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Understory Unfiltered: $13M ARR Without Venture Capital?
Catch up on our Understory Unfiltered episode sharing how WIZA generated $13M ARR without any venture capital
TLDR: Slow verification beats fast data through accuracy-first philosophy
Stephen Hakami, founder and CEO of WIZA, has built a $13 million ARR data platform at just 27 years old without taking a single dollar of venture capital. Starting the company at 21, Stephen transformed a personal sales prospecting problem into a comprehensive data verification platform that prioritizes accuracy over speed.
Listen to this episode to learn how WIZA's "slow but accurate" approach beats competitors with high email verification rates, how Stephen survived losing his only developer and nearly losing the entire company, and the pricing model evolution that helped them scale from $3M to $13M ARR with just 24 employees.
Meet the guests: Stephen Hakami, founder and CEO of WIZA
Stephen Hakami is the founder and CEO of WIZA, a data verification platform that prioritizes accuracy over speed through live email, job title, and company verification. Starting the company at 21 after a short sales career at a conference company, Stephen experienced firsthand the inefficiencies of bouncing between LinkedIn scrapers, email finders, and verification tools, ending up with just 28% valid emails.
WIZA launched six and a half years ago with a simple model: charge 15 cents per valid email and verify everything live. The platform has since grown to $13M ARR with just 24 employees, entirely bootstrapped. Stephen spent the first four years focusing almost exclusively on product development with minimal sales or marketing, remaining profitable while scaling organically to fund growth without external capital.
Chapters
[00:27] From sales rep to data platform founder: how a conference company prospecting problem sparked the WIZA idea
[04:29] The "slowest email finder" strategy: why WIZA prioritizes live verification over speed and charges only for valid emails
[07:31] Behind the verification engine: managing multiple API providers and scoring systems for maximum accuracy
[10:32] Engineering-first growth: four years without substantial sales or marketing team, focusing purely on product
[12:59] Crisis and recovery: losing sole developer Ryan and rebuilding from local code with no GitHub backup
[20:00] Bootstrapped to $13M ARR: building a profitable data platform without venture capital at 27 years old
[21:07] Pricing evolution masterclass: from 15 cents per email to unlimited models that streamlined enterprise sales
[28:14] Competing with ZoomInfo: how newer data platforms are unseating expensive incumbents in enterprise accounts
[32:25] The $100M ARR goal: scaling from 25% enterprise to 75% sales-driven revenue mix
Key Insights
SaaS data platforms can win by deliberately optimizing for accuracy over speed.
Most data platforms optimize for speed, but Stephen took the opposite approach. "We somewhat pride ourselves on being the slowest email finder out there because we do these live verifications," he explains. WIZA checks job titles, company information, and email status in real-time rather than serving cached results.
As we discuss in the episode, we've run accuracy tests comparing WIZA against competitors. The results confirmed Stephen's thesis: slower verification delivers dramatically better data quality, eliminating the need for secondary validation APIs that most Clay workflows require.
Bootstrapped SaaS founders can reach $13M ARR by ignoring commercial teams for four years.
Stephen started WIZA at 21 and spent the first four years building products with minimal commercial focus. "I did all our marketing until two years ago maybe. Sales, we didn't have much of a sales motion until late 2024," he shares. The team invested heavily in engineering, allowing them to remain profitable and fund growth organically.
This approach suits technical products where data quality creates natural word-of-mouth. For founders considering similar paths, Stephen's model shows how product excellence can drive growth without external funding or large commercial teams.
Losing critical team members without documentation can destroy companies, but surviving it builds leadership resilience.
WIZA's sole developer passed away unexpectedly at $3M ARR, leaving Stephen with no access to code, systems, or documentation. "We didn't have any of the code, like even on GitHub. It was local on the machine of the previous developer. And he was also in Morocco," Stephen recalls. The website went down regularly, forcing Stephen to carry his laptop everywhere.
A new CTO had to reverse-engineer the entire platform. The experience shaped Stephen's philosophy: "It's how you act and lead in those very difficult high-stress times. That's what your job really is."
Waterfall verification through multiple scored providers maximizes data accuracy for B2B prospecting.
Rather than relying on a single verification source, WIZA routes requests through multiple third-party providers with internal scoring. "We score our verifiers and we can say, hey, this one's underperforming for whatever reason, can kind of switch those out," Stephen explains.
At Understory, we use similar waterfall enrichment logic in Clay for client campaigns. The principle applies broadly: no single data source is consistently accurate, so stacking providers with fallback logic produces better results than any individual tool.
Credit-based pricing creates renewal friction that unlimited models eliminate for enterprise sales.
WIZA's original credit model created constant friction. "If someone has a bunch of credits leftover, even if they love the product, they don't feel right about buying more credits," Stephen notes. Sales conversations became complex calculations of team size and usage forecasting.
The unlimited model with fair-use caps transformed both acquisition and renewal. Enterprise customers use far less data than expected, making unlimited viable. "SDRs are reaching out to 50, 75 people per day," Stephen observes, not building massive lists.
The LinkedIn echo chamber dramatically overstates how quickly enterprise buyers switch data platforms.
The market perception doesn't match reality. "If you just go on LinkedIn, you would think ZoomInfo is like an older legacy platform. Not many people use it anymore. It's not true at all," Stephen explains. ZoomInfo still dominates $100K+ enterprise accounts.
We hear this constantly in paid media discovery calls: "If I purchase Clay, can I cancel ZoomInfo?" The transition is happening, but slower than the 10,000-person LinkedIn echo chamber suggests. Enterprise procurement cycles and data governance requirements create substantial switching costs.
Data platforms face unique complexity that pure software companies avoid entirely.
Stephen frequently envies pure software companies. "We always talk about Gong as an example. It doesn't really do anything, it's just a bit nicer to access calls. It must be so nice to sell something that's just purely software with no data component," he reflects.
Data platforms face dual challenges: maintaining accuracy across sources while building user-facing features. The complexity creates stronger competitive moats but requires sustained engineering investment that most startups underestimate.
Want more insight on bootstrapped SaaS growth and data platform strategies? Listen to the full episode on YouTube and subscribe to Understory's podcast for more insights on scaling without venture capital.
Looking to eliminate the coordination overhead consuming your growth team's time? Book a call with Understory to explore how coordinated paid media, outbound, and creative can streamline your SaaS growth without specialist management complexity.
