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Executive Summary

Engineering teams repeatedly over- or under-commit because planning-poker produces noisy, poorly calibrated estimates: votes reflect optimism bias, varying baselines, and context loss, and those problems are amplified for remote/hybrid teams and cross-functional squads. This is a problem for product and engineering managers who must forecast capacity and allocate work reliably; using our market sizing, there are roughly 600,000 such teams that could justify a team-level subscription. The product would ingest anonymous planning-poker voting histories, issue metadata (Jira/GitHub), and actual cycle-time outcomes to produce calibrated, probabilistic sprint estimates and per-story confidence scores, plus cohort benchmarking against anonymized peers. Features would include actionable remediation (suggest split, re-scope, or test tasks), automated retrospectives that surface systematic bias, Slack/Jira integrations for in-meeting nudges, and a team-level SaaS pricing target near $6,000 ACV. This is an attractive moment because three structural trends align: widespread adoption of digital facilitation creates centralized vote and interaction data, leaders demand analytics beyond raw velocity, and ML tooling for teams is maturing—together supporting a $3.6B addressable market in our sizing and a high market score (92/100). Buyers are increasingly willing to pay for predictable capacity and quantified uncertainty, but they will expect concrete ROI signals (reduced missed commitments, decreased rework). To stand out you should emphasize noise calibration and benchmarking rather than replacing human judgment, provide transparent uncertainty estimates and explainable model outputs, and use privacy-preserving aggregation (or federated approaches) to build cross-team benchmarks without exposing IP. Challenges are real: small teams will have sparse data, models can drift with process changes, and sales require pilot evidence tied to measurable forecasting improvements, so go slow with targeted pilots and clear success metrics rather than broad promises.

Executive Summary

Noisy sprint estimates — AI + benchmarking for planning-poker accuracy

Executive Summary

Noisy sprint estimates — AI + benchmarking for planning-poker accuracy

Executive Summary

Market Validation

Market Opportunity

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