AI beyond the surface: AI audit to production delivery

An agency’s AI-generated front-end for 24 page templates ran and rendered, but it had drifted from the Figma designs. We ran an AI audit, priced three rescue paths up front, and rebuilt it the way they chose: AI-assisted, with a senior engineer checking every section against the design. 

Our client is a US-based branding and web design agency for member-driven organizations and associations, and our partner since 2011.

Business challenge

The agency reached out to us with an AI-generated front-end build containing 24 page templates. Their AI coding assistant had produced a surprisingly complete first pass in a few hours, but they knew better than to ship code no engineer had checked. Our initial AI audit proved them right and found critical gaps across the build:

• Surface-level quality hiding business risk. The code ran and rendered, but the markup only approximated the Figma designs. Sizes, fonts, and shadows drifted, and wherever the written spec and the design disagreed, the AI made its own choice. That put brand reputation and client trust at risk.
• Inconsistency that breaks the user experience. Spacing, hierarchy, and responsive behavior varied from page to page, and keyboard navigation together with focus states had never been tested. Inconsistent, inaccessible pages could confuse real users and put conversions at risk.
• Technical and commercial debt built in. Hard-coded values, duplicated styles, and missing human validation could lead to future bugs, fragile code, and messy handoffs, all risking costly rework later.

Left unchecked, these problems surface after launch as poor user experiences, brand damage, and missed sales that slow adoption and drain revenue.

Solution delivered: Three ways forward

After a thorough line-by-line audit of the HTML/CSS codebase, the GetDevDone expert team documented every finding in an explicit report. Written in plain language, we offered the reasoning behind each issue, best practices, and three concrete ways to fix the build. Each option came with transparent, fixed pricing attached upfront.  

• Option 1 — Full manual rebuild (our traditional production flow). Start over. Every page built by hand, staged development, full QA against the original design, clean handoff at the end. Highest cost, highest certainty.
• Option 2 — AI-assisted, human-supervised. Rebuild with AI, but a senior engineer prompts, checks, and refines every section using our best-practice process. The AI does the heavy lifting; a human makes sure it’s actually right with a targeted 99% visual-to-code accuracy.
• Option 3 — Targeted remediation. Keep the existing codebase. Go through it line by line and fix what’s broken instead of replacing it. Fastest and cheapest, but only as strong as the foundation underneath it.

The client chose the option that gave them the speed of AI-assisted development without relying on AI-generated code alone. A senior engineer reviewed every section against the design and refined it until everything matched. Of the three options, this offered the best balance of quality and cost.  The client knew exactly why: we’d laid out the numbers for all three approaches before they made their decision.

Technologies & tools 

• HTML5: markup structure
• SCSS: scoped component styling
• Figma: UI design and layout specifications
• AI coding assistant: code generation and workflow optimization
• Cross-browser and accessibility QA:  platform compatibility, WCAG compliance validation

Business benefits 

AI produced the first version in just 5–8 hours, but turning that output into production-ready code took an estimated 50–60+ hours of senior engineering work. Much of that effort went into what AI hadn’t gotten right: matching the approved designs, resolving code quality issues, and making the result easier to maintain. Our AI-generated code audit brought those problems to light before launch, helping the agency avoid expensive rework and sustain their client relations. 

That cooperation experience gave the agency the confidence to bring more projects to GetDevDone, including the development of their own website.

• Client trust protected. An independent, page-by-page audit identified discrepancies between the AI-generated code and approved designs before they reached the agency’s client, cutting the risk of complaints, last-minute fixes, and reputational damage.
• Faster delivery, with engineering oversight. A supervised AI workflow reduced the estimated build effort by approximately 25% while maintaining the required quality standards. A senior engineer reviewed and refined every section without a compromise between fast and right.
• Clear options, informed decisions. GetDevDone presented three remediation approaches, with pricing and trade-offs explained upfront. The agency could compare cost, speed, and quality before committing to a delivery model.
• Technical debt caught before launch. Hard-coded values, duplicated styles, and accessibility gaps were identified early, reducing the risk of costly fixes, maintenance issues, and difficult handoffs after delivery.