About BuilderProof
By the BuilderProof editorial team
BuilderProof is an independent benchmarks publication for AI app builders. We test the tools that turn a prompt into a working application, then publish the scores, the rubric behind them, and the test brief used to produce them. Our audience is the operators, agencies, and engineers who have to pick a builder and ship with it, so every benchmark is written to be reproduced and contested rather than taken on trust.
What we publish
Each benchmark covers a current cohort of AI app builders against a single, fixed test brief. We score four axes: output quality (the visual fidelity, code structure, and functional correctness of the generated app), speed (wall-clock time from prompt to a usable build artifact), deploy quality (SEO, accessibility, and performance audits run against the live deployed product), and agency suitability (whitelabel, MCP support, public API surface, and code portability). Standalone posts report unweighted per-axis scores; a weighted roll-up is used only when an article compares builders across multiple axes, and the per-axis tables are always shown alongside it so a reader who weights differently can recompute the result.
How we score
Every score is produced by a published, version-stamped method rather than by editorial preference. The rubric, the per-axis weights, the fixed test brief, and the environment standards are documented in public before any builder is scored, so a result can be challenged on its own terms instead of in the abstract. Each axis returns a 0 to 100 score under an axis-specific rubric, and every builder in a cohort is run under identical conditions: a fresh account, the builder's default model, an automated Chromium runner, and a logged 72-hour test window. When the methodology updates, the whole cohort is re-run together so scores stay comparable across a benchmark generation, and superseded figures are retained in each page's history.
Refresh cadence
Builders ship quickly, so a benchmark is a snapshot, not a verdict. We re-run a cohort when a methodology version changes, when a builder makes a material release, or on a periodic refresh, and we date-stamp every result so a reader can see how current it is. If you believe a score is wrong or out of date, send the evidence through our contact form. We review every challenge against the published method, and a correction that holds up is applied in the next cycle with the prior figure kept in the page history.