AWS Open-Sources AI Search Citation Analysis System: Track Brand Exposure in AI Search Engines

AWS has officially open-sourced the Citation Analysis System project, which aims to help enterprises track their brand's exposure and competitive landscape in AI search engines like ChatGPT, Perplexity, Gemini, and Claude. Built on Amazon Bedrock, Step Functions, and React, this system uses a serverless architecture to automatically query multiple AI providers, capture citation sources, and present key data via a visual dashboard.

As AI assistants like ChatGPT and Perplexity become primary entry points for information acquisition, the logic of traditional SEO has undergone fundamental changes: brands need to focus on citations and recommendations in AI responses, rather than just Google search rankings. AWS's open-source project addresses this emerging need by providing a complete solution.

This system uses a serverless architecture, with core features including:

• Multi-provider querying: Send keyword queries to OpenAI, Perplexity, Gemini, and Claude
• Citation capture and deduplication: Extract citation links and normalize/deduplicate them
• Web crawling: Use Bedrock AgentCore to crawl dynamic page content
• Data storage and analysis: Store results in DynamoDB for analysis
• Visual dashboard: React-based real-time display of exposure, competitive comparison, etc. The project uses the MIT-0 license, allowing free use and modification.

The system uses Step Functions to orchestrate workflows and CDK to deploy infrastructure. Core components:

1. Search Lambda: Send queries to various AI providers (supports models like GPT-5 mini, Sonar, etc.)
2. Deduplication Lambda: URL normalization and deduplication
3. Crawler Lambda: Bedrock AgentCore for dynamic page crawling
4. Step Functions: Orchestrate the process (parallel query → collect citations → deduplicate → crawl → generate summary)
5. DynamoDB: Store results and history
6. Secrets Manager: Securely store API keys
7. React frontend: SPA dashboard hosted on S3 + CloudFront

Deployment methods:

• Quick deployment: ./scripts/deploy.sh (automatically checks dependencies, builds, and deploys)
• Manual deployment: Two phases (infrastructure → frontend build and upload) Usage steps:

1. Configure AI provider API keys (paid keys required)
2. Set up brand and competitor tracking
3. Add analysis keywords
4. (Optional) Define query prompt templates
5. Run analysis (3-5 minutes per keyword)

Core dashboard features:

• Real-time statistics: Total searches/citations, distribution across AI providers
• Brand exposure: Share of voice, competitive comparison, 30-day trends
• Mention details: Sentiment analysis, ranking position, source URL
• Citation gap: Identify authoritative sources that do not cite your brand
• Content studio: Generate content briefs using Claude (based on gap analysis)

Target users:

1. Brand marketing teams: Monitor exposure and identify content opportunities
2. SEO/content strategists: Expand to AI search optimization (AIO)
3. Market analysts: Track industry trends and AI recommendation preferences
4. Architects: Learn AWS serverless architecture patterns Practical value: Quantify AI search visibility, competitive intelligence, trend tracking, etc.

Limitations:

• External AI API calls incur costs (suggested budget of $5-10 per provider)
• Sample code requires additional security review before production use
• Data privacy compliance requirements need to be evaluated Summary: This project provides a practical starting point for brand monitoring in the AI search era, helping teams build AIO capabilities. Project open-source address: https://github.com/aws-samples/sample-llm-search-citation-analysis-with-amazon-bedrock