S-Curve Data helps product teams turn forecasts, experiments, causal readouts, recurring reports, and decision logs into one operating process. Specialist agents produce the evidence; the operating loop turns that evidence into decisions.
The demo shows how a team moves from North Star planning to evidence gates, operating reports, and accountable decisions without losing the source trail.
Set annual targets, compare baseline forecasts, size initiatives, track source-backed lift, and keep assumption-only impact separate from measured topline impact.
Route randomized tests, observational causal readouts, must-do launches, guardrails, SRM checks, power analysis, and measurement narratives into launch decisions.
Generate QBR and metrics-review artifacts that tie actuals, forecasts, initiatives, funnels, segments, and data readiness back to topline movement.
Make every claim inspectable through source tables, artifact status, pending data gates, decision owners, and follow-up actions.
The product value is both the operating process and the expert agent bench behind it: forecasting, experimentation, causal inference, analytics, data quality, funnel, segmentation, predictive modeling, insights, and planning.
Specialist agents package recurring data science judgment into governed workflows without hiding assumptions, caveats, or source lineage.
Forecasting informs Plan, Experimentation and Causal support Prove, Analytics and Insights power Report, and Planning turns evidence into Decide.
Agent outputs carry quality checks, lineage, evidence gates, and human review so teams can tell assumptions from measured impact.
The loop is powered by shared data contracts, source-backed evidence, specialist agents, and human-reviewed planning synthesis.
The operating loop gives teams a clear process. Specialist agents give that process depth: they produce inspectable evidence for planning, proof, reporting, and decisions.
Estimate incremental impact from experiments, observational launches, and quasi-experimental designs. The agent helps teams choose methods, check assumptions, produce diagnostics, and turn measured impact into governed decision evidence.
Compare baseline forecast methods, inspect backtests and uncertainty distributions, refresh planning projections, simulate scenario overlays, and expose the gap between likely landing points and annual targets.
Predict churn, activation, and revenue with an auditable model lifecycle. Connects feature onboarding and expanding temporal cross-validation to XGBoost and Tweedie models, baseline hurdle gates, batch prediction ledgers, and delayed-outcome monitoring.
Pilot platform in active development. Enforces immutable feature snapshots, calibration stability, and mature outcome evaluation without automated retrain leaks.
Enforce statistical rigor across registered experiment designs, fixed-horizon readouts, and launch decisions. Features assignment diagnostics, chi-square SRM gates, continuous CUPED variance reduction, and Holm-corrected simultaneous inference bound to immutable evidence.
Pilot platform in active development. Standardizes multi-arm contrast families, conservative simultaneous intervals, and versioned decision memory.
S-Curve Data is being built by Sophia Chen, drawing from her experience defining foundational success metrics and core ML forecasting capabilities at Google, and spearheading 0-1 product data science and experimentation culture at Intuit.
I am actively developing this into a working S-Curve Data product: a team-scoped operating loop for North Star planning, evidence gates, recurring reports, and source-backed decision logs, powered by a bench of specialist data science agents. The system is built around scalability, statistical rigor, governance, and executive alignment, with each agent producing analysis that a human data scientist can inspect, challenge, and reuse. I am looking for technical collaborators and design partners to help shape its future.
Read Full ProfileReach out if you want to compare notes on source-backed product planning, specialist agents, evidence gates, operating reports, decision logs, or the S-Curve Data demo.
