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.

Codex at Cisco: 95%+ of New AI Features

Cisco’s Codex rollout changed the pace of product work

Cisco says Codex now writes 95%+ of new AI features 95%+, helped write the majority of AI Defense, and supports nearly every new feature the company is building majority; nearly every new feature. That is not a pilot. That is a production workflow shift.

The number that matters most is not just output. Cisco says Codex saves more than 1,500 engineering hours per month. It also reports a 10-15x increase in defect resolution throughput using Codex CLI. Those two claims point to the same thing: AI is no longer just drafting code. It is changing where engineering time goes.

Cisco says Codex writes 95%+ of new AI features. That is the kind of number that changes team structure, not just tooling.

For security teams, this kind of velocity creates a new problem. Faster shipping can be good. Faster shipping with weak review is not. If you are trying to understand where AI-assisted development creates risk in practice, see AI Coding Tools and AppSec Risk: What to Audit Now.

What Cisco says Codex is doing

Cisco’s public case study gives a rare look at how a large enterprise is using an AI coding tool across multiple workflows. It is not just generating snippets. Cisco says Codex helped compress critical engineering work for AI Defense from several quarters to weeks several quarters to weeks.

That matters because multi-quarter work usually includes planning, implementation, review, debugging, and release coordination. If Codex can compress that cycle to weeks, then the pressure shifts from code generation to integration. The bottleneck becomes validation, not typing speed.

Cisco also says it used Codex to help build its Defense Squad open-source tool, and that the tool moved from ideation to the developer community in under one week under one week. Again, the pattern is the same: the first draft is cheap, and the path to a public artifact is shorter than many teams are used to.

This is why AI-assisted development is now a governance problem as much as a productivity story. The tool can move fast. The question is whether the rest of the pipeline can keep up.

Why the 1,500-hour figure matters

A monthly savings of more than 1,500 engineering hours per month is large enough to change planning. That is not a tiny efficiency gain. It is the kind of number that gets folded into roadmap assumptions, staffing choices, and release schedules.

Cisco ties that saved time to build optimization work across more than 15 repositories. Codex analyzed build logs and dependency graphs across those repositories and identified inefficiencies. The result was about a 20% reduction in build times.

Cisco says Codex saved more than 1,500 engineering hours per month. When a tool gives back that much time, you should ask what kind of review debt it also creates.

That combination matters. A single repository is hard enough to manage. Cross-repo optimization means the system has to understand build behavior, dependencies, and workflow coupling across a wider surface. That makes speed gains more valuable, but it also raises the stakes for anything the model misses.

If your team is already using AI to accelerate code generation, this is a good point to revisit your review process. In practice, a tool that helps ship faster also needs stronger guardrails. If you need a framework, Cisco’s example connects naturally to Enterprise AI Agents: How to Secure Them in 2026.

What 95%+ of AI features means for engineering teams

Cisco says Codex wrote more than 95% of new AI features. That is the kind of adoption that changes how teams think about authorship.

When a model writes most of the first pass, engineers are no longer spending their time on empty scaffolding. They are editing, reviewing, wiring systems together, and checking behavior across edge cases. That can be efficient. It can also hide risk if teams start trusting the output because it looks complete.

The practical issue is not whether AI can write code. Cisco’s numbers show that it can, at scale, inside a large enterprise. The issue is whether the team has a repeatable way to answer four questions:

Did the generated code change authentication, authorization, or data flow?
Did it introduce new dependencies or widen existing ones?
Did it affect build behavior across repos?
Did anyone test the failure path, not just the happy path?

Those questions matter because AI-written code often arrives as a polished answer. Polished is not the same as safe. If you are already seeing AI-generated code land in PRs, it is worth pairing the speed gains with the checks that stop silent regressions.

For teams that want to go deeper on this tradeoff, our broader guide on What Is AI-Augmented Software Development? A Complete Guide helps frame the operational change without pretending the risk disappears.

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How build optimization changed the release pipeline

Cisco’s build story is easy to miss because it sounds less dramatic than feature generation. It should not be missed. Codex analyzed build logs and dependency graphs across more than 15 repositories and delivered a 20% reduction in build times.

That matters because build time shapes developer behavior. Slow builds reduce iteration. Slow builds also encourage batching, which delays feedback and makes failures more expensive to fix. If Codex can shorten that loop, engineers get faster feedback and more room to test changes before release.

But build optimization is also where hidden coupling tends to show up. Cross-repo work can surface legacy assumptions, fragile dependencies, and expensive workflows that nobody wanted to touch manually. AI can help identify that mess. It can also create a false sense that the whole pipeline is now simpler than it really is.

The right response is not to reject the speedup. It is to treat it as an opportunity to harden the delivery process. That means tighter checks on build outputs, clearer dependency ownership, and a policy for when AI-generated changes need extra review.

What security teams should do now

Cisco’s case study is useful because it shows the scale at which AI coding is already landing in enterprise workflows. Once a tool is responsible for 95%+ of a feature stream, the security question becomes concrete: who reviews the output, and what do they inspect first?

Start with the code paths that move fastest. Review generated auth logic. Review data handling. Review dependency changes. Review build pipeline edits. Review any repository that feeds a shared release system. Then track whether AI output is changing the rate of defects, not just the rate of commits.

The Cisco example also suggests a simple organizational rule: if AI can compress work from several quarters to weeks, then security review cannot stay on the old schedule. Review has to move earlier, and it has to happen before merge, not after release.

If your team wants a structured assessment of where AI-generated code is already adding risk, this is where an AI Code Security Audit becomes relevant. It answers the question teams usually ask after the first productivity win: what did we speed up, and what did we fail to inspect?

The takeaway for teams using AI coding tools

Cisco’s numbers are clear. Codex now writes 95%+ of new AI features, saves more than 1,500 engineering hours per month, and improved defect resolution throughput by 10-15x. It also helped cut build times by about 20% across more than 15 repositories.

That is a real productivity gain. It is also a warning sign for teams that have not updated their review model. If AI is now writing most of the first draft, security can no longer rely on the old human bottlenecks to catch mistakes.

The practical move is simple: pin review gates to the changes AI touches most often, audit generated code before merge, and measure whether faster shipping is also changing defect rates. Speed is useful. Speed with proof is better.

Codex at Cisco: 95%+ of New AI Features