Aug 14, 2022 · 15m · top-founders
Bootstrapped to $1m in Under 18 Months, How he's growing his ATS SaaS
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Waldo Labs founder Taylor Bergen explains how he bootstrapped his recruiting automation platform to an $84,000 monthly run rate and $40,000 in monthly net profit with a lean four-person team.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 39% of the talking time here. How this is scored →
Nathan as informed peer 5.5 Guest teaching 2.8 Guest disagreement 1.2 Nathan pushing back 3.0
speaking balance: gold is Nathan, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 4:01 Correcting the interpretation of candidate submission metrics
Taylor reframes Nathan's skepticism regarding overall candidate volume by explaining that 40% of the very first candidate submitted gets hired due to upfront calibration.
Hardest push from Nathan ▶ 10:37 Calling out AI marketing vs manual backend realityNathan directly presses Taylor to be honest about whether the software is genuinely automated or relying on hidden human labour.
Biggest teaching moment ▶ 1:52 Explaining recruitment SaaS retainer pricing vs per-hire feesTaylor corrects Nathan's assumption that clients pay monthly per active employee placed, detailing their 3-role concurrent flat retainer model.
Nathan holds their own ▶ 12:40 Deconstructing net margin and cost structure live on airNathan deduces the company's real cost breakdown—salaries, offshore contractor spend, and SaaS tools—accurately estimating their ~$40k/mo bottom-line profit.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Nathan as informed peer | Guest teaching | Guest disagreement | Nathan pushing back | Why |
|---|---|---|---|---|---|---|
| Episode Preview: Bootstrapping to a Million Dollar Run Rate | 5 | 4 | 1 | 4 | Nathan probes the flat monthly fee model, initially misunderstanding whether clients pay continuously while an employee is active. Taylor clarifies that billing lasts only for the duration of the hiring contract, not the tenure of the employee. | |
| Hiring Alignment, Efficiency, and Conversion Metrics | 6 | 5 | 2 | 5 | Nathan challenges potential incentive misalignments and presses on ambiguous conversion statistics. Taylor clarifies his 40% first-candidate hiring rate and benchmark data against traditional industry averages. | |
| Origins of Waldo Labs and Fixing Broken Agency Recruiting | 6 | 2 | 1 | 2 | Nathan quickly computes the ARR run rate ($84k MRR / ~$1M ARR) and investigates team composition and offshore engineering management. The exchange is highly collaborative with clear financial metrics. | |
| Host Announcement: Founder 500 Event in Austin | 5 | 3 | 2 | 4 | Following the mid-roll event promo, Nathan questions the automation claims by asking how much manual human work powers the backend. Taylor candidly admits that matching remains manual while workflow automations handle communication. | |
| Referral Growth Engine and High SaaS Profitability | 7 | 2 | 1 | 3 | Nathan models Waldo's unit economics and burn rate on the fly, breaking down salary and contractor costs to uncover their ~$40k/month net profit. Taylor agrees and contrasts his lean structure against bloated agency headcount. | |
| The Famous Five Questions and Episode Conclusion | 4 | 1 | 0 | 0 | Nathan conducts the standard Famous Five rapid-fire questions and provides an accurate, complementary closing synthesis of the company's metrics. |
Statements from this episode (13)
Waldo Labs charges a flat $7,000 monthly fee for three roles
“So our model is we just charge seven K a month, take on your top three priority roles at a time. That's it. No additional costs, no hidden fees, nothing.”
Bergen: Waldo Labs clients achieve an average 22-day time to hire
“Their average time to hire is 22 days, but they'll typically have a handful of roles they want us to fill.”
Bergen: Waldo Labs has made a hire for every client worked with
“We've made a hire for every client we've worked with.”
Bergen: 40% of first candidates submitted by Waldo Labs are hired
“40% of the first candidate that we submit to clients is hired.”
Bergen: 52% of Waldo Labs' total hires are diversity candidates
“And then 52% of our overall hires have actually been diversity candidates as well.”
Bergen: Waldo Labs made over 50 hires across 15-20 startups YTD
“So year to date, we've made over 50 hires for at least 15 to 20 different startups, including current clients.”
Waldo Labs maintains 12 to 15 regular active clients
“We have about 12 clients that we maintain regularly. It depends, 12 to 15.”
Waldo Labs is entirely bootstrapped and just over one year old
“No, totally bootstrapped. Yeah. Just over a year old.”
Waldo Labs operates with one CTO and two offshore engineers
“One, the CTO co-founder, and then we have two offshore engineers as well that he was managing.”
Bergen: Most companies claiming to use AI actually use human labor
“Well, that's the funny thing about AI. Everyone says they're doing AI and typically there's just a bunch of people behind the scenes.”
Waldo Labs automates outreach but relies on manual backend candidate matching
“The matching is still manual. Like we do that on our end when a positive response comes in, we match them in the platform. But from there, like all the email outreach, everything in the platform after that is all automated.”
Waldo Labs retains about $40,000 in monthly net profit
“So the other 50, we keep in the bank. We also have like some third party tools and solutions that we invest in, but yeah, it's about 40 K a month that we have in, in like overall.”
Bergen: Recruiting agencies bloat overhead by hiring sourcers instead of automating
“Well, something I've seen that's really interesting is all these agencies and recruiting firms, they want to hire like a ton of headcount. And I don't really get it because they want to hire all these sourcers and individuals to do it manually. And then like y…”