How to vibe code100 Questions
AI visibility audit and benchmarking tool
100questionsai.com ↗SaaS / AI Marketing
The verdict: can you vibe code 100 Questions?
You can build this personal clone over a weekend using Next.js and LLM APIs, making the paid reports unnecessary unless you want pre-configured agency templates.
The core engineering challenge is writing robust async orchestration scripts that call four separate LLM providers with web search enabled, parsing their responses to verify valid web citations, and computing basic mathematical ratios for share of voice and visibility. An AI coding agent can scaffold the database models, dashboard UI, and API orchestration loops in an afternoon. The primary friction points are handling upstream API rate limits and ensuring your regex/parsing logic correctly extracts grounding URLs from varying provider response payloads.
Estimated effort: 1-2 weekends
What you can't replicate
- The exact commercial dataset and brand positioning of the original tool
Founded
2025
Raised
—
Team
Solo-founder
Cheapest paid tier
$9
What 100 Questions does
An AI visibility audit and benchmarking tool that analyzes how businesses appear in web-grounded LLM responses across OpenAI, Claude, Gemini, and Grok.
Core features
- Multi-provider API orchestration (OpenAI, Anthropic, Google, xAI)
- Parameterized question template generation (20 neutral + 5 diagnostic prompts)
- Web grounding verification (filtering out ungrounded model outputs)
- Share of voice and competitor comparison analytics
- Citation domain matching and occurrence tracking
- Prioritized AI visibility action plan generation
- 30-day rolling report snapshots and evidence retention
- PDF and CSV data export engine
The business
Pricing
- First Benchmark$9
- Single Benchmark$15
- Three Benchmarks Pack$39
- Ten Benchmarks Pack$99
Funding
Unknown / bootstrapped
Pay vs build, cumulative
Break-even at month 3 — after that, every month is money kept.
The hard parts of vibe coding 100 Questions
- Handling concurrent API rate limits and structural JSON schema variances across 4 providers
- Reliably parsing and validating web search grounding URLs returned inside model responses
- Aggregating deterministic scoring metrics from unstructured LLM text blocks
How to vibecode 100 Questions
Prerequisites
Node.jsfree
Runtime environment for Next.js and TypeScript orchestration scripts
GitHubfree
Source code repository and deployment integration
API KeysPay-as-you-go
Access keys for OpenAI, Anthropic, Gemini, and xAI model endpoints
AI coding tools
Recommended stack
| Frontend | Next.js with Tailwind CSS |
|---|---|
| Backend | Next.js App Router API Routes |
| Database | Turso (SQLite at the edge) |
| Auth | better-auth |
| Payments | None needed for personal use |
| Other | Vercel AI SDK, Lucide React |
Build guide
01Scaffold Project and Database Schema
Initialize a Next.js project with Tailwind CSS and configure Turso with Drizzle ORM to store audits, questions, model responses, and citation evidence.
Create a new Next.js TypeScript project using App Router and Tailwind CSS. Configure Drizzle ORM to connect to a Turso SQLite database. Set up database tables for 'audits' (id, target_name, domain, category, competitors, created_at), 'questions' (id, audit_id, prompt_text, type), and 'provider_responses' (id, audit_id, question_id, provider, raw_text, citations_json, is_grounded, brand_mentioned, prominence_score). Ensure all foreign keys and indexes are correctly defined for fast aggregation queries.02Implement Multi-Provider LLM Orchestration Engine
Build an asynchronous service layer that dispatches prompt sets concurrently across OpenAI, Anthropic, Gemini, and xAI with web grounding enabled.
Create a TypeScript orchestration service that accepts a list of 25 questions (20 neutral discovery, 5 diagnostic) and executes them concurrently across OpenAI (GPT-4o/5 mini), Anthropic (Claude Sonnet), Gemini (Flash Lite with search grounding), and xAI (Grok). Ensure each provider request enables native web search tooling or grounding parameters. Catch request timeouts and API errors gracefully without failing the entire batch, returning structured payloads containing raw response text and raw citation URLs.03Build Grounding Verification and Scoring Algorithm
Write deterministic parsing functions to verify citation URLs, filter ungrounded responses, and calculate visibility, prominence, and share of voice.
Implement scoring and validation logic in TypeScript. For each model response, verify that at least one valid HTTP/HTTPS URL is returned in the citation payload; flag 'is_grounded = false' otherwise. Analyze the response text using target brand aliases and competitor lists to compute: (1) visibility percentage (mentions in neutral discovery answers), (2) prominence weighting (headline vs body mention), (3) competitor share of voice ratios, and (4) claimed domain citation frequency. Save these computed metrics to the database.04Construct Audit Configuration and Trigger UI
Develop the front-end form allowing users to submit their target brand, canonical domain, category description, market locale, and competitors.
Build a clean Next.js dashboard page with a form to initiate a new audit run. The form must accept: Subject Name, Canonical Domain, Category/Use-case Description, Market/Locale, and a comma-separated list of Competitors. On submission, show a loading state with progress indicators while calling the backend orchestration API. Store the input parameters and frozen timestamp in the audit record.05Build Report Dashboard and Evidence Inspector
Design a client-ready audit dashboard displaying overall metrics, model breakdown charts, missed questions, and source citation evidence.
Create a comprehensive audit report view in Next.js displaying summary KPI cards (Discovery Visibility, Owned Citations, Model Breakdown across OpenAI, Claude, Gemini, Grok). Include an expandable evidence inspector table showing each of the 25 questions, what each model answered, whether citations matched the claimed domain, and which competitor appeared instead. Ensure the UI matches a professional, clean SaaS aesthetic using Tailwind CSS and Lucide icons.06Implement Action Plan Generator and Export Tools
Synthesize missed citation evidence into five prioritized GEO/AEO recommendations and add JSON/CSV export capabilities.
Add an action plan generation module that analyzes low-visibility questions and missing competitor citations to output five prioritized technical or content recommendations for GEO optimization. Implement client-side export functions that serialize the audit report and retained evidence into clean CSV and downloadable PDF-ready formats.
Cost vs paying for 100 Questions
What will you build it with?
Starting total with Cursor$0 one-time
Starting costs (one-time)
- AI Provider API Credits~$5 (OpenAI/Anthropic/Google/xAI test calls)
Total~$25 one-time
Ongoing costs (monthly)
- API usage per audit run~$0.10 - $0.50 per run depending on model calls
TotalUsage-based (~$2-5/mo)
Paying for 100 Questions
$9 - $15 per run
Your time to build
10-15 hours
AI tool credits
$20 (Cursor Pro or Claude Code usage)
Break-even
Immediate if running multiple audits
Vibe code 100 Questions: FAQ
- Can you vibe code 100 Questions yourself?
- Weekend build — 85/100 vibecodeable. You can build this personal clone over a weekend using Next.js and LLM APIs, making the paid reports unnecessary unless you want pre-configured agency templates.
- How long does it take to vibe code 100 Questions?
- 1-2 weekends — roughly 10-15 hours of hands-on time with an AI coding agent.
- How do you build your own 100 Questions?
- Scoped to personal use: Next.js with Tailwind CSS on the front, Next.js App Router API Routes behind it, Turso (SQLite at the edge) for data. Follow the 6-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
- How do you code your own 100 Questions without being an expert?
- Use an AI coding tool (Cursor or Claude Code) and work in small steps: scaffold, data model, core screens, then deploy. Realistic effort: 1-2 weekends. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code 100 Questions instead of paying?
- About ~$25 one-time to start and Usage-based (~$2-5/mo) to run, versus $9 - $15 per run for 100 Questions. Break-even: Immediate if running multiple audits.
- What stack should you use to vibe code 100 Questions?
- Next.js with Tailwind CSS; Next.js App Router API Routes; Turso (SQLite at the edge); plus Vercel AI SDK, Lucide React.