S-Curve Data is building a working Product Intelligence OS for recurring product data science workflows. Teams connect their metrics, funnels, experiments, forecasts, causal readouts, segments, and planning assumptions; specialist agents assemble source-backed evidence with lineage, quality gates, and human review built in.
The current prototype turns real source marts into planning, measurement, forecasting, and review workflows with explicit evidence status.
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.
A team-configurable semantic workspace coordinated with governed, auditable agents for product data science workflows.
Give product data teams a reusable workspace for registered metrics, funnels, experiments, forecasts, source lineage, recurring diagnostics, and executive communication.
Connect existing warehouse tables, metric definitions, experiment logs, model artifacts, and scheduled marts into agent-ready decision evidence.
Package analytics, data quality, funnel, segmentation, experimentation, causal inference, forecasting, predictive modeling, and planning into specialist agents while keeping DS judgment and statistical rigor in the loop.
Decision workflows sit on top of shared data contracts, source-backed evidence, specialist agents, and human-reviewed planning synthesis.
Focused data science agents that can run as standalone expert tools or plug into the Product Intelligence OS as governed evidence producers.
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 quarterly projections, and expose the gap between likely landing points and annual targets.
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 Product Intelligence OS: a team-scoped semantic workspace with high-quality Data Science agents, reusable planning and review artifacts, source-backed decision logs, and explicit measurement gates. The system is built around scalability, statistical rigor, governance, and executive alignment, with each agent designed to produce evidence that a human data scientist can inspect, challenge, and reuse. I am looking for technical collaborators and design partners to help shape its future.
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