AI assistants and search decision
Rank Prompt
A narrowly scoped replacement (monitoring prompts, extracting citations, scoring visibility, and scheduled reports) is realistic for a small technical team to build and run; Rank Prompt's polished UI, multi-engine coverage, and enterprise features are the expensive parts to replicate.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Periodically run prompts against target AI engines, collect their responses, detect brand mentions and citations, compute visibility scores and trends, and generate scheduled reports/alerts.
What it still won’t have
- Coverage and engineered support for six specific AI platforms out of the box (ChatGPT, Perplexity, Google AI Mode, Claude, Gemini, Grok)
- Polished UI, analytics polish, and enterprise features like white-label client portals
- Built-in integrations (GA4, GSC, WordPress) and the Rank Prompt developer conveniences
- Operational scale, monitoring, and reliability guarantees
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted AI-visibility tracker using Python/FastAPI, PostgreSQL, Redis/Celery for scheduling, and a small React dashboard. In scope: (1) a scheduler that runs engine-specific prompt jobs (supporting at least ChatGPT and Perplexity) via HTTP or headless browser, (2) parsers that extract mentions and citation domains from responses, (3) storage of raw responses and time-series visibility events in Postgres, (4) logic to compute per-engine visibility scores and aggregate trends, (5) a REST API to list brands, trigger runs, and fetch reports, (6) scheduled PDF/CSV report generation and a webhook delivery endpoint. Out of scope: building full Content Studio, white-label client portal, and integrations (GA4/GSC/WordPress). Require robust error handling, retries, idempotency keys for runs, unit and integration tests, and a simple Docker Compose deployment for a single server.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score67
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productRank Prompt | AI Search Visibility, AEO & GEO Tracking
- official pricingRank Prompt Pricing: AI Visibility Plans & Free Trial
- official docsRank Prompt API Docs (v1)
- open sourcedevflowinc/trieve
- open sourcedzhng/deep-research
Integrity checks
What held up, and what did not.
