How to vibe codeLLM Pulse
All-in-one AI search visibility & reputation platform
llmpulse.ai ↗Marketing & SEO SaaS
The verdict: can you vibe code LLM Pulse?
Build a personal prompt tracker clone, but expect significant engineering pain maintaining reliable scrapers against shifting AI UIs.
The dashboard and database CRUD are straightforward with AI coding tools, but the data ingestion pipeline will break constantly. Querying multiple live AI engines at scale without triggering blocks, rate limits, or DOM changes requires resilient queuing and retry logic that consumes weeks of debugging.
Estimated effort: 4-6 weeks of part-time work
What you can't replicate
- Historical data archives of AI search volatility across millions of prompts
- Enterprise white-label agency portal infrastructure and customer trust
Founded
2024
Raised
$0
Team
3
Cheapest paid tier
€49/mo
What LLM Pulse does
Monitor, analyze, and optimize brand visibility across ChatGPT, Perplexity, Gemini, and Google AI search engines.
Core features
- Automated prompt query scheduling across multiple AI engines
- Citation source intelligence and URL extraction
- Sentiment classification and brand reputation scoring
- Competitor share-of-voice benchmarking
- AI traffic analytics integration (GA4 / Plausible)
- Agent analytics (tracking AI crawlers like GPTBot)
The business
Pricing
- Starter€49/mo
- Scale€299/mo
Funding
$0
Pay vs build, cumulative
Break-even at month 1 — after that, every month is money kept.
The hard parts of vibe coding LLM Pulse
- Bypassing bot-detection and rate limits on live consumer-facing AI interfaces
- Reliably parsing messy markdown and anchor tags into canonical citation domains
- Asynchronous scheduling and orchestration of thousands of concurrent AI runs
- Maintained integrations with external web analytics and BI connectors
How to vibecode LLM Pulse
Prerequisites
Node.jsFree
Runtime for Next.js full-stack application development
GitHubFree
Code repository and deployment source control
AI coding tools
Recommended stack
| Frontend | Next.js |
|---|---|
| Backend | Next.js Server Actions / API Routes |
| Database | Neon |
| Auth | better-auth |
| Payments | None |
| Other | Vercel AI SDK, Inngest, PostHog |
Build guide
01Project Scaffolding & Database Schema
Initialize the Next.js application, configure Tailwind CSS, set up better-auth, and define the Neon database schema for projects, prompts, responses, and competitors.
Scaffold a new Next.js project with Tailwind CSS and TypeScript. Integrate better-auth with email/password authentication connected to a Neon PostgreSQL database using Drizzle ORM. Define database tables for `projects`, `prompts`, `competitors`, `runs`, and `responses`. Each run should link a prompt to an AI model, storing the raw response text, extracted brand mentions, sentiment score (-1 to 1), and JSON-serialized citation URLs. Ensure clean separation of schema files and proper foreign key constraints.02Dashboard UI & Project Management
Build the core dashboard interface for managing projects, adding tracked prompts, and setting up competitor lists.
Create a responsive dashboard layout in Next.js with a sidebar navigation and top project selector. Implement pages for managing projects, a prompt tracking table with pagination and filtering, and a competitor management modal. Use shadcn/ui components for tables, dialogs, forms, and status badges. Connect the UI to server actions that perform CRUD operations on projects, prompts, and competitors in the Neon database.03AI Model Orchestration & Response Parsing
Implement the core ingestion pipeline that submits prompts to target AI models via API, extracts brand mentions, parses citation URLs, and scores sentiment.
Build a backend service module using the Vercel AI SDK and direct API clients for OpenAI (ChatGPT), Anthropic (Claude), and Google Gemini. The service must accept a prompt and target model, execute the query, and return the raw text along with search grounding metadata or markdown anchor links. Implement a parsing utility that extracts all citation URLs, normalizes them to canonical domains, calculates share of voice, and runs a lightweight LLM call to classify brand sentiment on a 5-point scale. Store the results in the `runs` and `responses` tables.04Asynchronous Scheduling & Background Jobs
Set up Inngest to handle weekly and scheduled recurring prompt runs across multiple AI engines reliably without timing out serverless functions.
Integrate Inngest into the Next.js app to handle background job execution. Create a durable background workflow function that triggers weekly (or on demand), iterates over all active projects and their tracked prompts, fans out execution across configured AI models using the orchestration service from step 3, and saves results to the database. Implement robust error handling, rate-limit backoff logic, and step-level retries for each AI provider call.05Analytics & Visualization Views
Build charts and reporting views to display brand visibility scores, share of voice over time, citation sources, and sentiment breakdowns.
Create a comprehensive analytics view for projects using Recharts. Include line charts showing brand visibility and sentiment trends over time, bar charts for competitor share of voice comparison, and a data table summarizing top trusted citation domains and their occurrence counts. Add date-range filters and export-to-CSV functionality for the reporting data.
Cost vs paying for LLM Pulse
What will you build it with?
Starting total with Claude Code$0 one-time
Starting costs (one-time)
- Custom domain$12/yr
Total~$12 one-time
Ongoing costs (monthly)
- AI API Usage (OpenAI/Anthropic/Gemini for prompt tracking)~$15-30/mo
- Vercel & Neon Hosting$0-20/mo
Total~$30-50/mo
Paying for LLM Pulse
€299/mo (Scale tier)
Your time to build
40-60 hours
AI tool credits
$20/mo (Claude Pro)
Break-even
1 month
Own LLM Pulse? Wear the score
Put this badge on your site or README — it links back to this report.
<a href="https://vibeityourself.com/app/llmpulse"><img src="https://vibeityourself.com/badge/llmpulse" alt="LLM Pulse vibe-codeability score" /></a>[](https://vibeityourself.com/app/llmpulse)Vibe code LLM Pulse: FAQ
- Can you vibe code LLM Pulse yourself?
- Serious undertaking — 45/100 vibecodeable. Build a personal prompt tracker clone, but expect significant engineering pain maintaining reliable scrapers against shifting AI UIs.
- How long does it take to vibe code LLM Pulse?
- 4-6 weeks of part-time work — roughly 40-60 hours of hands-on time with an AI coding agent.
- How do you build your own LLM Pulse?
- Scoped to personal use: Next.js on the front, Next.js Server Actions / API Routes behind it, Neon for data. Follow the 5-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
- How do you code your own LLM Pulse without being an expert?
- Use an AI coding tool (Claude Code or Cursor) and work in small steps: scaffold, data model, core screens, then deploy. Realistic effort: 4-6 weeks of part-time work. The prompts on this page are written so the AI does the heavy lifting.
- How much does it cost to vibe code LLM Pulse instead of paying?
- About ~$12 one-time to start and ~$30-50/mo to run, versus €299/mo (Scale tier) for LLM Pulse. Break-even: 1 month.
- What stack should you use to vibe code LLM Pulse?
- Next.js; Next.js Server Actions / API Routes; Neon; plus Vercel AI SDK, Inngest, PostHog.
Methodology
This report was generated by VibeItYourself's standard pipeline: we scrape llmpulse.ai (content, branding, screenshot), deep-research the company with AI + web search (pricing, funding, team, engineering complexity), then score rebuild feasibility 0–100 against the same rubric used for every app — scoped to a personal-use clone, not a competing business. How scoring works. Verdicts are honest by design: what you can't replicate is listed above.
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