track metrics across Stripe dashboards, spreadsheets, internal databases, and third-party analytics tools. Each source tells part of the story, but assembling the full picture requires manual data pulls and custom calculations that break every time the business model evolves.

The real cost isn't the tooling — it's the time. Hours spent compiling board updates, investor reports, and executive summaries from fragmented data. By the time the report is ready, the numbers are already stale. Anomalies surface in retrospect, not in real time.

Natural language querying of business data remains a promise most BI tools haven't delivered on. Teams still write SQL or navigate complex filter UIs to answer basic questions about their own metrics.

This reference dashboard demonstrates what a modern metrics platform looks like when AI is native, not bolted on. MRR waterfalls, cohort retention, churn analysis, and pipeline tracking — all computed in real time from connected data sources.

The AI layer does three things: detects anomalies proactively (flagging unusual churn spikes, MRR drops, or conversion changes before they compound), answers natural language questions about the data, and generates executive-ready reports with narrative summaries.

The architecture is domain-agnostic by design. The data adapter pattern means the same platform can serve a SaaS company tracking MRR, a marketplace tracking GMV, or a services business tracking utilization — swap the adapters, keep the intelligence layer.