CodeWatchdog
Three questions: Why are you better? Why are you private? Why don't you store code? Direct answers below.
Static analysis tools — SonarQube, Semgrep, ESLint — match rules written for human code. AI generators produce plausible, well-formatted code that passes those rules while hiding subtle trust issues, missing edge cases, and logic that almost works.
AI review bots are circular: an AI reviewing AI shares training data, assumptions, and blind spots with the generator. No adversarial layer exists. Traditional security firms are enterprise-only — expensive, slow, and built around procurement cycles that outlast most shipping timelines.
We layer AI and human review in the right order. The AI scan runs first — fast, structured, using prompts tuned for AI failure modes, not generic vulnerability rules. It produces a categorized, severity-ranked report.
A senior engineer then reviews that report alongside your code. They validate findings, find what the AI missed, and apply adversarial judgment no model provides. Every finding in the final report has a severity rating, explanation, and a specific implementable fix — not a suggestion, not a documentation link.
We don't assign whoever is available. Solidity goes to someone who has audited Solidity in production. Node.js auth goes to someone who has found broken auth in production systems. Domain expertise determines whether a review is useful or just thorough-looking. A reviewer outside their domain catches obvious issues. They miss the subtle ones. The subtle ones are what kills you.
Every review is scoped and priced before work begins. No hourly rates, no subscription tiers, no enterprise contracts required. If the scope changes, we discuss it before continuing — not after the invoice.
| Tool Type | Core limitation | CodeWatchdog |
|---|---|---|
| Static analysis tools | Rule sets pre-date AI-generated code. No context, no intent, high false positive rate on AI output. | Prompt engineered for AI-specific failure patterns. Human reviewer validates context. |
| AI review bots | AI reviewing AI is circular. Shared training data means shared blind spots. No adversarial thinking. | Human judgment is the adversarial layer. The reviewer has no shared bias with the generator. |
| Security firms | Enterprise-only. Five-figure minimums, months of procurement, not designed for fast-moving teams. | Same caliber of reviewer. Per-project pricing. No contract required to start. |
| Freelance marketplaces | You vet the reviewer yourself. Quality and methodology are unpredictable. No structured output format. | Pre-vetted, stack-matched reviewers. Consistent methodology. Standardized reports. |
Open access means managing volume. Volume means degraded quality, slower responses, imprecise matching, and templated reports. Every project gets a reviewer matched to that exact stack and domain. That's only possible because we control the pipeline. Controlled access is what makes that guarantee real.
Every engineer passed an evaluation before being matched to client work. We don't add reviewers to meet demand — we add them when we find the right person. Twenty rigorously selected engineers beats a marketplace of two thousand.
A public service accepts everyone. We don't. If a project falls outside our network's expertise, we say so instead of assigning a poor match. If scope is unclear, we ask before starting. Being selective protects the quality of every engagement, not just yours.