Code intelligenceGaganCodes productStatic analysis · AI grounding
AI can write code.
Evidence earns trust.
CodeRescue is an evidence-backed audit system designed to help teams understand the structural risks hiding inside fast-moving, AI-assisted codebases.
THE PROBLEM IS NOT AI CODE.It is shipping code nobody can confidently explain.
More software.
Less certainty.
AI coding tools have dramatically increased implementation speed. They have also made it easier for teams to accumulate duplicated patterns, ambiguous dependencies, broken assumptions and architectural drift before anyone has reviewed the system as a whole.
Traditional linting catches syntax and local rule violations. Generic AI reviews can sound convincing without being grounded in repository structure. CodeRescue was designed for the gap between them.
- Large repositories exceed naive analysis limits.
- Changed files can disappear when file caps are applied incorrectly.
- Configuration boundaries complicate dependency analysis.
- AI findings need traceable evidence and confidence.
- Teams need prioritized recovery decisions, not hundreds of warnings.
Internal validation
Tested against real, complex repositories.
Repository identities are withheld. Figures below are engineering validation results—not invented client outcomes.
02 / The pipelineDeterministic first.
Deterministic first.
Reasoning second.
Scope
Clone the requested revision and prioritize changed paths without excluding changed files.
Analyse
Build module and configuration graphs, then run static and repository-aware checks.
Ground
Give the reasoning model structured evidence instead of asking it to guess from raw source.
Report
Return findings with location, impact, confidence and a practical recovery direction.
Engineering doctrine
Never ask a model to infer what the system can establish deterministically.
Module relationships, changed paths, configuration scope and structural candidates are computed first. AI is used to interpret grounded evidence, explain impact and help prioritize the response.
From memory failure
to controlled analysis.
An early full-repository run reached 1,841 files across 409 directories and exhausted a 4 GB process heap. Rather than hiding the problem behind a larger machine, we changed the analysis approach.
The revised pipeline scopes changed paths first, expands to relevant neighbours second, preserves every changed file, and constructs a reusable module graph before reasoning begins.
Changed paths firstNeighbour expansionConfig-aware graphBounded contextNo changed-file drops
04 / Validation runsAmbiguity is reported.
ANONYMIZED REPOSITORY A921 ANONYMIZED REPOSITORY B668
Ambiguity is reported.
Not quietly converted into certainty.
files analysed
394 candidates0 contradicted95 ambiguous
files analysed
417 candidates3 contradicted51 ambiguous
Built as an audit worker.
Ready for any interface.
The initial product is deliberately headless: an asynchronous analysis endpoint accepts a revision-scoped job and returns results through a callback. A health endpoint supports operational monitoring.
This separation keeps the analysis engine independent from any dashboard, marketplace or internal workflow that may consume it later.
Product strategyAudit architectureStatic analysisModule graph engineeringAI groundingPerformance optimizationAPI designValidation methodology