# Build brief — a focused alternative to LLM Pulse

> **Verdict:** Partly, if you narrow it · **Buildability:** 49/100 · **Category:** SEO Marketing
> **Source:** https://www.canitbevibecoded.com/llm-pulse
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from LLM Pulse. Verify current pricing and capabilities before acting.

## Context

**LLM Pulse** — Track brand mentions, citations, sentiment, competitors, and AI referral traffic across major AI platforms. It currently costs $56.52/mo.

The core loop is a realistic focused build: run a fixed prompt set against a model API, detect brand and competitor mentions, collect citations, and chart the results. The gap appears when you need dependable runs across many models, long-term evidence, team access, exports, alerts, and the broader visibility and reputation workflow.

This brief describes a focused, single-operator replacement for the part of LLM Pulse 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 a fixed prompt set against one model API, store the answers, detect brand and competitor mentions and citations, and show weekly trends for one project.

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

## Requirements

### Functional

- Node.js 22.
- SQLite.
- A scheduled local process.

### Data and integrations

- One compatible model API key.
- A small API budget.

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 a local, single-user AI visibility tracker for one brand.
Use Node.js 22, TypeScript, Express, better-sqlite3, server-rendered HTML, and vanilla JavaScript.
Bind the app to localhost:4173 and provide one documented command for the first run.
Keep MODEL_BASE_URL, MODEL_API_KEY, and MODEL_NAME in .env and ship a safe .env.example.
Support one JSON chat endpoint configured entirely through those environment variables.
Document the endpoint contract and isolate it behind one small adapter so it can be replaced later.
Let the user configure one brand, aliases, three competitors, and up to 25 prompts.
Run prompts manually and on a weekly local schedule with a clear API budget limit.
Limit concurrency, retry transient failures, and keep failed prompts visible instead of dropping them.
Store every prompt, raw answer, model name, timestamp, latency, and error in SQLite.
Detect case-insensitive brand and competitor mentions using editable aliases.
Extract and normalize URLs from answers, then preserve the source answer for every citation.
Use one structured model pass to label brand sentiment as positive, neutral, negative, or absent.
Calculate mention rate, citation rate, competitor share of voice, and net sentiment with documented formulas.
Show current results, weekly trends, and a prompt-level evidence table on a compact dashboard.
Make every aggregate metric link back to the raw answers used to calculate it.
Export prompts, answers, mentions, citations, and weekly metrics as CSV files.
Add backup and restore commands for the SQLite database.
Do not add accounts, billing, teams, telemetry, web crawling, or an integration catalog.
Do not claim parity with managed multi-model collection, reputation workflows, traffic analytics, or production monitoring.
Write tests for alias matching, URL normalization, retry handling, and metric calculations.
Include a README with setup, API cost controls, data location, backup steps, and limitations.
Run the tests and production build before finishing, then list the exact commands used.

## 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:

- Managed execution across the full model set.
- Long-term historical comparisons and evidence.
- Reputation, source, traffic, and competitor workflows.
- Team permissions, exports, alerts, and integrations.
- Production monitoring and support.

## 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/) — Tracks mentions, citations and competitors across the major engines; sentiment and referral traffic are still on the road map

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/llm-pulse