Revenue Intelligence Dashboard

Status: Candidate Project
Market Size: $274–315B (SaaS market) growing at 15–22% CAGR; Revenue intelligence is sub-segment
Last Updated: 2026-05-02

Overview

An open-source, AI-native revenue intelligence platform for SaaS companies that combines subscription metrics dashboarding with AI-powered anomaly explanation and scenario forecasting. Today's tools split into:

Subscription metrics platforms (ChartMogul, Baremetrics): Great dashboards but limited forecasting
Enterprise revenue forecasting (Clari, Gong): AI forecasting but $100–$200/user/month and CRM-centric

This project bridges the gap with:

Real-time MRR/ARR/NRR dashboards feeding from Stripe, Chargebee, Recurly in a single unified view
AI-powered anomaly explanation: Automatically correlate revenue movements with product usage signals, support ticket volume, or pricing changes—turning a number into a diagnosis
Natural-language revenue querying: "Which customer segments drove expansion MRR this quarter vs. last year?"—without SQL or data team
Predictive expansion/churn scoring: Account-level probability scores with feature attribution for CS teams
Monte Carlo scenario forecasting: Transparent uncertainty bands and assumption controls for finance teams

The Market Gap

The revenue intelligence market is underserved at the SMB/mid-market level:

Free/cheap ($0–$500/mo): ChartMogul, Baremetrics, ProfitWell—excellent dashboards but no AI
Mid-market/enterprise ($1K–$200K+/yr): Clari, Gong—AI forecasting but expensive, sales-motion-centric, complex
Predictive analytics ($5K–$50K/mo): Pecan AI—strong churn models but no subscription metrics, batch-only, requires data warehouse engineering

The gap: No open-source tool provides production-ready MRR/ARR dashboarding + AI anomaly explanation + scenario forecasting in one place. Teams want:

Free or low-cost entry (eliminates $100K+/yr contracts)
Zero data engineering (direct Stripe/Chargebee integration)
AI-powered insights, not just static dashboards
Self-hosting for financial data sovereignty

Core Features

MVP (Must-Have)

Connect to at least Stripe (optionally Recurly, Chargebee) to ingest subscription events and calculate MRR, ARR, churn, NRR, LTV, and ARPA in real time
MRR waterfall visualisation (new, expansion, contraction, churn, reactivation) with time-range filtering
Cohort retention analysis with configurable grouping (plan, signup month, geography)
Customer-level drilldown showing account MRR history, plan changes, and payment status
Alerting for anomalous metric movements (threshold-based, delivered via Slack/email)
Dashboard export (PNG, CSV) suitable for board and investor reporting

Should-Have (v1.1)

AI-powered anomaly explanation correlating MRR movements with contextual signals (product usage, support volume)
Natural-language query interface allowing users to ask ad-hoc questions about subscription data without SQL
Account-level churn and expansion probability score using ML trained on cohort patterns
Automated ARR bridge narrative generation for CFO / finance team reporting
Multi-billing-source ingestion (Stripe + Recurly + Chargebee unified view)

Nice-to-Have (Backlog)

Monte Carlo / Bayesian scenario forecasting with transparent uncertainty bands and assumption controls
Native dunning / failed-payment recovery workflows
Industry benchmarking against an anonymised peer dataset
Embedded CRM lightweight task management for CS and RevOps users
MCP server endpoint exposing live revenue signals to external AI agents

AI-Native Opportunities

Automated anomaly detection in revenue movements
- Existing tools surface MRR waterfalls but don't explain why contraction or churn spikes
- An AI-native tool could correlate revenue anomalies with product usage signals, support ticket volume, pricing change dates, or macro seasonality—turning a number into a diagnosis
- Example: "Contraction MRR dropped 15% last week because support ticket volume doubled and product logins fell 40% vs. historical average"
Natural-language revenue querying
- Most dashboards are fixed-metric grids; business users cannot ask ad-hoc questions without data team support
- An LLM-powered query layer over structured subscription data eliminates this bottleneck
- Example: "Which customer segments drove expansion MRR this quarter vs. last year?" → instant query, chart, and narrative
Predictive expansion and churn scoring at account level
- Rule-based health scores (usage thresholds) are brittle and noisy
- A model trained on historical expansion/contraction patterns per cohort, product tier, and sales motion could produce calibrated probability scores with feature attribution
- No current SMB/mid-market tool provides this natively
Automated narrative reporting
- CFOs and boards receive manual slide decks summarizing ARR movements
- An AI layer could draft accurate, data-grounded revenue narratives (ARR bridge, cohort commentary, forecast variance) directly from underlying data
- Reduces analyst hours significantly
Scenario forecasting with explainability
- Current forecasting tools (e.g., Clari) are black-box ML or simple extrapolation
- An open-source AI-native tool could offer Monte Carlo or Bayesian forecasting with transparent uncertainty bands
- Empowers finance teams to stress-test assumptions rather than trust opaque model outputs

Competitive Landscape

Tool	Type	Real-Time MRR	AI Forecasting	NL Query	Account Scoring	Cost
ChartMogul	Commercial	✓	❌	❌	❌	Free–$599/mo
Baremetrics	Commercial	✓	❌	❌	❌	$108–$500+/mo
ProfitWell	Commercial	✓	❌	❌	❌	Free–$99/mo
Clari	Commercial	❌	✓	❌	✓	$100–$200+/user/mo
Gong	Commercial	❌	✓	❌	✓	$1,300–$3,000/user/yr
Pecan AI	Commercial	❌	✓	❌	✓	Quote-based
Metabase	OSS	❌ (needs warehouse)	❌	❌	❌	Free self-hosted
This Project	OSS + SaaS	✓	✓ (AI)	✓ (NL)	✓ (ML)	Free self-hosted

Technical Design Considerations

Billing ingestion: Direct Stripe API connectors; support Recurly, Chargebee, Zuora via webhook or API polling
Metrics calculation: PostgreSQL for state management; exact SaaStr / Bessemer definitions (MRR, ARR, NRR, GRR, LTV, CAC)
AI layer: Claude API for anomaly explanation, NL query interpretation, and narrative generation
ML forecasting: Prophet (Facebook) or ARIMA for time-series baseline; Bayesian posterior sampling for scenario analysis
Account scoring: XGBoost or LightGBM trained on cohort churn/expansion patterns; SHAP for explainability
Deployment: Docker Compose self-hosted; managed SaaS with team collaboration (similar to ChartMogul model)
Compliance: ASC 606 / IFRS 15 revenue recognition standards built-in from start; audit trail for finance teams

Market Validation

Market drivers:
- Clari/Salesloft merger (Dec 2025) consolidating forecasting market; creates opportunity for open-source alternative
- Paddle acquired ProfitWell; bundling metrics into billing platform
- Snowflake acquired Select Star; consolidating analytics-layer tooling
Customer personas:
- SaaS founders/operators needing real-time MRR visibility without data engineering
- VP of Sales / CRO focused on pipeline-to-revenue conversion and forecast accuracy
- CFO / finance teams requiring ASC 606-compliant numbers and integration with FP&A tools
- Revenue Operations (RevOps) teams diagnosing expansion/contraction drivers

Why Build This

Market timing: Clari/Salesloft merger and Paddle acquisition of ProfitWell create consolidation opportunity
Technology maturity: Time-series forecasting (Prophet, ARIMA) is well-established; SHAP-based ML explainability is standard
Zero data engineering: Direct Stripe integration eliminates ETL burden that blocks current open-source adoption
Commercial opportunity: Build on free tier; offer managed SaaS with integrations (Slack, Salesforce, QuickBooks) for SMB/mid-market
Platform leverage: Stripe API, Prophet forecasting, Claude for narratives; focus on UI and integration

Success Metrics

Adoption: 500+ GitHub stars within 12 months; featured as ChartMogul + Clari alternative for mid-market
Commercial: Win 30+ SMB/mid-market customers with managed SaaS tier ($1K–$5K/mo)
Forecasting accuracy: Achieve <10% mean absolute percentage error (MAPE) on real SaaS revenue patterns
Community: Active issue triage; 3+ contributors beyond core team

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