# Build brief — a focused alternative to CueScout > **Verdict:** Partly, if you narrow it · **Buildability:** 63/100 · **Category:** SEO Marketing > **Source:** https://www.canitbevibecoded.com/cuescout > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from CueScout. Verify current pricing and capabilities before acting. ## Context **CueScout** — Tracks AI visibility on Perplexity and ChatGPT, mines Reddit and Hacker News for buyer questions, and turns the gaps into a dated writing plan. It currently costs $49/mo. The visibility-check loop is the straightforward to build half: generate buyer questions, run them through Perplexity and ChatGPT, detect brand and competitor mentions, normalize citations, and score a GEO number. That much is close to what a $49/mo plan actually ships, since Basic only covers one engine. What does not fit in a prompt is the continuous Reddit and Hacker News scan that mines buyer questions from real threads instead of guessing them, the writing-plan-to-draft loop that turns a score into dated work, and weeks of trend history without which one run tells you almost nothing. This brief describes a focused, single-operator replacement for the part of CueScout that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest. ## What you are building Generate buyer questions for a product, run them through Perplexity and ChatGPT, detect brand and competitor mentions, collect citations, and score a basic GEO number. - Automate a bounded research or reporting workflow using permitted data sources. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - Durable per-run storage. - A scheduler for repeat runs. ### Data and integrations - Perplexity API key. - OpenAI API key. Each of these needs a real account, credential, or quota. Set them up before writing feature code. ### Non-functional - Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support. - Security: server-side secrets, validated input, and no credentials in the client bundle. - Reliability: retries with backoff on external calls, and a clear failure state when a provider is down. - Portability: the operator can export their data and leave without losing it. ## Implementation brief Build me a local AI buyer-question visibility tracker for one product. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - product.json holds my product name, aliases, domain, and up to 5 competitor names. - `track questions` generates 20 buyer-intent questions from the product name and a one-paragraph description, or accepts a JSON list I supply. - `track run` sends every question through Perplexity's API and OpenAI's Chat Completions API with web search enabled where supported. Keys live in .env. - Store one immutable row per run, question, and provider: raw answer, cited URLs, model id, and error text. Never overwrite a prior run. - Retry transient failures twice with backoff; keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively against the alias list. - Normalize citation URLs to hostname plus canonical path, strip tracking parameters, then compute a top-sources table and an owned-domain citation rate. - Compute one GEO score per run: mention rate times citation rate, shown with the raw counts behind it, never just the number. - `track serve` renders visibility over time by provider, share of voice against each competitor, and the sources table. - `track export` writes questions, answers, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and the GEO score calculation. - Out of scope: Reddit/Hacker News monitoring, Google rank tracking, writing-plan or content generation, hosted sharing, teams, and billing. - README: setup, per-run API cost estimate, and a cron line for a daily run. ## Delivery standard - Inspect the repository first, then write a short implementation plan before writing code. - Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data. - Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations. - Include responsive layouts plus genuine empty, loading, success, validation, and failure states. - Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data. - Add structured logs around every external call and return actionable errors without leaking sensitive details. - Write unit tests for the core logic and one automated test of the main user journey. - Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations. ## Acceptance criteria - [ ] A clean install starts the app using only the README and .env.example. - [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable. - [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state. - [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control. - [ ] Tests, type checking, linting, and a production build all pass with no ignored failures. - [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified. ## Non-goals Do not build these, and do not claim to have replaced them: - The continuous Reddit and Hacker News scan and buyer-question clustering, which finds where buyers are already asking instead of guessing questions to feed a model. - Google rank badges on matched threads, sourced from a paid search API. - The writing-plan-to-draft loop that turns a gap into a dated 30-day plan and AI-ready page drafts, not just a dashboard number. - Weeks of trend history and a competitor 'what moved' digest; a single run is mostly noise. - A hosted, shareable report link you can hand a client without exposing your own infra. ## What you still own after launch - Secure credentials, rotate secrets, and handle provider rate limits. - Run migrations, backups, restores, and dependency updates. - Test the critical journey after every model, API, or hosting change. - Monitor failures and fix the edge cases a first prompt will miss. - Maintain every third-party integration as APIs and OAuth rules change. ## Risk **Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost. Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet. ## Existing alternatives Before building, compare these checked options: - [Elmo](https://www.elmohq.com/) — Covers the visibility-check half well; it has no Reddit/HN buyer-thread mining and no writing-plan or draft generation on top of the scores ## Prior art Working open-source software you can read, fork, or borrow from before starting: - [Elmo](https://github.com/elmohq/elmo) — MIT-licensed self-hosted AEO/GEO tracker: runs prompts across the major answer engines and records mentions, competitors, and cited sources --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/cuescout