Benchmarks

98.5% of tasks ship within estimate.

Measured across 1,425 completed builds in 229 cycles on PAPI itself. Estimation in AI-assisted software is the part teams expect to be unreliable. With the cycle methodology, it is consistently the predictable part.

98.5% of 1,425 tasks shipped within estimate across 229 PapiUI cycles, baseline reference 50%.
Per-cycle estimation accuracy, smoothed across a 5-cycle trailing window. Orange dots mark cycles where accuracy dropped below 95%.

How it compares

McConnell's industry reference for software estimation puts unaided projects at roughly even odds of landing within estimate at all, with median schedule overruns of 30 to 50% (Software Estimation: Demystifying the Black Art, 2006). PAPI's 98.5% figure sits well above that baseline, sustained over more than two hundred cycles rather than a single project.

Methodology

Source: PapiUI's own build_reportstable in Supabase. Each row is a completed build with the planner's estimated effort and the builder's reported actual effort.

  • Cycle range: 17 to 268 inclusive (effort tracking began at cycle 17).
  • Effort sizes map to points: XS=1, S=2, M=3, L=5, XL=8.
  • “Within estimate” means actual_pts is at or below estimated_pts.
  • Filter: completed = 'Yes'. Cancelled, abandoned, or partially-completed builds are excluded.
  • Result: 1,404 of 1,425 = 98.5%.

What this means in practice

High estimation accuracy is not a claim that PAPI builds the right thing; it is a claim that when PAPI agrees to build something, the work lands inside the box the planner drew. Combined with structured handoffs and review cycles, that predictability is what lets a single builder coordinate a project of this size without the calendar slipping out from under them.

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