The Front Lines
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Gorkem Sevinc.
Founder & CEO · Qualytics
Most data failures aren’t technical — they’re human. They get detected too late: when dashboards break, AI starts hallucinating, and executives quietly stop trusting the numbers. And most of the time, they don't get detected at all. I’m the Co-Founder & CEO of Qualytics, where we help large enterprises catch data quality issues before they turn into business problems by shifting data quality left and treating it like an operating discipline, not a fire drill. Qualytics is built for modern & legacy data and AI stacks. We automatically infer rules, pull subject-matter experts into the loop where context actually matters, and make remediation faster than the meeting about why something broke. I’ve spent my career building complex systems, and I have a few strong opinions: - Data quality is a business problem, not a tooling problem - AI without trusted data is just very confident guesswork - Governance only works when real humans (SMEs) are involvedI write for CDOs, data leaders, and executives who want AI and analytics to move faster without breaking trust along the way. Outside of work, I’m a car nerd and a lifelong LEGO builder. I like systems you can take apart, understand, and rebuild better, where minute details are crucial, which probably explains both my hobbies and my career.
Guest
Gorkem Sevinc
Founder & CEO
Company:
Qualytics
Location:
Washington DC-Baltimore Area
Funding:
$16.7M Raised
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How Qualytics Built Enterprise Data Quality Infrastructure by Validating First, Building Second

Gorkem Sevinc spent 18 months building a product nobody wanted.

The radiology second opinion platform seemed defensible. He had co-founded it with Johns Hopkins radiologists, invested nights and weekends into development, and brought genuine domain expertise. There was just one problem: patients didn't even know radiologists were doctors, let alone understand why they'd need a second opinion on their reports.

"Turns out nobody was interested in second opinions in radiology," Gorkem recalls. "Most patients didn't even know who a radiologist is, that it's an actual doctor that is reading your images and generating a report that goes to your clinician."

The company eventually pivoted to litigation cases and achieved a modest exit, but the lesson crystallized. In a recent episode of BUILDERS, Gorkem, now Founder and CEO of Qualytics, shared how that failure shaped an entirely different approach to building his current company—a data quality platform experiencing explosive inbound demand as enterprises realize their data foundations can't support AI democratization.

The Whack-a-Mole Problem

Before founding Qualytics, Gorkem operated as a CTO and Chief Data Officer at a financial planning and wealth management company. The recurring problem was maddeningly specific: his team managed 100+ data sources feeding into Snowflake, supporting analytics and ML models across the organization. Everything looked architecturally sound—until dashboards broke and KPIs reported incorrect numbers.

"I'm putting all these controls around my data. I have my Snowflake, I have 100 different systems feeding data into this centralized data repository. I have all sorts of crazy analytics, ML models, AI models built on my data foundation. That's all great except the data is wrong and KPIs are off," Gorkem explains.

The reactive pattern became numbingly predictable: executives escalate, data team writes a custom rule to catch that specific issue going forward, everyone moves on until the next break. "We end up taking a reactive approach and we're writing a piece of code for that scenario. Kind of like software testing, but this data testing, data quality, right? We write a rule to catch a specific issue going forward."

His team was managing hundreds of these custom rules, yet they were still getting yelled at. "My team was writing and managing a lot of these rules and we're still being yelled at because we're being taking a highly reactive approach. It's expensive. Whack a mole."

After evaluating incumbents and other startups focused on data observability—the "did I get the right amount of data at the right time" verbiage—Gorkem concluded they were solving the wrong problem. "What I need is my business people to say this data is fit for purpose. It is actually able to be used."

The 100-Conversation Rule

As the pandemic began in 2020, Gorkem and his co-founder started building Qualytics with a hard constraint: validate with 100 prospects before writing production code.

His network of CTOs and CDOs, plus PE portfolio connections from his previous company, provided access to potential customers who could stress-test his hypothesis. The goal wasn't confirming the problem existed—he'd lived it. The goal was understanding whether prospects recognized they had the problem, how they currently addressed it, and what they'd actually pay to solve it.

"I wouldn't start an idea and go after it if I hadn't talked to 100 people first," Gorkem states. This wasn't informational interviews about pain points—it was NSF I-Corps-style validation: show rough sketches, understand what happened when they hit the pain point, probe how it hurt them financially or operationally.

The first six months focused on competitive research and customer conversations while his co-founder built the initial prototype. Only after validation did they line up beta customers and raise their pre-seed round.

Repeatability Lives in Weekly POC Patterns

Early customers came from Gorkem's network, but the inflection point arrived in late 2022 when Qualytics closed their first paying customer with no prior relationship—a large enterprise that required navigating procurement without SOC 2 certification or multi-year track record.

Gorkem's approach: convert the free POC into a paid one. Not for revenue, but for commitment. "I convinced one to make it into a paid proof of concept... there is shared risk. Now I'm not taking all the risk for you do a free POC and then you walk away."

Getting the MSA signed upfront forced both parties through legal and security review before technical validation started. That customer 10x'd their initial contract and remains with Qualytics today.

But one successful customer doesn't prove repeatability. For Gorkem, product-market fit crystallized when POC behavior became predictable by week. "When I started seeing the trends between proof of concepts that we're running... when I see everybody getting replicability of what I would expect within that poc, I can say, okay, week two, I'm expecting this. Week three, I'm expecting this."

That pattern recognition—emerging in late 2023 and early 2024—enabled them to build ROI calculators and internal champion enablement materials. "Now I can start thinking, let me put an ROI calculator together for you. Let me actually help you sell this internally."

The patterns were consistent enough to systematize and hand off to professional sellers. Qualytics raised their Series A.

Simultaneous Sales and Marketing Leadership Hiring

Gorkem made a structural decision that runs counter to typical founder instinct: he hired sales and marketing leadership at the same time, not sequentially.

"I felt very strongly that sales and marketing leadership need to start at the same time and that I am keeping them accountable to go to market motions and go to market metrics, not just sales metrics and marketing metrics because the two have to be working in lockstep."

Before Series A, the company operated with essentially no marketing budget. "We had spent no money on marketing and we had an absolute shit website." The company unveiled their rebrand two days before this conversation—a corporate identity finally matching product maturity.

For sales hiring, Gorkem leveraged his and co-founder Eric's practitioner credibility to hire for complementary skills rather than domain expertise. "I can teach you data quality, I can teach you data operations. You don't have to be an expert in data quality to be able to do the sales motions. I can teach you that. I cannot teach you how to grind, how to have that grit within you."

The division of labor: technical practitioners (Gorkem, Eric, customer success, sales engineering) maintain empathy and credibility with buyers who've lived similar problems. Enterprise AEs and BDRs bring the sales motion discipline.

In-Product Engagement as Leading Churn Indicator

Beyond net revenue retention, Gorkem instruments granular product usage: frequency of data quality operations, number of anomalies discovered, how actively customers edit rules. These are leading indicators—engagement drops precede churn by months.

"I pay a lot of attention to within a poc, within our customers... how often are they doing DQ operations, how many anomalies are they finding, how many rules are they editing, what's the kind of amount of activity that I have within my product."

When engagement signals decline, he doesn't schedule a QBR for next quarter. "I'm not going to do a QBR with you. I'm going to pop into a weekly meeting that you have with my team and as the CEO, I'm going to have a little conversation with you what's working, what's not working and adjust accordingly."

The intervention latency matters. "If I only look at NPS once a year, I have already failed."

Vertical-Specific Business Outcome Translation

Qualytics' most important positioning decision was refusing to sell "data quality" as a horizontal data engineering tool.

"Data quality is not a data problem. It's a business problem. It has to be a collaboration between business users and data users. I'm not selling to a data engineer. I'm selling to an organization with the goal that they're going to engage business users."

In practice, this means translating technical capabilities into outcomes each vertical cares about. Insurance companies hear about premium leakage. Banks hear about OCC and SEC data controls and regulatory compliance requirements. Supply chain companies hear about their specific operational problems.

"Let's say that you're an insurance company and you have premium leakage problems or you are in supply chain and you have different problems. Those may not be called data quality, but they're data quality behind the scenes. So when I understand that vertical and I can talk to a bank about their data controls and their regulatory requirements from the OCC and SEC, they take us a lot more seriously than here's another sales guy pitching me on a data quality product."

This requires research into each vertical's regulatory environment, operational pain points, and executive-level business metrics. When you walk in speaking their language about P&L impact, you're not another vendor—you're someone who understands their world.

The AI Forcing Function

When Qualytics launched, some enterprises classified data quality as "nice to have." AI adoption eliminated that optionality overnight.

"Thanks to all the AI innovations that are happening. Every single enterprise has AI initiatives and AI budgets," Gorkem explains. The implications are stark: "Imagine this. I have, through AI enablement, every single employee in my organization, in my 20,000 person organization can go ask questions, interrogate data and come up with insights that are previously unknown. Somebody didn't build that dashboard. You are actually interrogating it and coming up with it. Great. Do you trust that data?"

For most enterprises planning to democratize data access through AI interfaces, the answer is no—and that realization is driving demand. "In order to trust that data, you have to have a robust data governance program. You have to have a data quality product like quality. You have to have your data catalog, et cetera. You have to have your data ecosystem in order."

The result: "The amount of sales activity, the amount of inbounds that we're getting is exploding now. And I think that's going to be 10x in just 2 years very easily because of the amount of AI adoption that is happening."

Gorkem's timing wasn't accidental. He built during the "nice-to-have" phase so the product would be ready when external forces—AI adoption, in this case—made data quality mandatory infrastructure.

His ultimate goal: "I want my company to be the name that is synonymous with data quality."

For a founder who once spent 18 months building something nobody wanted, that ambition rests on a foundation of ruthless validation, repeatable POC patterns, and the understanding that the best enterprise products solve business problems, not technical ones.

Seven takeaways from this conversation.

Actionable for undefined founders

  1. Talk to 100 prospects before writing code—even with deep domain expertise
    After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally.
  2. Repeatability appears in micro-conversions during trials, not just closed-won rates
    Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion.
  3. Use paid POCs as a procurement front-loading mechanism, not a revenue play
    Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity.
  4. Hire sales and marketing leadership in parallel and hold them to unified GTM metrics
    Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system.
  5. Instrument product engagement as your earliest churn signal—then intervene immediately
    Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process.
  6. Translate technical capabilities into vertical-specific business outcomes
    Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it.
  7. Time your market entry to when "nice-to-have" becomes "must-have"
    When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller.

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