Vibe-coded projects ship fast and break silently. We audit AI-generated software with a repeatable, outcome-based methodology that measures consistency, correctness, cost, and resilience — before your clients find out.
Vibe-coded projects function at demo scale. Under real users, real data volumes, and real edge cases, the cracks surface. The AI patched what it didn't understand and moved on.
By the time quality is considered, architecture is fixed and technical debt is compounding. Standard approaches assume deliberate design that vibe coding never produces.
Each piece was built independently by an AI optimizing locally. The seams — where two vibe-coded sections meet — are where systems fail. These are invisible until they aren't.
Most teams shipping AI features have no idea what they're spending until the bill arrives. Inefficient prompts and unoptimized pipelines scale costs non-linearly with usage.
Each layer targets a distinct failure mode that standard QA never catches in AI-generated codebases.
We execute your AI pipeline against a fixed dataset repeatedly and compare outputs. Tests missing from some runs, clusters that shift, results that vary — all quantified into a consistency score. Fast to run, impossible to fake.
We build or supply ground truth datasets — inputs with known expected outputs — and score your system against them. This is the distinction between a system that reliably produces wrong answers and one that actually works.
We deploy AI browser agents to execute user journeys end-to-end. Unlike brittle Selenium scripts that break when a button moves, agents navigate by intent. As AI-built UIs simplify, this approach becomes more reliable — not less.
We instrument your AI pipelines, measure token consumption per operation, and project monthly spend at real usage volumes. Inefficient prompts and redundant calls are flagged with specific optimization recommendations.
Traditional stress testing checks uptime. We measure output quality under concurrent load — whether AI features produce worse results when hammered. A system that stays up but hallucinates more under pressure is not a system you can trust.
How reliably your AI pipeline produces the same output for the same input across ten repeated runs.
AutomatedHow accurately your system produces expected outputs against a curated ground truth dataset.
AutomatedFull coverage of critical user journeys via AI browser agents, with failure tickets filed automatically.
AutomatedPer-operation token costs extrapolated to real usage volumes, with specific prompt optimization recommendations.
AutomatedPrioritized list of fixes and improvements, AI-generated and engineer-reviewed before delivery.
AI-assisted + reviewedEvery failure grouped by root cause and filed as a structured Jira ticket, ready to action immediately.
AutomatedWe review the project, identify AI components and critical user journeys, define benchmark datasets and test scope. No deep codebase knowledge required from your team.
Consistency pipeline runs 10 passes. UI agents execute all mapped journeys. Load tests simulate production traffic. Token instrumentation captures cost data.
Diffs are analyzed, outputs scored against ground truth, failures classified by root cause. AI-generated recommendations reviewed by our engineers before delivery.
You receive the quality dashboard, full Jira ticket queue, cost projection, and prioritized recommendations. Audit repeatable on any future deployment.
We audit what exists. No process change required, no access to development needed. Receive your quality index within 48 hours and know exactly where you stand.