Vibecode Profound
track this build5 steps, step by step0%KINDA · weekend project
The tracking loop is genuinely weekend-buildable: send a fixed prompt set through four model APIs on a schedule, count brand and competitor mentions, normalize the citations, and parse your access logs for AI crawler and AI referral traffic. That gets you most of the dashboard for one brand. The gaps are the honest part. API answers are not the answers the ChatGPT app or Google AI Overviews actually serve, you cannot measure what real people ask AI, and a number with no history behind it tells you nothing on week one.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Profound. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Profound indie build ## Goal Build the smallest trustworthy replacement for the core Profound workflow for one developer or a tiny team. ## Scope Run a fixed prompt set daily through the answer engine APIs, score brand and competitor mentions plus cited sources, and parse server logs for AI crawler and AI referral traffic. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - prompt volume data: what people actually ask AI is not measurable from outside - the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see - months of history and competitor baselines, without which a single week's visibility number means nothing - upkeep as engines, crawler user agents, and citation formats keep changing - the agent, recommendation, and product visibility layers stacked on top of the tracking If those capabilities are essential, use Elmo instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a local AI answer engine visibility tracker for one brand. 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 40 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, and Perplexity with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run. - 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. - Score the sentiment of each brand mention in one cheap structured pass over stored answers, after the run, never inline. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors appear and I do not. - `track crawlers --log access.log` parses server logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and friends, plus referral hits from chatgpt.com and perplexity.ai, and reports which URLs they touched. - Keep the bot user agent list in an editable JSON file. Log lines matching nothing get counted as unknown agents, not silently discarded. - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and log parsing. - Out of scope: real consumer surface answers, prompt volume estimates, content generation agents, teams, and hosted scheduling. Do not scrape the consumer web UIs. - README: setup, per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - OpenAI, Anthropic, Gemini, and Perplexity API keys - each provider's web search or grounding tool - server access logs - durable per-run storage - a scheduler for daily runs - an API budget that scales with prompts x engines x days ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Profound. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Profound indie build ## Goal Build the smallest trustworthy replacement for the core Profound workflow for one developer or a tiny team. ## Scope Run a fixed prompt set daily through the answer engine APIs, score brand and competitor mentions plus cited sources, and parse server logs for AI crawler and AI referral traffic. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - prompt volume data: what people actually ask AI is not measurable from outside - the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see - months of history and competitor baselines, without which a single week's visibility number means nothing - upkeep as engines, crawler user agents, and citation formats keep changing - the agent, recommendation, and product visibility layers stacked on top of the tracking If those capabilities are essential, use Elmo instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a local AI answer engine visibility tracker for one brand. 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 40 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, and Perplexity with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run. - 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. - Score the sentiment of each brand mention in one cheap structured pass over stored answers, after the run, never inline. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors appear and I do not. - `track crawlers --log access.log` parses server logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and friends, plus referral hits from chatgpt.com and perplexity.ai, and reports which URLs they touched. - Keep the bot user agent list in an editable JSON file. Log lines matching nothing get counted as unknown agents, not silently discarded. - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and log parsing. - Out of scope: real consumer surface answers, prompt volume estimates, content generation agents, teams, and hosted scheduling. Do not scrape the consumer web UIs. - README: setup, per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - OpenAI, Anthropic, Gemini, and Perplexity API keys - each provider's web search or grounding tool - server access logs - durable per-run storage - a scheduler for daily runs - an API budget that scales with prompts x engines x days ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Profound. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Profound product brief ## Problem The tracking loop is genuinely weekend-buildable: send a fixed prompt set through four model APIs on a schedule, count brand and competitor mentions, normalize the citations, and parse your access logs for AI crawler and AI referral traffic. That gets you most of the dashboard for one brand. The gaps are the honest part. API answers are not the answers the ChatGPT app or Google AI Overviews actually serve, you cannot measure what real people ask AI, and a number with no history behind it tells you nothing on week one. ## Product outcome Run a fixed prompt set daily through the answer engine APIs, score brand and competitor mentions plus cited sources, and parse server logs for AI crawler and AI referral traffic. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI, Anthropic, Gemini, and Perplexity API keys - each provider's web search or grounding tool - server access logs - durable per-run storage - a scheduler for daily runs - an API budget that scales with prompts x engines x days ## Explicit non-goals for v1 - prompt volume data: what people actually ask AI is not measurable from outside - the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see - months of history and competitor baselines, without which a single week's visibility number means nothing - upkeep as engines, crawler user agents, and citation formats keep changing - the agent, recommendation, and product visibility layers stacked on top of the tracking ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build me a local AI answer engine visibility tracker for one brand. 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 40 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, and Perplexity with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run. - 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. - Score the sentiment of each brand mention in one cheap structured pass over stored answers, after the run, never inline. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors appear and I do not. - `track crawlers --log access.log` parses server logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and friends, plus referral hits from chatgpt.com and perplexity.ai, and reports which URLs they touched. - Keep the bot user agent list in an editable JSON file. Log lines matching nothing get counted as unknown agents, not silently discarded. - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and log parsing. - Out of scope: real consumer surface answers, prompt volume estimates, content generation agents, teams, and hosted scheduling. Do not scrape the consumer web UIs. - README: setup, per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Profound capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Profound indie build ## Goal Build the smallest trustworthy replacement for the core Profound workflow for one developer or a tiny team. ## Scope Run a fixed prompt set daily through the answer engine APIs, score brand and competitor mentions plus cited sources, and parse server logs for AI crawler and AI referral traffic. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - prompt volume data: what people actually ask AI is not measurable from outside - the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see - months of history and competitor baselines, without which a single week's visibility number means nothing - upkeep as engines, crawler user agents, and citation formats keep changing - the agent, recommendation, and product visibility layers stacked on top of the tracking If those capabilities are essential, use Elmo instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build me a local AI answer engine visibility tracker for one brand. 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 40 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, and Perplexity with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run. - 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. - Score the sentiment of each brand mention in one cheap structured pass over stored answers, after the run, never inline. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors appear and I do not. - `track crawlers --log access.log` parses server logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and friends, plus referral hits from chatgpt.com and perplexity.ai, and reports which URLs they touched. - Keep the bot user agent list in an editable JSON file. Log lines matching nothing get counted as unknown agents, not silently discarded. - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and log parsing. - Out of scope: real consumer surface answers, prompt volume estimates, content generation agents, teams, and hosted scheduling. Do not scrape the consumer web UIs. - README: setup, per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - OpenAI, Anthropic, Gemini, and Perplexity API keys - each provider's web search or grounding tool - server access logs - durable per-run storage - a scheduler for daily runs - an API budget that scales with prompts x engines x days ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Profound product brief ## Problem The tracking loop is genuinely weekend-buildable: send a fixed prompt set through four model APIs on a schedule, count brand and competitor mentions, normalize the citations, and parse your access logs for AI crawler and AI referral traffic. That gets you most of the dashboard for one brand. The gaps are the honest part. API answers are not the answers the ChatGPT app or Google AI Overviews actually serve, you cannot measure what real people ask AI, and a number with no history behind it tells you nothing on week one. ## Product outcome Run a fixed prompt set daily through the answer engine APIs, score brand and competitor mentions plus cited sources, and parse server logs for AI crawler and AI referral traffic. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI, Anthropic, Gemini, and Perplexity API keys - each provider's web search or grounding tool - server access logs - durable per-run storage - a scheduler for daily runs - an API budget that scales with prompts x engines x days ## Explicit non-goals for v1 - prompt volume data: what people actually ask AI is not measurable from outside - the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see - months of history and competitor baselines, without which a single week's visibility number means nothing - upkeep as engines, crawler user agents, and citation formats keep changing - the agent, recommendation, and product visibility layers stacked on top of the tracking ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build me a local AI answer engine visibility tracker for one brand. 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 40 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, and Perplexity with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run. - 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. - Score the sentiment of each brand mention in one cheap structured pass over stored answers, after the run, never inline. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors appear and I do not. - `track crawlers --log access.log` parses server logs for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and friends, plus referral hits from chatgpt.com and perplexity.ai, and reports which URLs they touched. - Keep the bot user agent list in an editable JSON file. Log lines matching nothing get counted as unknown agents, not silently discarded. - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection, URL normalization, and log parsing. - Out of scope: real consumer surface answers, prompt volume estimates, content generation agents, teams, and hosted scheduling. Do not scrape the consumer web UIs. - README: setup, per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Profound capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent
Because the tracking is the cheap half. Profound sells the two things a personal script cannot produce: prompt volume data drawn from real conversations, so you know which questions are worth ranking for at all, and a maintained panel across nine answer engines including the consumer surfaces with no usable API. Marketing teams also want a number somebody else vouches for before it goes in a board deck.
xprompt volume data: what people actually ask AI is not measurable from outside
xthe real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see
xmonths of history and competitor baselines, without which a single week's visibility number means nothing
xupkeep as engines, crawler user agents, and citation formats keep changing
xthe agent, recommendation, and product visibility layers stacked on top of the tracking
share on X ↗"I just replaced Profound ($99/mo) with one prompt"
Don't feel like building it? These folks already made it free.
ElmoA real self-hosted AI visibility dashboard; crawler-log analysis is the conspicuous thing it does not replace.open source↗no votes, no pay-to-list · just what's real
Profound pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| starter | $99/workspace | — | 1 seat; 50 prompts; 1,500 AI responses/month; daily refresh; 1 language/region; 100 agent credits; unlimited domains. |
| growth | $399/workspace | — | 3 seats; 100 prompts; 9,000 AI responses/month; daily refresh; 1 language/region; 400 agent credits; exports. |
| enterprise | custom | — | Custom seats, prompts, response volume, languages/regions, data access, security, and support. |
free tierno free tier; a trial link is offered, but public duration and numeric caps were not verified
billingmonthly only on the public pricing page; no public annual rate
hidden costsAgent usage can continue past included credits or be paused, but public per-credit overage pricing is not disclosed.
verified 2026-08-14 · source ↗
Vibecode Profound
Kinda. The core of Profound is buildable in a weekend with the prompt on this page, but there are real gaps: prompt volume data: what people actually ask AI is not measurable from outside, the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see. Read the honest list above before committing.
How much does Profound cost?
Profound costs about $99/month (Starter, checked 2026-08-04), which is $1188 per year.
What do I lose by replacing Profound?
Honestly: prompt volume data: what people actually ask AI is not measurable from outside; the real consumer surfaces, since AI Overviews and the ChatGPT app have no API that matches what users see; months of history and competitor baselines, without which a single week's visibility number means nothing; upkeep as engines, crawler user agents, and citation formats keep changing; the agent, recommendation, and product visibility layers stacked on top of the tracking. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Profound?
Yes: Elmo (A real self-hosted AI visibility dashboard; crawler-log analysis is the conspicuous thing it does not replace.) The prompt is for when you want it exactly your way.