Methodology · how we measure

How we measured it: 3X faster, 90% fewer bugs.

The claim

Products delivered on Wegile's AI-native pipeline ship at least 3X faster with ~90% fewer production defects than comparable projects on our traditional model.

Our measured data shows more than 3X — we publish the conservative floor and let the numbers below speak.

The basis

Measured across our first 9 engagements delivered end-to-end on the AI-native pipeline (September 2025 – June 2026), compared against 20 comparable traditional-model engagements from our own delivery history — same project types (mobile apps, web platforms, API backends) and similar scope.

What we measured, exactly

1. Specification & architecture phase — project kickoff → approved specification.

Traditional: ~30 working days · AI-native: ~10 working days · 3X faster.

AI-accelerated requirements mapping and architecture estimation, validated by the BA architect (Gate: expert review of all findings).

2. Build phase — approved specification → full scope live on staging.

Traditional: ~120 working days · AI-native: ~15 working days · 8X faster.

This is the number that surprised us too. It's what daily staging deploys plus AI generation under architect specification produce when the LaunchPad foundation (cloud, CI/CD, AI toolchain) is in place from day one.

3. End to end — kickoff → product on staging.

Traditional: ~150 working days · AI-native: ~25 working days · ~6X faster.

We say “3X” in our marketing because we'd rather you experience the upside than discount the claim.

We deliberately do not measure story points or velocity — those are self-graded. Working days between checkable milestones, visible in the repo and project tool, are not.

4. Quality — production defects per release. Client-visible bugs logged against production builds in the first 30 days after each release.

Traditional: ~10 per release · AI-native: ~1 per release · ~90% fewer.

Defect = incorrect behavior logged in the tracker. Excludes change requests and spec changes.

5. Time to fix. When a defect does appear:

Traditional: a multi-day cycle — reproduce, troubleshoot, identify, fix, test, release.

AI-native: fixed and released same-day in the measured sample. The six-gate pipeline that ships features daily ships fixes at the same speed.

Why the pipeline produces these numbers

  1. AI generates against architect specification (Gate 1) — the typing is fast, the intent is senior.
  2. Machine gates catch what humans miss at speed — complexity, security, and regression scans on every push (Gates 2, 3, 5).
  3. A named architect reads every diff (Gate 4) — AI never approves AI.
  4. Daily staging deploys — bugs surface in 24 hours, not at sprint-end; they never compound.

What we're NOT claiming

  • Not every project hits these numbers — complex integrations and legacy rebuilds run closer to 2X, which is why 3X, not 6X, is our public claim.
  • The sample is 9 engagements against a 20-project baseline, and growing; we update these figures as the sample grows.
  • Speed gains assume the LaunchPad foundation is in place — that's why it's step one of every engagement.

Verify it yourself

  • Live walkthrough of the pipeline on a real PR — on any technical call, no slides.
  • The underlying cycle-time and defect data for this sample — shareable on request under NDA.
  • Clutch ★4.9 (verified, moderated reviews) · Upwork Top Rated Plus, 100% Job Success.
  • Client references, including engagements in this sample, available on request.

Wegile · founded 2013 · 120-person firm, 50 senior architects · wegile.com