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A serviceability calculator that helps Australian mortgage brokers compare 40-plus lenders in seconds and reached AUD $5M ARR without outside funding.
10,000+ mortgage brokers · 50%+ Australian market share · AUD $30–50/user/month
The niche is tiny, the spreadsheet logic is brutal, and that is exactly why a product that becomes trusted can reach half the market without a large sales or marketing machine.
Fewer bars = easier, cheaper, or faster for an AI-assisted solo builder. Editorial judgments based on the case details.
Quickli is a B2B SaaS product for Australian mortgage brokers. It replaces the manual spreadsheet work required to assess a borrower's serviceability across lenders: a broker enters the financial position once and receives results from more than 40 lenders in seconds. Cofounder Eric Dill experienced the pain as a mortgage broker; Angus Keatinge brought the engineering and product focus. They spent roughly 16 to 18 months building before a paying customer, then invited brokers to attack a beta version. About 400 to 500 brokers joined before launch, and accuracy plus word of mouth carried the product through a tight professional community. After three years, the founders reported AUD $5M ARR, just over 10,000 mortgage brokers, more than 50% market share in Australia, and roughly 40 employees. Brokers pay around AUD $30 to $50 per user each month. The company bootstrapped, stayed profitable, and grew with almost no proactive sales or marketing. Its moat is not a clever landing page; it is the difficult maintenance work required to keep lender rules accurate and trustworthy.
Quickli's first customers came from the profession the founders understood: mortgage brokers who were willing to try a beta and challenge its calculations.
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“Yeah, so we recently cracked the 5 million ARR figure.”
6:45 · hear it in the videoData credibility: Founder interview; ARR, customer count, market share, pricing, team size, and growth history are self-reported by the cofounders and unaudited. The product and workflow are described directly in the interview.
Quickli began as a free beta and clearly told users that charging would follow. That made the first dollar a conversion from demonstrated utility rather than a cold sale.
Quickli's flywheel came from a painful problem shared by nearly every broker and a professional community with high trust. A broker who found the calculator accurate could recommend it to colleagues, and those colleagues faced the same lender spreadsheets.
Quickli's hardest quiet period came before the first customer, when the founders spent 16 to 18 months building and testing difficult lender logic. They managed it by keeping income streams on the side and inviting brokers to try to break the beta, which removed pressure to release something unreliable.
Quickli is a serviceability aggregator for Australian mortgage brokers. The broker captures a customer's income, expenses, liabilities, property information, and other parts of the financial position once. Quickli then applies each lender's terminology, policy rules, and risk buffers to produce comparable borrowing results. The original workflow relied on large spreadsheets, often one for each lender, and a broker could spend 15 to 20 minutes crunching numbers per lender. The product is therefore a rules engine, data-entry experience, comparison view, and workflow export rather than a simple calculator. Accuracy is the product. Lenders change policies, use different terms for similar concepts, and contain awkward edge cases such as negative gearing. A strong MVP needs versioned lender rules, transparent inputs, calculation tests, audit trails, and an export that fits the broker's next action. It should also make it easy to flag a result for review without hiding uncertainty. Quickli's 40-person team and mostly engineering composition show that maintenance is a permanent operating function, not a one-time build milestone.
Quickli charges mortgage brokers roughly AUD $30 to $50 per user per month, depending on the plan. At just over 10,000 brokers and reported AUD $5M ARR, the business has a clear per-seat SaaS model and room to expand account value. The founders also described future offerings that could move certain customers toward AUD $100 or $150 subscriptions, including lender-facing products, AI features, data, analytics, and additional in-app tools. The economics depend on trust and retention: a broker who relies on a correct serviceability result does not casually switch after the product becomes part of daily work. The cost side is heavier than a simple calculator because lender-policy maintenance, engineering, customer support, and quality assurance are ongoing. Price should reflect the financial risk avoided and the hours returned, while enterprise plans can cover governance, team administration, auditability, and integrations. The reported figures are founder statements, not audited accounts, but the combination of ARR, seats, pricing, and market share describes a coherent recurring-revenue model.
Quickli grew through product-led and community-led distribution in the Australian mortgage-broker market. Early cold emails and phone calls helped seed the beta, but the larger engine was broker word of mouth through Facebook groups, local communities, and trusted professional circles. A satisfied broker could become a mega-fan and introduce the product to several circles without a formal sales team. The company maintained a website and occasional LinkedIn posts, but the founders said they had no real sales team and only a small amount of marketing capacity. That is a strong signal about problem selection: the pitch was immediately legible because every broker knew the spreadsheet pain. A similar business should map the communities before building the acquisition machine, recruit beta users who are willing to teach peers, and give them a shareable result rather than a vague demo. Events, industry awards, partner channels, and lender relationships are available expansion paths, but they should follow a reliable core workflow. In a narrow market, one respected operator can be more valuable than broad reach.
Build Quickli as a rules-heavy B2B SaaS with a strong audit and testing foundation. Use Next.js App Router with TypeScript and Tailwind for the product, Clerk for authentication, Neon Postgres with Drizzle for workspaces and customer cases, Stripe for seat billing, and Vercel for deployment. Keep lender policies in versioned rule tables with effective dates, source references, test fixtures, and an approval workflow. Store a calculation snapshot with every result so a broker can see which policy version produced it. Key services include a rules engine, scenario comparison, export generation, support issue capture, and monitoring for unexpected result changes. Do not make an AI model the authority for financial calculations; AI can help explain inputs or surface a missing field, while deterministic rules produce the number. Costs rise with engineering, policy research, customer support, security, and quality assurance. Start with one country and one professional segment, because every new lender or jurisdiction expands the rule-maintenance surface. The reported 40-person team is a warning that defensibility comes with operating expense as well as code.
Copy the niche, the painful manual baseline, the beta group, the accuracy obsession, and the decision to stay bootstrapped while the product earned trust. Copy the founders' willingness to spend time on hard domain logic that competitors may underestimate. Change the scope before hiring a 40-person team: select one jurisdiction, one role, and one calculation that can be verified with real users. Build an explicit route from beta feedback to paid workflow, including billing, team management, export, and audit history. Do not copy the assumption that build quality alone will always produce growth. Quickli benefited from a tightly connected broker community, a universally shared spreadsheet problem, and a simple one-sentence pitch. A new product needs to prove those conditions rather than call every niche viral. The opportunity is strongest where a small group shares an expensive, repetitive decision and can recommend a trusted tool to peers. The main risk is accuracy debt: if the product gets one high-stakes result wrong, the acquisition loop reverses into negative word of mouth.
The four founder playbooks — first customers, first dollar, the flywheel, the silent stretch — are in the Unicorn Club, on this idea and on all 1,435 others. The story, the numbers, the sources and every later section stay open to everyone.
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