100 Questions

Build me a local AI visibility benchmark for one brand. Requirements: - Use Node 22, TypeScript, official provider SDKs, SQLite, and a CLI. - `benchmark --domain example.com --description "..."` creates one immutable run. - Generate 25 buyer questions from the domain and description, or accept a JSON question file. - Ask the exact same questions through OpenAI, Anthropic, Gemini, and xAI. - Use each provider's supported web-search or grounding tool; keys live only in `.env`. - Limit concurrency per provider, retry transient failures, and preserve failed cells in the report. - Store prompts, raw answers, citations, timestamps, model ids, and errors in SQLite. - Detect exact and case-insensitive brand mentions; allow aliases in a config file. - Extract named competitors with one structured LLM pass after all answers are stored. - Normalize citation URLs by hostname, canonical URL, and stripped tracking parameters. - Compute visibility by provider, answer coverage, owned-domain citation rate, and top sources. - Show missed questions where competitors appear but the target brand does not. - Render a self-contained static HTML report with filters and expandable raw evidence. - Export questions, answer metrics, competitors, and citations as CSV files. - Every aggregate metric must link back to the answer rows used to calculate it. - Out of scope: accounts, billing, teams, scheduled monitoring, and recommendation generation. - Include fixture-based tests for mention detection, URL normalization, and metric calculations. - README: setup, provider-specific grounding caveats, estimated API cost, and exact run commands.