CRM and sales decision
localprospects.ai
A technical user can build the core search+enrich+CSV workflow using existing open-source scrapers and email-verification tools, but reproducing the product's scale, Google Maps/GBP scraping reliability, and polished enrichment accuracy (phone intelligence, dedupe, exports, billing) is non-trivial and costly, so keep paying if you need production scale and reliability.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off120 h to build
$120/mo6 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Search for businesses by city+keyword, run background enrichment jobs that scrape GBP and websites to extract owner names/emails/phones/phone-type/socials, poll job status and fetch enriched JSON, export flattened CSV or push to a CRM.
What it still won’t have
- Scale and reliability of Google Maps / GBP scraping and deduplication at volume
- Proprietary enrichment accuracy (owner-name matching, best-match email heuristics)
- Built-in credit/billing and refunds handling
- UI polish, polished CSV presets for GHL/Instantly/HubSpot
- Customer support and SLA
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
localprospects.ai does not publish a price we could read, so there is nothing to compare against. What building costs is below.
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 self-hosted Local Prospects minimal replacement using Node.js + Express, Postgres, Puppeteer/Playwright, and a small worker queue (BullMQ). Core features in scope: 1) a locations fuzzy-search endpoint (store a small cities table), 2) POST /search that enqueues an async job reserving depth and returning job_id, 3) worker that scrapes Google Business Profile search results and each business website (use Puppeteer with residential/rotating proxies), extracts owner name, emails, phone numbers, phone line type and carrier (use libphonenumber and an external carrier lookup API), and stores enriched records in Postgres, 4) GET /job/:id to return job progress and GET /job/:id/results that returns JSON and offers CSV export with the exact header order, 5) a simple web UI to start searches and download CSVs. Out of scope: building a credit/billing system, campaign orchestration across thousands of cities, advanced owner-name best-match ML, and paid support. Require robust error handling, retries, proxy failure handling, unit and integration tests, and a README with deployment steps (Docker compose) and cost estimates for proxies and carrier lookups.
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- 3/3 assessment runs agreed+4
- Evidence score57
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.
- official productLocalProspects.ai | AI-Powered Local Lead Generation
- official docsAPI Documentation | LocalProspects.ai
Integrity checks
What held up, and what did not.
