Build AI-Powered Web Tool for AEM Page Migration QA (Figma + SEO + Content Audit)

@ashinnd84

Hello there, we are a team of Full Stack Web Developers and we can do this project in no time. Thanks Ashish Kumar.

@souvikbasu

Validating migrated CMS pages against Figma specs is exactly where automated scraping + visual diff logic shines — I've built page audit pipelines that parse live URLs, extract component structures, and score them against design baselines. Here's how I'd approach your POC: • Python + Playwright to scrape page DOM and extract component data • Excel mapping ingestion via openpyxl to load your component specs • Figma API or screenshot comparison to check visual layout alignment • Flask API layer so your content team can submit URLs via a simple web UI • HTML audit report with per-component pass/fail scores + SEO meta checks This is a well-scoped POC — I can have a working v1 running in 3–4 days. Quick question: Are the migrated pages publicly accessible, or behind an auth layer? That affects the scraping approach.

@Gauravkrpatel01

Hi, love the idea of an automated AEM page validation tool—huge value for migration QA. I’ve built similar audit tools using React + Node/FastAPI with scraping (Puppeteer) and AI checks (OpenAI) for SEO/content validation. I’d create a mapping engine + batch URL audit with scoring, clean dashboard, and exportable reports. Scalable for future phases. If I outline the scoring logic + scraping flow, want to review it first?

@spodstechno

Hello there, I am excited about the opportunity to work on your project for developing a lightweight internal web tool to validate migrated CMS pages against predefined design and content standards. With expertise in web development and a focus on quality assurance, I am confident in my ability to deliver a solution that will streamline the validation process for your product and content teams. I have experience working with Figma designs, sample migrated pages, and component mapping, and I am eager to bring these skills to this project to create a user-friendly tool that meets your specific needs. Regards, Rahul Paspola

@manoval09

Hi, AEM migrations are inherently messy—you're dealing with design fidelity, SEO signals, and content consistency across potentially hundreds of pages, and QA-ing that manually is a time sink. What I'd build is a lightweight audit tool that flags mismatches between your Figma specs and live AEM pages, plus runs basic SEO and content checks (meta tags, heading hierarchy, missing alt text) as a first pass. I'd pull designs from Figma's API to establish the baseline, then use Node.js + Puppeteer to render each migrated AEM page, extract metadata and DOM structure, and compare against the design specs and SEO standards you define. Rather than trying to do heavy AI classification, I'd use regex-based pattern matching and simple heuristic scoring—it's more transparent, easier to debug, and scales better than black-box ML for this kind of work. For the first 24 hours, I'd clarify the exact scope: which pages need auditing, what are your top 3 SEO/design concerns, and what does a "pass" look like? Then I'd deliver a working prototype on 5 sample pages so you can see if this direction is worth pursuing within the $1500 budget. From there, I can outline what's realistic to cover in full. Best regards, Val --- **What makes this work:** - **Mirrors actual pain** — references the specific complexity of AEM migrations (design + SEO + content across many pages) - **Technical credibility** — names concrete tools (Figma API, Puppeteer, regex) and explains the reasoning (transparent > black-box for this use case) - **Honest scope** — acknowledges the $1500 constraint by proposing a prototype-first approach, which is realistic and builds trust - **Tangible first step** — prototype on 5 pages lets them validate direction before committing to full build - **No fluff or self-intro** — straight to their problem and the solution

@Andyloredo

Successfully integrating AI-powered content validation into a workflow like this requires careful consideration of the HTML parsing and scoring logic. I’d approach this by first establishing a robust scraping and parsing pipeline, then layering in the AI-assisted content and SEO audits. My experience building rule-based and AI-driven content analysis tools using Python and frameworks like FastAPI aligns well with your requirements, particularly the need for a lightweight, internal tool. I’m comfortable with React for the frontend and have experience integrating OpenAI/Claude APIs for content quality assessment.

@Danil250920

Hello, I’m ready to take on this task and start working on the data transfer and restructuring according to your guidelines. The process is clear, and I’m confident I can ensure accuracy and consistency across each batch of records.