Can BrandGEO be vibe coded?
Audits how ChatGPT, Claude, Gemini, Grok, and DeepSeek describe your brand, with white-label reports for agencies
Running a fixed battery of brand questions through five model APIs and scoring the answers with a second LLM pass is a genuine focused build, and for one brand it answers the headline question: what does AI say about us. The gaps are the ones every tracker in this category shares. API answers approximate but do not equal the consumer apps, a score with no trend history behind it is a screenshot rather than a signal, and a rubric only becomes comparable after it has scored many brands.
Jump to the build brief ↓Legacy-calibrated assessment
Checked Aug 2026
What you pay today, before any DIY hosting
medium editorial confidence
Tracked separately from the pricing check
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- Send a fixed set of brand questions through the OpenAI, Anthropic, Gemini, xAI, and DeepSeek APIs, score each answer against a six-dimension rubric with a second LLM pass, store every run, render a Markdown or PDF report.
- Automate a bounded research or reporting workflow using permitted data sources.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- white-label PDF reports an agency can hand to a client
- weekly monitoring that keeps running when nobody is thinking about it
- a rubric calibrated across many brands, so scores are comparable
- competitor benchmarks per brand
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
- Reliability at the vendor's scale is an operations problem, not a prompt.
Build, switch, or keep paying
Narrower, with trade-offs
Send a fixed set of brand questions through the OpenAI, Anthropic, Gemini, xAI, and DeepSeek APIs, score each answer against a six-dimension rubric with a second LLM pass, store every run, render a Markdown or PDF report.
Use the build brief ↓1 checked option
- Elmo ↗A self-hosted AI visibility dashboard that runs your prompts across the major engines and records mentions and citations; the white-label reporting is the part you keep paying for.
$79/mo
Agencies are the tell: they pay for a report with someone else's methodology behind it that they can white-label and bill for, plus monitoring and trend history someone else keeps alive. A founder auditing one brand once is exactly who the free audit and a DIY script are for.
Visit BrandGEO ↗Why people still pay
Agencies are the tell: they pay for a report with someone else's methodology behind it that they can white-label and bill for, plus monitoring and trend history someone else keeps alive. A founder auditing one brand once is exactly who the free audit and a DIY script are for.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
Reliability at the vendor's scale is an operations problem, not a prompt.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to BrandGEO
Context
BrandGEO — Audits how ChatGPT, Claude, Gemini, Grok, and DeepSeek describe your brand, with white-label reports for agencies. It currently costs $79/mo.
Running a fixed battery of brand questions through five model APIs and scoring the answers with a second LLM pass is a genuine focused build, and for one brand it answers the headline question: what does AI say about us. The gaps are the ones every tracker in this category shares. API answers approximate but do not equal the consumer apps, a score with no trend history behind it is a screenshot rather than a signal, and a rubric only becomes comparable after it has scored many brands.
This brief describes a focused, single-operator replacement for the part of BrandGEO 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
Send a fixed set of brand questions through the OpenAI, Anthropic, Gemini, xAI, and DeepSeek APIs, score each answer against a six-dimension rubric with a second LLM pass, store every run, render a Markdown or PDF report.
Automate a bounded research or reporting workflow using permitted data sources.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
A written scoring rubric with per-dimension definitions.
Durable per-run storage for trend history.
A scheduler for recurring audits.
Data and integrations
API keys for OpenAI, Anthropic, Gemini, xAI (Grok), and DeepSeek.
An API budget that scales with prompts x engines.
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 brand visibility auditor for one brand. Requirements:
Node 22, TypeScript, better-sqlite3, a CLI. Local only, no accounts, no telemetry.
brand.json holds my brand name, aliases, domain, one competitor, and up to 25
audit questions (who is X, best tools for Y, X vs competitor, is X legit).
audit run sends every question through OpenAI, Anthropic, Gemini, xAI, and
DeepSeek APIs. Keys from .env; skip engines whose key is missing and say so in
the report instead of failing.
Store one immutable row per run, question, and engine: raw answer, model id,
latency, error text. Never overwrite a previous run.
Cap concurrency at 2 per engine and retry twice on 429 and 5xx with backoff.
A separate scoring pass grades each stored answer 0-10 on six dimensions:
recognition, knowledge depth, competitive context, sentiment, contextual
recall, discoverability. Rubric text lives in rubric.md, scores must cite the
answer sentence that justifies them.
audit report renders a Markdown report: overall score per engine, the
six-dimension table, competitor mentions, and every flat-out wrong claim the
models made about the brand, quoted.
audit history prints score per engine across runs from SQLite, so week two
starts meaning something.
Out of scope: white-label PDFs, multi-brand management, scheduled monitoring,
and scraping the consumer web UIs. One brand, run by hand.
README: setup, per-run cost estimate by engine, and a plain note that API
answers only approximate what the apps actually show users.
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:
White-label PDF reports an agency can hand to a client.
Weekly monitoring that keeps running when nobody is thinking about it.
A rubric calibrated across many brands, so scores are comparable.
Competitor benchmarks per brand.
The consumer app surfaces, which no API exactly reproduces.
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.
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 — A self-hosted AI visibility dashboard that runs your prompts across the major engines and records mentions and citations; the white-label reporting is the part you keep paying for
Prior art
Working open-source software you can read, fork, or borrow from before starting:
Elmo — MIT-licensed self-hosted AEO/GEO tracker, a working reference for the prompt-battery loop
llm-brand-tracker — research-grade toolkit for monitoring brand visibility in LLM search, a small starting point
Generated by Can It Be Vibe Coded? · Full report: https://www.canitbevibecoded.com/brandgeo
You still own the product
- 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.
Projects built from this idea
No reviewed implementation has been linked for BrandGEO yet. A submission is evidence for review, not automatic proof that the whole product was replaced.
Built a version of BrandGEO?Submit the project as evidence for this report.
Open-source prior art
Before you start
Can BrandGEO be vibe coded?
Partly, if you narrow it. Running a fixed battery of brand questions through five model APIs and scoring the answers with a second LLM pass is a genuine focused build, and for one brand it answers the headline question: what does AI say about us. The gaps are the ones every tracker in this category shares. API answers approximate but do not equal the consumer apps, a score with no trend history behind it is a screenshot rather than a signal, and a rubric only becomes comparable after it has scored many brands.
What can an AI coding agent reproduce from BrandGEO?
Send a fixed set of brand questions through the OpenAI, Anthropic, Gemini, xAI, and DeepSeek APIs, score each answer against a six-dimension rubric with a second LLM pass, store every run, render a Markdown or PDF report. Automate a bounded research or reporting workflow using permitted data sources. A responsive interface with real empty, loading, success, and error states.
What will a DIY BrandGEO replacement still be missing?
white-label PDF reports an agency can hand to a client; weekly monitoring that keeps running when nobody is thinking about it; a rubric calibrated across many brands, so scores are comparable; competitor benchmarks per brand; The last 20 percent is sync, migration fidelity, speed, and edge cases.; Reliability at the vendor's scale is an operations problem, not a prompt.
What do I still own after building a BrandGEO alternative?
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.