CRM and sales decision

Waalaxy

A technical user can reproduce a basic LinkedIn outreach workflow (capture profiles, run sequences, detect replies, export leads) using existing automation libraries and the cited community projects, but matching Waalaxy’s polish, integrations, deliverability safeguards and large-scale safety tooling would require more time and resources.

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Subscription$19/month ✓ verified
Initial build80 hours
Monthly upkeep8 hours + $30
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

  • Find prospects on LinkedIn, enqueue them into outreach sequences, send connection requests/messages and scheduled follow-ups, track replies and export or push qualified leads to a CRM.

What it still won’t have

  • Polished UI/UX and onboarding flow
  • Scale, reliability and account-safety tooling for high-volume outreach
  • Built-in integrations catalog (HubSpot, Pipedrive, Make, Zapier, n8n)
  • Priority support and in-house customer success
  • Email-finder credits and advanced multichannel features

What remains hard

  • Product polish and ongoing maintenance
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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 minimal LinkedIn outreach automation service using a Chrome extension + Node.js backend and Postgres. In scope: (1) Chrome extension that captures selected LinkedIn search results/profiles and sends them to the backend; (2) backend job scheduler that executes connection requests/messages and follow-ups with randomized/human-like delays; (3) simple sequence builder UI, campaign and prospect storage in Postgres, and a web inbox showing reply state; (4) CSV export and webhook to push qualified leads to a CRM; (5) integration with an email-finder API and SMTP for optional email outreach. Out of scope: training proprietary ML models, building a marketplace, advanced inbox analytics, and enterprise multi-tenant billing. Include error handling for LinkedIn rate-limits and browser failures, logging, and unit + integration tests. Deploy backend to a small cloud VM and use managed Postgres.
How we checked5 sources · 3/3 runs agreed · evidence score 67

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 →

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded