SEO and marketing decision

LocalRank.so

A capable engineer can reproduce a usable rank tracker, LLM-mention monitor, and citation-submission automation in-house, but they cannot easily replicate LocalRank's large proprietary datasets, citation distribution network, nor enterprise polish—so building a narrow replacement is realistic, full parity is not.

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Subscription$57/month ✓ verified
Initial build80 hours
Monthly upkeep8 hours + $150
Evidence3/3 runs agree

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

  • Track local rankings and LLM mentions for a set of businesses, store results, run audits and generate citation submissions, and provide a dashboard/reports.

What it still won’t have

  • Large proprietary datasets (verified contacts, existing citation placements)
  • Established distribution/placement network for automated citation submissions
  • Scale and historical data (millions of citations and rank-history at scale)
  • White-label polish and enterprise support/SLAs

What remains hard

  • Proprietary data15M+ verified business contacts
  • Proprietary data2.3M Citations built
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 3 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

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

Not run yet
Build a self-hosted local-SEO dashboard using: Postgres, Node.js (Express) backend, React frontend, and Redis for job queueing. Core features in scope: 1) import/manage businesses and locations; 2) scheduled geogrid rank checks via a SERP API (or configurable scraper) and store time-series results; 3) query multiple LLM APIs to detect LLM mentions for a business and store visibility scores; 4) generate citation submission jobs that POST to directory endpoints or produce formatted submission payloads; 5) user dashboard with history, CSV/PDF export, and scheduled email reports. Out of scope: building a paid citation placement network or acquiring large contact databases. Include retries, rate-limit handling, logging, unit and integration tests, and Docker-compose for deployment.
How we checked2 sources · 3/3 runs agreed · evidence score 62

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 2 cited sources+1
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score62

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 · 2

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 2 moats quoted from the page