SaaS •Entrepreneurship

I Built a $10K/Month SaaS Using Other People's Customers

Starter Story • • 16 min • #140
I Built a $10K/Month SaaS Using Other People's Customers

Ivan built a $10K/Month SaaS without finding a single customer himself. Instead, he found the people who already had them. This is his exact playbook

Summary

  • Ivan
    • Built Lancer, an AI agent that automates job discovery, qualification, and bidding on Upwork.
    • Previously ran NB Masters, a software development agency that did over seven figures and grew to almost 20 full-time employees.
    • Used Upwork as the agency’s main client acquisition channel and saw that it was an underrated source of quality leads.
    • Built the first version as an internal tool after noticing that qualifying jobs and writing personalized proposals took significant manual time.
  • Lancer
    • Helps freelancers and agencies turn Upwork into an automated acquisition channel.
    • Saves users more than 10 hours per week of repetitive work.
    • Uses LLMs for job qualification and proposal writing.
    • Offers a subscription model with pay-as-you-go, light, and unlimited plans.
    • Prices include $79 for 30 proposals, $300 for 250 proposals, and an initial $500 per month unlimited launch offer.
  • Metrics
    • Reached $10K per month in the third or fourth month after launching.
    • Helped beta users close three five-figure clients within two weeks.
    • Grew with zero paid ads.
  • Product origin
    • Started with a problem Ivan personally had inside his agency.
    • Saw that Upwork has around 200,000 jobs posted each month.
    • Treated the workflow as a strong AI-agent use case because jobs appear around the clock and each one takes time to qualify and answer manually.
    • Turned the internal tool into a standalone product after friends who owned agencies got results in beta.
  • AI agent opportunity
    • Sees strong potential in automating workflows that sit on top of existing software platforms.
    • Looks for platforms with large user bases and public customer activity because the users and pain points are easier to identify.
    • Uses Upwork as an example because ideal customers and their activity are visible on the marketplace.
  • Connector growth strategy
    • Identifies a layer above the ideal customer instead of selling directly through ads or cold email at scale.
    • Calls these people connectors because they already have trusted access to the target customers.
    • Chose Upwork coaches because they work with agencies and freelancers who want leads from Upwork.
    • Got most growth from two Upwork coaches, one through a beta-user intro and one through personalized LinkedIn outreach.
    • Offered commissions of 30% lifetime when a connector sells, onboards, and sets up a client, or 20% when they only refer.
  • Connector playbook
    • Define the ideal customer profile based on who onboards easily, pays, and stays.
    • Find people one level above that customer who have a trusted network of those buyers.
    • Send highly personalized pitches with research, references to their work, or a Loom video.
    • Work out incentives such as 20% to 30% lifetime commission or upfront payments when the connector’s network justifies it.
    • Track referrals and monthly payouts with affiliate software.
  • Tech stack
    • Built the internal weekend version before turning it into a commercial product.
    • Built the MVP over three months with TypeScript, Next.js, Node.js, GCP, and Firestore.
    • Uses Cursor with Opus, OpenRouter for LLM APIs, Hetzner and GCP for hosting, proxy providers for safe Upwork account connections, Elasticsearch for job data, and Tolt for affiliate marketing.
  • Advice
    • Start building software products sooner instead of delaying for income security.
    • Treat software product skills as highly leveraged because they apply across many online businesses.
    • Recognize that AI creates decades of opportunities to build useful products.
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