Please introduce your company and describe your role as CEO within the organization.

I’m Oleg Kulyk, CEO and co-founder of ScrapingAnt – a web scraping platform that makes the messy parts of extraction (proxies, headless browsers, CAPTCHAs, Cloudflare, rotating fingerprints) disappear behind a clean API. I set product direction, keep the infra resilient, and spend a lot of time turning chaotic customer requirements into repeatable, boring systems.

What inspired the story behind founding or leading this company?

I’ve been shipping things since I was a kid. When the full-scale war hit Ukraine, a lot of founders paused – our team doubled down. We built a service that stays up when life doesn’t: no drama, just data. The mission is simple – turn the hostile, ever-changing web into a reliable data layer for normal companies.

What is your company’s core business model – do you use an in-house team, third-party vendors, or a hybrid outsourcing approach?

Our core business model is simple: we keep the brain in-house and rent the muscle when it makes sense. All the things that are ScrapingAnt stay inside the team – the scraping engine, the API, the way we orchestrate headless browsers, how we dodge anti-bot systems, how we turn messy pages into clean JSON, and how we keep the whole thing alive during bad days. That’s product DNA, and I don’t want that scattered across agencies and freelancers.

At the same time, I don’t believe in owning every screw in the infrastructure. For elastic capacity, we use vendors: proxy networks in different geos, cloud compute, and sometimes a specialized service for very narrow tasks. But they’re always plugged into our control plane – our routing, our health checks, our monitoring. If a provider disappears tomorrow, the platform shouldn’t even flinch.

So you could call it a “hybrid” model, but in practice the line is very clear: strategy, critical systems, and anything that touches trust are fully in-house; commodity pieces are vendor-backed and fully observable. We don’t outsource “projects” in the classic sense. If we work with partners, it’s on small, well-defined chunks that we can turn off or replace without affecting the core. That’s how we stay lean, fast, and more like a product company than a body-rental shop.

How does your company differentiate itself from competitors in a crowded market?

We don’t try to win by shouting louder, we try to win by being the thing people stop thinking about. Most scraping tools sell “unlimited power” and then leave you alone with captchas, bans, and broken scripts. Our angle is the opposite: you give us a URL and a data shape, and our job is to make sure it keeps working next week, next month, after the next redesign, and during the next crisis.
The other difference is how we talk to customers. We don’t promise magic “undetectable scraping.” We tell you what’s realistically possible, where the legal and technical edges are, and we architect around that. People stay because they trust that if we say “yes,” it’s not a sales trick – it’s based on logs, not vibes. In a crowded market, that combination of resilience, honesty, and very operator-friendly UX is enough. You don’t need fireworks when the thing just works.

What are the primary industries or sectors you serve, and how has that focus evolved over time?

Started with e-commerce & pricing intelligence. Expanded into travel, marketplaces, fintech/alt-data, adtech verification, and research/media monitoring. Trend: more structured extraction (schemas/JSON) rather than raw HTML – teams want clean tables, not pages. We began serving more AI companies since the LLM gold rush emerged, but it is still an emerging market for us.

What are the most in-demand services or solutions that clients approach your company for?

• Scraping API (fetch + headless + anti-bot handling). That’s our core technology.
• AI-assisted extraction (LLM-guided field mapping + deterministic validators).
• Managed projects for high-stakes sites (playbooks, compliance review, monitoring dashboards).
• Datacenter and Residential proxies. For clients who’d like to use DIY approach and use their own browsers.
• Web Search API for discovery + capture pipelines It’s basically a Google available via API, but data is gathered by our own technology.

How do you personally stay ahead of industry shifts when most data is already yesterday’s news?

I don’t chase “news,” I chase signals that actually change how systems behave.

Most data in this industry is stale by the time someone turns it into a blog post or conference talk. By then, you’re just copying what worked for someone else months ago. So I try to stay closer to the raw feed: our logs, our customers, and my own small experiments.

So I don’t try to stay ahead by reading more hot takes. I stay ahead by instrumenting reality, listening carefully, and touching the tech myself often enough that I notice when something feels “off” before it becomes obvious.

Do you have a significant percentage of repeat clients? If so, what strategies contribute to that loyalty?

Yes – high repeat usage. I’d say about 90% over a 6-month period or so.
Drivers: consistent success rates, honest scoping, no lock-in games, fast answers from real engineers, and migration help when clients sunset DIY scrapers.

How do you measure and ensure high customer satisfaction in your operations?

On the hard side, we watch very basic but very honest numbers: success rate per domain, latency, error spikes, and weird traffic patterns. If those are healthy, customers usually are too. When something dips, we act before anyone opens a ticket. A surprising amount of “great support” is just fixing things while the customer is still formulating the email.
We do the usual check-ins and feedback loops, but the real goal is simple: if you’re using ScrapingAnt, you shouldn’t have to think about scraping much. When customers stop talking about the plumbing and start talking only about what they build on top, that’s my favorite “satisfaction metric.”

What kind of post-project support do you provide to address client queries or ongoing needs?

We’re not a heavy, enterprise-style shop with three layers of account managers.

If we’ve worked together on a project and you need help afterward, you can still reach out to us: we answer questions, make adjustments when targets change, and help you maintain a healthy pipeline. How exactly that looks – a Slack channel, email, or a small support package – we usually just agree on it together based on how much hand-holding you actually need.

Describe your pricing and billing structure – is it fixed cost, pay-per-milestone, or another model?

For the core product, it’s very straightforward: usage-based with a monthly rhythm, not some mysterious “custom enterprise” thing.

If you’re using the API, you pick a plan that gives you a certain volume of requests/credits per month, and if you go over, you just pay overage. No per-milestone invoices, no hourly guessing game. Most customers know roughly how much traffic they’ll push, so they lock that in and treat it like any other piece of infrastructure.

When we do more involved, managed work – like owning a whole extraction pipeline for a set of tricky sites – we usually frame it either as a fixed-scope package or as a monthly retainer with clear responsibilities and SLAs. The idea is the same: you know what you’re paying, you know what we’re on the hook for, and you don’t get surprise bills because we “spent more hours.”

What is the typical price range for projects you’ve handled in the past year, and how do you balance affordability with value?

It really depends on what “project” means in your case.

Some clients just use the API on standard plans, others have ongoing managed setups across multiple sites and countries. We’ve handled everything from small, tightly scoped engagements to long-running, complex pipelines. The common part is that we try to price it so it’s a clear win versus building and maintaining the same thing in-house, not some luxury add-on.

Have you turned down projects based on budget or scope? If so, what are your minimum requirements?

We decline anything that looks like abuse, PII harvesting, high legal risk, or “scrape the whole internet tomorrow.” For managed work, we expect clear goals, target list, data schema, and an internal owner. Minimums exist for a custom scope-dependent.

What key challenges has your company faced in the last few years, and how did you overcome them?

• War-time resilience: power/internet failover stacks, mirrored infra, async ops – kept churn basically zero when it mattered most.

• Anti-bot arms race: layered tactics (request → browser → fingerprinting → supervised solver), plus continuous testing farms.

• Talent bandwidth: documented playbooks, fewer heroes, more systems.

How do you foster innovation and adapt to emerging trends in your industry?

Tiny experiments, fast rollbacks. Internal “brown-bag” demos weekly. If a test cuts failure by 2% on a hostile site, it ships. If it adds complexity with no lift, it dies.

What role does company culture play in your success, and how do you build and maintain it?

Calm, practical, “boring on purpose.” We value clear writing, clean runbooks, and shipping fixes over hot takes. People can do their best work only if the system is boring. We value employees’ feedback, but we prefer turning it intoa  pre-defined plan for execution before doing it.

Where do you envision your company in the next 5-10 years? What are your boldest long-term goals?

From “scrape pages” to “query the web as structured data.” Think declarative extraction (you define the fields, we guarantee the table), compliance-aware pipelines, and agents that maintain your data flows autonomously.

How has your leadership style evolved throughout your career, and what influences it?

From builder-in-every-PR to systems designer. Fewer adrenaline nights, more clarity → rest → speed. I try to remove blockers and keep the scoreboard visible.

What emerging technologies or market shifts are you most excited about for your company?

On the tech side, I’m very interested in autonomous browsing agents that are not just “let an LLM click randomly,” but are wrapped in real guardrails, observability, and cost controls. If you combine that with lighter, sandboxed browser runtimes – WASM-based or similar – you get something that feels closer to a programmable, distributed browser layer than a pile of headless Chrome instances. On top of that, the LLM + rules hybrid pattern is finally getting practical: let the model propose a way to extract or normalize data, and let deterministic checks, schemas, and validators decide what’s acceptable. That’s a very natural fit for what we do.

On the market side, the shift I’m watching is that companies are moving from “give me HTML, we’ll handle it” to “give me a clean table I can plug straight into my models or dashboards,” and they are increasingly concerned with provenance and compliance. That pushes us toward being less of a “scraping tool” and more of a reliable web data layer with traceability: you know where the data came from, how it was transformed, and that it won’t suddenly disappear because a random script broke. All of these trends line up nicely with where I want ScrapingAnt to go.

What advice would you give to aspiring CEOs? Can you share one lesson from your journey that resonates with the business community?

Pick useful, boring problems and out-execute. Talk to customers, instrument everything, write it down, and keep your system simple enough that it survives bad days. The fancy parts come later; reliability pays now.