# How to Vibe Code Your Own CueScout (and Stop Paying for It)

> See what AI reads about your category, and get cited

- Site: https://cuescout.com
- Category: B2B Marketing & AI Visibility Analytics
- Platforms: Web app
- Verdict: **Solid side project** (68/100 vibecodeable)
- Estimated effort: 2-3 weeks part-time

## Verdict

Build a personal subset for your own SaaS, but keep paying if you want agency-scale multi-client tracking and ongoing maintenance against shifting AI crawler walls.

Building a personal clone of CueScout is a rewarding project that combines web scraping, LLM API orchestration, and structured data analysis. The primary friction points will be maintaining robust scrapers for discussion boards like Reddit and Hacker News without getting blocked, and managing the cost and rate limits of frequent AI answer engine checks. While a solo developer can code the core scanning and reporting loop over a few weekends, dealing with the fragile underlying APIs and rate-limiting will require ongoing maintenance.

### What you can't replicate

- Historical category citation datasets built up over months
- Maintained proprietary crawler proxies for resilient source extraction

## What it does

A specialized B2B marketing and GEO (Generative Engine Optimization) SaaS platform that tracks AI answer engine citations, analyzes public discussion sources like Reddit and Hacker News, checks Google search ranking overlap, and outputs a dated 30-day content publishing plan.

### Core features

- Multi-engine AI visibility checking (Perplexity and ChatGPT)
- Public discussion source scraping and intent scoring (Reddit, Hacker News, Quora)
- Cited URL collection, extraction, and host domain leaderboard aggregation
- Deterministic site readiness audits (robots.txt crawler inspection, JSON-LD schema validation)
- Google search rank overlap lookup for cited URLs
- Dated 30-day content publishing roadmap generation
- HMAC-signed login-free shareable Opportunity Reports
- Keyword tuning and relevance feedback loop

## The business

### Pricing

- Founder Pack: $19 — One-time 30-day trial tier giving the full loop on one product with no recurring charge.
- Basic: $43/mo — Billed quarterly at $129. For testing whether buyer-question research and AI visibility work for your product.
- Growth: $87/mo — Billed quarterly at $259. For consultants and founders publishing against AI search every week.
- Agency: $217/mo — Billed quarterly at $649. For agencies running AI-visibility retainers across a roster of clients.

Founded 2026.
Team size: Solo / Indie bootstrap.

## The hard parts

- Bypassing aggressive rate-limits and schema shifts when querying AI answer engines programmatically
- Handling public forum rate-limits and reliable thread scraping without breaking community rules
- Implementing deterministic site readiness checks that correctly parse robots.txt rules for individual AI user-agents
- Managing complex search overlap lookups efficiently across large question libraries

## How to vibe code CueScout

### Prerequisites

- Node.js (free): Required for running the Next.js framework and TypeScript tooling.
- GitHub (free): Source control and seamless deployment integration.
- Perplexity API / OpenAI API (Usage-based): Required for querying AI visibility checks and generating content drafts.

### Recommended AI tools

- Claude Code: Ideal terminal agent for scaffolding the full-stack Next.js app, setting up database schemas, and writing complex scraping and parsing routines.
- Cursor: Great for iterative frontend component development on the dashboard views and report tables.

### Stack

- Frontend: Next.js with Tailwind CSS and shadcn/ui components
- Backend: Next.js Server Actions and API Routes
- Database: Turso (SQLite at the edge) via Drizzle ORM
- Auth: better-auth
- Payments: None (Personal use clone)
- Other: Vercel AI SDK for LLM interactions, Firecrawl for web scraping and markdown extraction, Resend for transactional email alerts

### Hosting

- Cloudflare (Hosting the full-stack Next.js application, edge routes, and Turso database connection.): $0-5/mo

### Build guide

1. **Project Scaffolding & Database Schema** — Initialize the Next.js project with Tailwind CSS, configure Drizzle ORM with Turso SQLite, and establish the data models for products, scans, citation sources, and writing plans.

```
Scaffold a new Next.js 16 application using App Router, TypeScript, and Tailwind CSS. Initialize Drizzle ORM configured for Turso SQLite. Create database tables for 'products' (id, name, description, category, user_id), 'scans' (id, product_id, status, created_at, raw_payload), 'citation_sources' (id, scan_id, url, host, classification, verdict, rank), 'buyer_questions' (id, scan_id, question, intent_score, count), and 'writing_plans' (id, product_id, title, target_date, status). Include basic connection utilities and environment variable validations.
```

2. **AI Visibility & Engine Checker Module** — Build backend routines that prompt Perplexity and ChatGPT models with target buyer questions, capturing answers and structured citation blocks.

```
Build a TypeScript service module using the Vercel AI SDK that executes visibility checks against Perplexity and OpenAI models. Given a product description and a list of generated buyer questions, query the LLMs, extract the response text, and parse the structured citation URLs returned in the metadata. Save the resulting check records and extracted citation URLs into the database linked to a scan ID. Handle API errors, timeouts, and rate limits gracefully with retry logic.
```

3. **Discussion Scraping & Intent Clustering** — Implement scraping routines for public discussion platforms (Reddit, Hacker News) or integrate Firecrawl/Exa to harvest repeating buyer intent questions.

```
Implement a discussion harvesting service using Firecrawl and custom RSS/API fetchers to search public forums like Reddit and Hacker News for category keywords. Process the retrieved threads with an LLM call to extract repeating buyer questions, filter out irrelevant chit-chat, assign an intent score, and store them in the 'buyer_questions' table. Group duplicate questions and track their occurrence frequencies across scans.
```

4. **Site Readiness & Rank Overlap Checkers** — Develop deterministic site readiness analyzers to inspect robots.txt rules for AI crawlers, validate JSON-LD structured schema, and check search engine rank overlap.

```
Create a deterministic site-auditing engine that fetches a target website's robots.txt to inspect crawler access rules for bots like GPTBot, ClaudeBot, and PerplexityBot. Parse the homepage HTML to validate JSON-LD structured schema (Organization, FAQPage) and check for server-side rendering versus client-side hydration. Additionally, implement a search rank overlap checker that queries Brave Search or Exa to find whether cited URLs rank in organic search results for their associated buyer questions.
```

5. **Dashboard UI & Domain Leaderboard** — Build the main analytics dashboard displaying GEO scores, domain leaderboards, citation source classifications, and scan debug breakdowns.

```
Build a responsive Next.js dashboard using Tailwind CSS and shadcn/ui components. Create views for the main workspace, displaying a consolidated GEO score, visibility metrics, and a Domain Leaderboard that aggregates host citation shares. Include a Citation Sources tab with classification badges (own-it, earn-it, get-listed, competitor-owned, blocked) and a scan debugging panel showing exact queries run, sources fetched, and rejection counts.
```

6. **Writing Plan & Shareable Opportunity Reports** — Implement the 30-day content publishing plan generator and HMAC-signed login-free shareable public report pages.

```
Build a content planning engine that converts clustered buyer questions into a dated 30-day writing roadmap with weekly targets and AI page draft generators using the Vercel AI SDK. Then, implement a cryptographically signed HMAC token authentication mechanism for shareable Opportunity Reports, allowing external clients or prospects to view a live, read-only report at `/report/[token]` without requiring user authentication or exposing database IDs.
```

### Cost vs paying

**Starting costs (one-time):**

- Domain name (optional): $12 one-time
- AI API starting credits (Perplexity / OpenAI): $10 one-time
- Total: ~$22 one-time

**Ongoing costs (monthly):**

- Cloudflare / Turso / Vercel hosting: $0-5/mo
- Perplexity & LLM API usage for scans: $5-15/mo
- Total: ~$10-20/mo

- Paying for the SaaS instead: $43/mo (Basic plan)
- Build time: 25-35 hours
- AI tool credits: $20 (Claude Pro / Cursor)
- Break-even: N/A (Built for personal use and learning)

## Sources

- [CueScout Main Website & Home Page](https://cuescout.com)
- [CueScout Pricing & Billing Documentation](https://cuescout.com/pricing)
- [CueScout Features: Citation Sources & Domain Leaderboards](https://cuescout.com/features/citation-sources)
- [CueScout Features: AI Readiness Checks & Technical Architecture Insights](https://cuescout.com/features/readiness-checks)