# Build brief — a focused alternative to AmICited

> **Verdict:** Partly, if you narrow it · **Buildability:** 36/100 · **Category:** SEO Marketing
> **Source:** https://www.canitbevibecoded.com/amicited
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from AmICited. Verify current pricing and capabilities before acting.

## Context

**AmICited** — Grow your brand in AI search by feeding your AI agents the right context, and track prompts, page citations, and competitors' visibility to optimize your pages for AEO. It currently costs $57.73/mo.

The scoring loop is genuinely straightforward to build, but a faithful replacement is not, and that gap is the honest reason to keep paying. The naive personal build sends your questions through the model APIs, but API answers are not what a real user sees when they open ChatGPT, Perplexity, or AI Overviews, so that number is only a proxy. AmICited does not use LLM APIs at all: it drives real browsers to ask the actual consumer surfaces the way a person in a given country would, a browser-automation fleet routed through country-level proxies, kept working as every surface changes. The other gaps are the historical archive that compounds from day one and cannot be backfilled, an MCP server that lets your AI agents act on your visibility gaps, and, on higher plans, human AEO consulting from real experience that no script replaces. You can build the weekend proxy; the prompt below is that honest consolation build, with its limits stated plainly.

This brief describes a focused, single-operator replacement for the part of AmICited 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

Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers.

- Automate a bounded research or reporting workflow using permitted data sources.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Residential or geo-targeted proxies to query from specific countries.
- A scalable scheduler/queue and the infra to run and retry many browser sessions in parallel.
- Durable per-run storage that never overwrites an answer, so history compounds.
- Ongoing upkeep as each engine and surface changes its UI, models, and citation format.

### Data and integrations

- Headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs.

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 answer-engine citation tracker for one brand, as a rough personal proxy. Requirements:

- Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered
  dashboard. Local only, no accounts, no telemetry.
- brand.json holds my brand name, aliases, domain, and competitor names. prompts.json
  holds up to 50 buyer questions.
- `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI
  (Grok) with each provider's web search or grounding tool enabled. Keys live in .env.
- Store one immutable row per run, prompt, and provider: the full raw answer, cited
  URLs, model id, latency, and error text. Never overwrite an existing run, so the
  archive compounds over time.
- Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep
  failed cells visible in the report instead of dropping them.
- Detect brand and competitor mentions case-insensitively using the alias list, and
  record the first-mention character offset as a crude prominence proxy.
- Normalize citations to hostname plus canonical path, strip tracking parameters, then
  compute owned-domain citation share and a top 25 cited-sources table.
- `track serve` renders visibility per provider over time, share of voice against each
  competitor, the sources table, and the prompts where competitors are cited and I am
  not (the gap list is the point).
- `track export` writes runs, mentions, and citations to CSV.
- Fixture tests for mention detection and URL normalization.
- Out of scope, and be honest in the README that these are exactly what a paid product
  like this sells: the real consumer surfaces (this uses model APIs, which do NOT match
  what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users),
  country-specific results via a proxied real-browser fleet, running that fleet at
  scale, and any human AEO consulting. Do not try to automate the consumer web UIs.
- README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note
  that API answers only approximate what users actually see.

## 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 faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers.
- Country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up.
- Scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring.
- Months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late.
- Human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script 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.
- 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/) — Self-hosted AEO tracker covering the API-proxy scoring loop; it queries model APIs, which is exactly the faithfulness gap the paid product exists to cross

## 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 your prompts across the major answer engines and records mentions, competitors, and cited sources
- [llm-brand-tracker](https://github.com/sarahkb125/llm-brand-tracker) — Small research-grade toolkit for monitoring brand visibility in LLM search; a reasonable starting point

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