Crystal Ball PM · delivery forecasting

Software delivery estimation framework using historical completion data

Hallway estimates collapse because they hide distribution and scope fuzz. This workspace refines scope, pulls ranges from Jira history or offline modeling, and drafts tickets you can hand to engineering—so “two days” has to defend itself against data.

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About this tool

History beats heroics

Weighted averages from similar done work anchor optimism. When history is missing, the model still returns an explicit range with confidence you can debate.

Enterprise readiness without theater

Pair estimates with the Predictive Delivery Tracker and governance cadence from advisory when your bottleneck is system-wide, not a single ticket.

A scene from every roadmap

TPM: “Real quick—how long do you think that’ll take?”

Tech lead: “Two days.”

TPM: “Calendar days… or engineering days?”

Tech lead: “…Yes.”

Sound familiar? The crystal ball can’t read minds—but it can combine that eternal question with averages from history (plus a nudge when your ticket is fuzzier than a stuffed animal at a standup). Still not magic. Still beats pure vibes.

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