What does "AI-augmented development" mean?

AI-augmented development means our senior engineers use AI to accelerate drafts, tests, and documentation — then audit, harden, and review every line before it ships. Humans own architecture, security, and code quality. You get 40–60% faster delivery without the vulnerabilities that come from vibe-coded software.

Do you offer cybersecurity services on their own, or only with development?

Both. We deliver security alongside development — and we also run standalone security work for existing products, including security audits, penetration testing, and remediation planning. You don't need a new build to start a security engagement.

How do you secure AI-generated code?

Every AI-generated line is reviewed by a senior engineer before it ships, then checked with automated SAST scanning and our standard QA gates. AI speeds up drafts — humans and tooling own what reaches production.

How much does a typical project cost?

Costs depend on scope, complexity, and timeline. After a discovery call, we provide a transparent quote with clear milestones and no hidden management overhead.

What engagement models do you offer?

We support fixed-scope delivery, dedicated teams, and monthly retainers. We recommend the model based on your roadmap certainty, speed requirements, and internal team setup.

What's the first step to start a project?

The first step is a technical discovery call. We align on goals, users, scope, and constraints, then share a practical plan with timeline and delivery phases.

Who will work on my project?

You work directly with senior engineers and product-minded specialists. We avoid heavy management layers so communication stays clear and execution stays fast.

What industries do you specialize in?

We work across startups, SMEs, and enterprise teams in sectors like finance, healthcare, e-commerce, and SaaS, with deep experience in custom Chromium/browser products.

State of AI-Generated Code in 2026: Security Findings

AI-generated code in 2026: the security baseline

A scan of 424 projects found 346,944 issues across 21,632,176 lines of code. That works out to 16.0 per 1,000 lines of code, or one issue every 62.4 lines.

87% of projects had at least one security finding.

The median score was 86. The average score was 85. Only 56 projects were clean on security. The rest had at least one finding.

The sample was not random noise. The methodology used 4 source markers/classes: Lovable README text, v0 auto-sync READMEs, Bolt README references, and repositories tagged ai-generated or vibe-coding. The filters added 6 filter conditions: no forks, size between 150 KB and 100 MB, pushed after 2026-01-01, a primary language in TypeScript, JavaScript, Python, or PHP, a max of 2 repos per owner, and at least 100 lines of scannable code.

What the study scanned

The study scanned 424 public AI-generated projects and then grouped them by platform and language. The scan used the production rule engine of Quality Clouds Hub, Semgrep 1.136.0 plus a regex runner, with deterministic rules only.

That matters because the headline number is not just “AI code has problems.” It is a map of where those problems appear and how often the scanner hit them.

The corpus included 206 projects backed by Supabase. In that group, 98% had at least one security finding. Outside that group, 77% had at least one security finding.

98% of Supabase-backed projects had at least one security finding.

The dataset also reported 14% of projects shipped at least one leaked secret or hardcoded credential. That is the kind of issue that turns a code review problem into an incident response problem. If you want a broader checklist for where these mistakes usually show up, see AI Coding Tools and AppSec Risk: What to Audit Now.

AI-generated code security findings by platform

Platform choice changed the risk profile, but it did not remove it. The Copilot-assisted GitHub group had 89 repos and 9.1 issues/KLOC, with 65% of projects showing a security finding. The Bolt group had 42 repos and 11.4 issues/KLOC, with 67% of projects showing a security finding.

The v0 group was worse on both counts: 110 repos, 16.6 issues/KLOC, and 92% of projects with a security finding. Lovable sat highest in the set at 183 repos, 18.1 issues/KLOC, and 99% of projects with a security finding.

One detail stands out. Lovable’s median issues per project was 644 issues. That is not a small gap. It suggests a long tail of repeated problems, not a few isolated misses.

The dataset also said 84% vs 3% for Supabase-backed Lovable projects versus Copilot-assisted repos. That helps explain part of the spread, but not all of it. Platform defaults, generated patterns, and the kinds of apps people build all matter.

If your team is using agents to scaffold apps, the real question is not whether they write code fast. It is whether they write code that passes a merge-time security gate. That is where a services/ai-code-security-audit style review becomes relevant: after you know the failure modes, you need a repeatable way to catch them before release.

AI-generated code security findings by language

Language mattered more than the broad “AI-generated” label. Python repos had 4.0 issues/KLOC across 43 repos. JavaScript repos had 15.3 issues/KLOC across 37 repos. TypeScript repos had 16.9 issues/KLOC across 344 repos.

The corpus had 0 PHP repos. That makes the Python result especially interesting. It suggests the generated Python projects in this sample were either smaller, simpler, or both better controlled than the JavaScript and TypeScript groups.

React + Vite also showed up as a dense cluster: 215 repos at 17.1 issues/KLOC. That lines up with what many teams already see in practice: frontend scaffolds can look complete while still hiding weak async handling, bad state transitions, and sloppy environment handling.

The key takeaway is simple. Do not assume one language is “safe” because it feels more structured, or “risky” because it is popular. Measure the project you actually shipped.

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Which issue categories dominated

The study’s issue mix was lopsided. Scalability led with 150,212 issues, across 386 repos affected. Maintainability followed with 65,152 issues across 382 repos affected. Performance came next with 57,821 issues across 380 repos affected.

Manageability also showed up heavily, at 52,236 issues across 389 repos affected. Security itself was lower in raw count but still large at 21,361 issues across 368 repos affected. Architecture was tiny by comparison, at 162 issues across 29 repos affected.

That mix matters. It shows the pain is not only “classic security bugs.” AI-generated code often creates systems that are hard to scale, hard to maintain, and hard to operate. Those are security problems too, because messy systems invite rushed fixes and blind spots.

The top rules behind most findings

The repository also reported that the top 10 most frequent rules accounted for 88% of all findings. The five HIGH rules in that set accounted for 97% of all HIGH findings.

That is the most operational result in the whole study. It means you do not need to boil the ocean to reduce risk. You need to target the small set of issues that drive most of the noise and most of the danger.

The single most common HIGH finding, Async Operation Without Error Handling, appeared in 79% of projects and occurred 138,601 issues. In plain terms: async code failed without a safety net, and it failed everywhere.

79% of projects had the single most common HIGH finding.

For teams shipping AI-generated code, this is the right place to start. Put checks around the few rules that dominate the corpus. Add security review for async error handling, secret leakage, and framework defaults. Then scan every merge, not just every release. If you need a deeper framing for how AI changes day-to-day development risk, Senior Developers vs Junior Developers in the Age of AI is a useful companion piece.

What to do before you ship AI-generated code

Do not treat AI-generated code as prototype-only code. This study’s numbers say the opposite. The majority of the sample had findings, and the common ones repeated at scale.

Start with three steps:

Block secret commits. The dataset showed 14% of projects shipped at least one leaked secret or hardcoded credential.

Fail builds on the few rules that drive most issues. The top 10 most frequent rules made up 88% of all findings.

Audit the project type you actually use. In this sample, Lovable sat at 99% with a finding, while Copilot-assisted GitHub projects sat at 65%.

If your team publishes AI-assisted code or depends on it in production, review the highest-frequency failure modes first. Then scan on merge, not after launch. The study does not say AI code is unusable. It says AI code is already producing the same classes of bugs, at scale, before humans have time to notice them.