Built by Working Model
AI Search Validator
AI is changing how brands get found. We built the tool to measure how ready yours is.
Google isn't the only door anymore. As large language models, voice interfaces, and AI-powered search features take over more of the discovery process, the rules for being found are being rewritten. Most brands don't know where they stand in this new landscape — because until now, there was no standardised way to measure it.
Working Model built the AI Search Validator to change that. It's a web application that analyses any website against the metrics that matter for LLM and AI search visibility — and returns a scored report with specific, prioritised recommendations across four categories: AI Optimisation, Content Quality, Technical, and Metadata.
Project Name: AI Search Validator
Live Site: https://aio.workingmodel.co
Developer: Working Model Inc
Goal: To analyze a website against key metrics for LLM/AI-driven search optimization and content quality.
Key Challenges & Objectives
- Defining "AI Search Optimization": The primary challenge was translating the nebulous concept of "LLM-friendly content" into concrete, measurable metrics (e.g.,
robots.txtdirectives for AI bots,llm.txtdetection, and readability scoring like Flesch-Kincaid). - Scalable, Cost-Effective Architecture: The analysis process involves fetching and parsing external URLs, which required a robust, scalable, and pay-per-use backend structure to manage unpredictable traffic spikes.
- Performance and User Experience: Deliver a fast, responsive user interface that can handle the complexity of displaying detailed, multi-category analysis results clearly.
Technical Solution & Architecture
The architecture was designed using a modern, serverless, and decoupled approach to maximize efficiency and minimize operational costs.
1. Serverless Backend for Core Logic
- Technology: AWS Lambda (Node.js 20.x) and API Gateway.
- Function: The analysis logic is encapsulated in a single
ai-search-analyzerLambda function. This approach provides infinite scaling for the website analysis request and cost-efficiency (paying only for execution time). - Process: The Lambda receives a URL via the API Gateway, uses Cheerio for efficient HTML parsing, executes all category checks (Robots.txt, Schema.org, Content Quality), and returns a structured JSON response with scores and recommendations.
2. High-Performance Frontend Deployment
- Technology: Next.js 16 (Static Export), React 19, TypeScript, and Tailwind CSS.
- Deployment: The built application is deployed as a static site to AWS S3 and served globally via CloudFront CDN.
- Benefit: Serving the frontend from S3/CloudFront eliminates the need for a persistent server, drastically reducing latency and load times while simplifying infrastructure maintenance.
3. Core Analysis Logic & Tech Stack
| Feature | Tech/Method | Purpose |
|---|---|---|
| Parsing | Cheerio | Fast, server-side parsing of HTML content in the Lambda function. |
| Readability | Flesch-Kincaid Algorithm | Calculating a numeric score to ensure content is simple and concise for LLM ingestion. |
| AI Bot Control | robots.txt check, llm.txt detection | Verifying proper directives to manage AI crawler access (e.g., GPTBot, CCBot). |
| Styling & UI | Tailwind CSS & Working Model Design System | Rapid, utility-first styling for a beautiful, responsive, and brand-aligned user interface. |
Outcomes and Results
The AI Search Optimization Checker successfully delivered a cutting-edge analysis tool:
- Comprehensive Scoring: Websites receive scores across four critical categories: AI Optimization, Content Quality, Technical, and Metadata.
- Actionable Intelligence: Each finding is tied to a prioritized recommendation, allowing users to immediately understand and address critical issues.
- Scalability: The serverless architecture ensures the application can handle high volumes of analysis requests without requiring manual resource provisioning or complex load balancing.
- Maintainability: The clear project structure, separation of concerns (Frontend/Backend/Infrastructure scripts), and TypeScript usage promote long-term code health and easier contribution.
The project demonstrates Working Model's ability to quickly identify emerging technology trends (AI Search) and deliver a high-quality, architecturally sound application using best-in-class serverless and modern frontend technologies.
Let's Build Something That Works.
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