Viktor Levchenko

Ever spent hours digging through messy databases just to answer a simple research question? Last week, I spent 2 hours digging through databases trying to find German SaaS startups with AI-focused CTOs who recently raised Series A funding. You know the drill: bouncing between Crunchbase, PitchBook, and LinkedIn. Filtering by country, then funding stage, then manually cross-referencing CTOs' backgrounds. Compiling spreadsheets. Starting over when I realized I needed different data points. Sound familiar? My friend Ravit Jain introduced Deep Lookup at Bright Data to me when I shared my challenge with him. All I need to do is just tell Deep Lookup what I’m looking for: "Find all SaaS startups in Germany that raised Series A in the last 12 months and have a CTO with previous AI/ML experience. Show me company details, funding info, location, CTO LinkedIn, and team size." It takes just a minute for it to return with clean, relevant, and useful output. And I can add more context like CEO backgrounds or revenue estimates to enrich my search without starting over. Why these matters: ✅ I can focus on analysis instead of data hunting ✅ No more drowning in irrelevant results ✅ Research feels less like detective work, more like having a conversation ✅ Tailored for market mapping, startup scouting, or due diligence prep ✅ Built for researchers, investors and strategists who value their time. Anyone else tired of spending more time searching than analyzing? Would love to hear how you're handling research workflows these days. Bright Data #DataLoop #Search #AI #Researcher #Investors To stay current with the latest trends in #Technology and #Innovation, Subscribe to 👉 #CXOSpiceNewsletter here https://lnkd.in/gy2RJ9xg Or 👉 #CXOSpiceYouTube here https://lnkd.in/gnMc-Vpj

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Pylon's pricing model has changed 10 times. Here's how 👇 1/ Seats (1 tier) 2/ Flat Rate 3/ Platform Fee + Add-Ons 4/ Platform Fee + Add-Ons + Scaling Pricing 5/ Seats (1 tier) (was meant to be temporary) 6/ Seats (2 tiers) 7/ Seats (3 tiers) 8/ Seats (3 tiers) + AI Assistants 9/ Seats (3 tiers) + AI Assistants + AI Agents 10/ Seats (3 tiers) + AI Assistants + AI Agents + Account Management The thrash is from product and vision evolution, and market expectations. To start we just wanted to charge more than $0 to prove value. After that we raised prices by saying random flat-rate numbers and seeing where we got pushback. As our product expanded, we figured we may as well charge more for the additional products that felt distinguished as we released them. Then we thought people who use certain parts of the product more should be paying more, so we introduced scaling-pricing. Then, our product and vision developed where we began replacing support products like Zendesk and our prospects were getting confused on how to compare pricing, so we decided to mirror the existing pricing model in the market to simplify. Then we started introducing new SKUs that scaled pricing (AI Assistants and AI Agents). Then we launched Account Management whose pricing also grows with usage because of AI functionality (and whose pricing for now mimics that of a customer success platform). And we're not done. Pricing will continue to evolve. Pricing is messy, there's no way around it. We've simply continued to try stuff and iterate quickly. -- Notes -- 1/ Our pricing has become more complex as we've become multi-product which has been confusing to customers. But it makes sense when you look at the SKUs as replacing existing products: - Pylon Seats → Zendesk Seats - AI Agents/Assistant → Fin/other support Agents - Account Management → Customer Success Platform 2/ Long-term we'll likely iterate towards replacing all of the add-ons with a credit-based system similar to Clay, where you buy packages of credits, where each credit can afford you an action (e.g. write a knowledge base article, draft a reply, ask AI a question, AI-tag an issue, etc.)

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Venture Capital is quietly mutating. The old playbook was simple: → Fund 25 startups → Wait 10 years → Hope 1 or 2 become unicorns. That game is over. Today, the smartest funds in the world are doing something different: • Buying shares from employees before IPOs (secondaries) • Snapping up old businesses and rebooting them with AI • Building companies in-house, not just funding them Sequoia Capital, Lightspeed, General Catalyst, Thrive Capital - they’re all playing this allocator game. Here’s how - • Sequoia split its business, created an evergreen structure so it can hold companies forever, buy secondaries, act more like a PE firm than a classic VC. • Lightspeed registered as an RIA (Registered Investment Advisor), which legally allows them to do way more than early-stage VC - including secondaries, public stocks, and roll-ups. • General Catalyst didn’t just fund startups they acquired an entire hospital system in the US. That’s PE-style operating, not VC spray-and-pray. • Thrive Capital raised a $1B “permanent capital” vehicle focused on AI and secondaries, so they can buy big chunks of late-stage companies and hold them long-term. But here’s the catch: founders and operators are still being told to think only in “startup vs VC” terms. That’s yesterday’s world. The winners of tomorrow will be the ones who know how to play across the entire capital stack - secondaries, roll-ups, AI-native bets, liquidity plays.

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Ever notice how founders overload their own offers and slow sales? Clients face too many choices. Packages stack up and blur. Confusion grows before checkout. • Add-on setup calls for basics. • Extra templates they never use. • Hidden tiers behind vague names. Stress builds before they buy. Indecision slows the sales call. Regret follows after the deal. Less choice makes action easy. 1. One clear package - faster decisions. 2. Simple steps - lower buyer risk. 3. Clear value - stronger trust. Simplicity closes deals more often. Audit your offers for clutter today. #founder #BusinessGrowth #OpsMastery  

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Let's be realistic; most startups won’t IPO. Here’s how most companies actually exit: ► 𝗠&𝗔 (Strategic)  Sold to a company for strategic value. Pros: High payout, brand boost. Cons: Long process, complex integration. Typical timeline: 6–18 months. ► 𝗠&𝗔 (Financial) Sold for financial ROI (e.g., PE firm). Pros: Faster, numbers-driven. Cons: Potential for restructuring. Typical timeline: 4–12 months. ► 𝗔𝗰𝗾𝘂𝗶-𝗵𝗶𝗿𝗲 Bought primarily for team. Pros: Talent retained, soft landing. Cons: Low payout, no real “win.” Typical timeline: 3–6 months. ► 𝗜𝗣𝗢 Public listing. Pros: Massive upside, prestige. Cons: Expensive, long road, regulation. Typical timeline: 5–12+ years. ► 𝗦𝗣𝗔𝗖 𝗠𝗲𝗿𝗴𝗲𝗿  Go public via acquisition by SPAC. Pros: Faster than IPO, liquidity. Cons: Less prestige, SPAC sponsor misalignment, high redemption rates. Typical timeline: 1–2 years. ► 𝗣𝗘 𝗕𝘂𝘆𝗼𝘂𝘁  Bought by private equity firm. Pros: Liquidity for founders/VCs. Cons: Less innovation post-deal. Typical timeline: 2–7 years. ► 𝗦𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆 𝗦𝗮𝗹𝗲  Founders/early investors sell shares. Pros: Partial liquidity before exit. Cons: Needs buyer, more common post-Series B. Typical timeline: Post-Series A. ► 𝗘𝗦𝗢𝗣 𝗟𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆  Team sells shares on secondary market. Pros: Team incentivized. Cons: Rare unless company is hot, often requires company facilitation. Typical timeline: Post-Series B+. ► 𝗔𝘀𝘀𝗲𝘁 𝗦𝗮𝗹𝗲  IP, product, or customer list sold. Pros: Last-resort cash out. Cons: Usually fire-sale price. Typical timeline: 1–3 months. ► 𝗦𝗵𝘂𝘁𝗱𝗼𝘄𝗻 (Soft-Landing) Close company, place team or IP elsewhere. Pros: Maintains reputation, salvages value. Cons: No real financial return. Typical timeline: 1–6 months. For all you startup founders: Plan early. Exit well. Source: Quang Phan - https://lnkd.in/dNsfcqgZ Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ] 

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I shared a simple framework for choosing startup ideas a while ago (here's the post: https://lnkd.in/d6Rub75Z). Once you've narrowed them down, there's the next question: What type of startup model are you building? Every idea fits into one of three models: Niche Model — serves a specific market segment with specialized solutions. Trend Surfer Model — catch an emerging wave and ride it with discipline. Problem Solver Model — obsess over a pain point and eliminate it. Why does this matter? Because each model requires a different mindset and knowing which one you're in keeps you aligned with yourself, your team, your customers, and investors. The model defines how you build, scale, and raise capital. 🟦 Niche Models demand depth and patience. You win by serving a tight market better than anyone. Growth can be slower, but defensibility is high if you stay focused. Examples: Etsy built specifically for creators and handmade sellers. Or Chewy, which created lasting loyalty by going all-in on pet owners. 🟦 Trend Surfers need strong filters. You're moving fast on a rising wave, but discipline is critical. Many panic when bigger players enter and may pivot too early. Examples: TikTok captured the trend toward shorter attention spans and more authentic, less polished content. Beyond Meat zeroed in on replicating meat, winning over flexitarians, not just vegetarians. 🟦 Problem Solvers are usually obsessed with the problem they’re trying to solve. You're building for one pain point. The biggest risk here is when founders drift by adding features for side problems until the original vision gets lost. Examples: Grammarly refined writing for clarity. Headspace made meditation easy and habitual, starting with beginners, not experts. I go deeper into this in my book Unicorn Focus. The Key to Building Billion-Dollar Startups in the Age of AI. I've released the full chapter, The Art of Choosing a Business Idea, as a free preview. Feel free to download it on my website (link in the comments). So let me ask: Which model are you building — Niche, Trend Surfer, or Problem Solver? #unicornfocus

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