Tomáš Čupr

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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A list of 30 European AI startups that raised €10M+ Series A rounds in H1 2025, all likely hiring: ▪️ AppliedAI – AI + Insurance (London) – $55M ▪️ Tandem Health – AI + Health (Stockholm) – $50M ▪️ Quibim – AI + Biotech (Valencia) – $50M ▪️ NexGen Cloud– AI + GPU infra (London) – $45M ▪️ Samaya AI – AI + Research tooling (London) – $43M ▪️ Bioptimus – AI + Biotech (Paris) – $41M ▪️ Latent Labs – AI + Biotech (London) – $40M ▪️ Relay – AI + E-commerce (London) – $35M ▪️ cartagon.ai – High performance GPUs – $32M ▪️ Salience Labs – AI + Chips infra (Oxford) – $30M ▪️ Unique – AI + Finance (Zürich) – $30M ▪️ Sereact – AI + Robotics (Stuttgart) – $25M ▪️ Wordsmith AI – AI + Legal Tech (Edinburgh) – $25M ▪️ Maze – AI + Cloud security (London) – $25M ▪️THEKER Robotics – AI + Robotics – $21M ▪️ Apheris – Model performance (Berlin) – $20.8M ▪️ Synthflow AI – AI + Voice Interfaces (Berlin) – $20M ▪️ GetWhy – AI + Research (Copenhagen) – $17M ▪️ ecoplanet – Energy Management (Munich) – $16M ▪️ Validation Cloud – AI + Web3 Infra (Zug) – $15M ▪️ TurinTech AI – Code Optimisation (London) – $15M ▪️ Gradient Labs – AI + CS (London) – $11.8M ▪️ Aive – AI + Video Automation (Levallois) – $12M ▪️ Stotles – AI + Public Sector Sales (London) – $10M ▪️ Inven – AI + B2B M&A SaaS (Helsinki) – $11.2M ▪️ Yaspa – AI + Fintech (London) – $12M ▪️ Jua.ai – AI + Weather Forecasting (Zürich) – $10M ▪️ ocell – AI + Climate Intelligence (Munich) – $10M ▪️ Flank – AI + Enterprise Governance (Berlin) – $10M ▪️ tldraw – AI + Developer Tooling (London) – $10M 𝗧𝗵𝗿𝗲𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝘁: • 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗔𝗜 𝗶𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁: 70% of rounds are in applied sectors like biotech, insurance, infra, and voice. • 𝗟𝗼𝗻𝗱𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗲𝗽𝗶𝗰𝗲𝗻𝘁𝗲𝗿: 11 of the 30 rounds are London-based, especially infra and vertical AI tooling. • 𝗕𝗶𝗼𝘁𝗲𝗰𝗵 & 𝗜𝗻𝗳𝗿𝗮 𝗹𝗲𝗮𝗱 𝗶𝗻 𝗰𝗵𝗲𝗰𝗸 𝘀𝗶𝘇𝗲: 6 out of the top 10 rounds are in biotech or GPU infra. 📩 Get these in your inbox → https://lnkd.in/dTwZY4Wg 📸: credit to Simon Vatinel on Unsplash #startups #tech #founders #vc #ai

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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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If you want to stay on top of the latest trends and developments in the startup and venture capital ecosystem, these 30 charts from Carta will give you a clear and insightful picture. Some key insights: 1. Q2 2025- Total capital raised by startups using Carta stands $23.7B 2. New round every 18 months is not happening - Seed to Series A- 1.6year to 2.1year - Series A to Series B- 1.8year to 2.6year - Series B to Series C- 1.7year to 2.5year 3. Interestingly, Seed stage valuations are at all-time highs- $15.6mn and Series A valuation is $49.5mn 4. For AI SaaS companies at the Seed stage, median pre-money valuation is $18.1mn, whereas for non-AI companies, the valuation is $12.9mn. And at Series A pre-money valuation stands at $54mn and $43 mn simultaneously. 5. Median Dilution per round: - Priced Seed- 20.1%- 20.2% - Series A- 19% to 20.4% - Series B- 14.5% to 16% - Series C- 13% to 12% - Series D- 7.8% to 14% - Series E- 6.2% to 7.7% 6. ARR at Series A: - Top Quartile- $6.8mn - Median- $2.9mn - Bottom Quartile- $1.3mn 7. Team sizes are shrinking across stages 8. Hiring timeline: Average Days from Incorporation to - First hire- 320- 476 days - Second hire- 393- 552 days - Third hire- 476- 628 days 9. Equity Grants for top 8 hires: 75th Percentile - Hire 1- 4% - Hire 2- 2% - Hire 3- 1.2% - Hire 4- 1% - Hire 5- 0.8% - Hire 6- 0.66% - Hire 7- 0.55% - Hire 8- 0.5% 10. Ownership Check in: Pre-Fundraising - Founding Team- 89.5% - Advisors- 0.5% - Employee Pool- 10% After Fundraising (median Ownership): - Founding Team- 56.2% - Employee Pool- 11.8% - Investors in Round- 21% - All prior investors- 11% 11. Ownership check in: after Series A- Founders are below 50% 12. Median total investor ownership crosses 50% between Series A and Series B 13. Q1 2025 data shows a rise in M&A activities, reaching 180 deals, while Q2 recorded a total of 150 deals. 14. Regarding IPOs- let’s see and wait Thank you, Peter Walker, for creating this beautiful document. ~~~~~ ♻️ Found this helpful? Repost it so your network can learn from it, too. And follow me, Fazlur Shah, for more content like this. #startups #entrepreneurship #venturecapital #investing

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Most founders overcomplicate growth strategy. If you're below $1,000,000 a year, do this: ❌ Avoid: - Testing 50 new ads per week - Tweaking sales script every call - Changing your landing page monthly ✅ Instead: - Clearly map your customer journey - Start collecting data on every step - Research industry 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 - Spot your biggest bottleneck - Fix it, then do a new one Focus on this until you get big enough so you have specific teams and departments, working on individual parts of the process. Until then, here's how you can do it: 𝟭/ 𝗠𝗮𝗽 𝘆𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 ↳ List all major steps people take from their first interaction with your business. 💡 Pro tip: Add steps after the sale, like activation, retention, referrals, etc. 𝟮/ 𝗦𝘁𝗮𝗿𝘁 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗲𝘃𝗲𝗿𝘆 𝘀𝘁𝗲𝗽 ↳ Gather all metrics in a simple Google Sheet this will help you understand what's up. 💡 Pro tip: alongside conversion rates, track the time it takes to pass each stage. 𝟯/ 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 ↳ Ask ChatGPT, talk to people, spy on competitors, read industry reports, listen to podcasts... Find the data! You need to know what's considered a good CTR, good contact rate, good close rate in your space, for businesses of similar size and services. 𝟰/ 𝗦𝗽𝗼𝘁 𝘆𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 ↳ Compare the success of your user journey with industry 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀. Find the most lagging. 💁‍♂️ Example: Your contact rate is 40%, the industry average is 60% while your close rate is 10%, the industry average is 30%. Your contact rate is 1.5 times smaller than 'average.' Your close rate is 3 times smaller than the 'average.' Rank all your bottlenecks like that and work on what sucks the most. 💡 Pro tip: sometimes your close rate is bad because you don't qualify prospects enough. Keep in mind that each stage affects the next one. 𝟱/ 𝗙𝗶𝘅 𝗶𝘁, 𝗼𝗻𝗲 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲 ↳ Now, when you lined up your bottlenecks, analyzed probable causes, and picked one to fix. Dive into tactics if you don't know how to do it. 💁‍♂️ Example: You figured that your close rate sucks, so this is your target to solve. Why tho? • Google how to increase close rate • Read a chapter from a book • Watch a YouTube video • Ask your mentor • Figure it out 1. Close the knowledge gap. 2. Come up with the hypothesis. 3. Implement the change and test. Worked? — Great. ↳ Move to the next problem to solve. Didn't? — Great. ↳ Lesson learned. Try another way. This is about as complex as it should be before $1M. Good luck! P.S. Do you use benchmarks in your strategy? ♻️ Repost to help founders simplify growth. ➕ Follow for more business growth insights.

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This week, OpenAI launched Instant Checkout, collapsing discovery, decision, and transaction into a single conversation. No more “browse → cart → checkout.” It’s “say the thing → own the thing.” Under the hood, it’s powered by the Agentic Commerce Protocol (ACP), co-developed with Stripe, which handles payment securely while letting merchants keep control of fulfillment, customer data, and the existential dread of shipping logistics. For now, it’s limited to Etsy sellers and single-item purchases. But Shopify’s in the pipeline, multi-item carts are coming, and international expansion isn’t far off. Think of this as the starter pistol. It’s easy to get carried away imagining the future. So let’s do that: - Your calendar buys the gift before you remember the party - Your fridge notices you’re low on oat milk and orders a refill - not from the cheapest vendor, but the one aligned with your values (organic, minority-owned) - Your AI negotiates a new phone plan, scans hidden fees, and switches you automatically - Your kids grow up thinking “shopping” is something old people did, like waiting in line at the bank. The endpoint isn’t faster checkout. It’s commerce collapsing into cognition. Here’s how I see it: (1) The Interface Becomes the Intermediary OpenAI just muscled into the middle of the transaction - not as a store, but as the interface to all stores. Amazon, Google Shopping, Shopify fought to own the storefront. OpenAI’s fighting to make the storefront irrelevant. If they succeed, we’ll see a platform power shift with familiar risks: discovery bias, fairness, monetization pressure. OpenAI swears it ranks listings “purely on relevance”. History suggests that will stay true… right up until Sponsored Results v1.0. (2) New Monetization Playbook Until now, OpenAI monetized via subscriptions and enterprise. They generated ~$4.3B revenue in 1H25, about 16% more than all of last year. Instant Checkout could unlock something far bigger: a slice of global commerce. It turns ChatGPT into a monetizable intent router that doesn’t just answer questions but closes the sale. Reuters noted OpenAI plans to take a cut when merchants fulfill through ACP. Given ChatGPT’s large user base and user engagement, even a modest commission on a fraction of purchases could scale massively. (3) From Brand Loyalty to Agent Preference In the old world, brands fought for customer loyalty. In the new world, they’ll fight for agent preference. Price, availability, ethics, carbon footprint, return policy - all become structured data feeding a ranking model behind the scenes. If your brand isn’t machine-readable, it’s invisible. The CMO of the future won’t optimize media spend - they’ll optimize for prompts. The future isn’t “voice shopping” or “buy buttons in chat.” That’s just plumbing. The future is delegated will: where you express desires, and agents convert them into transactions, logistics, and outcomes. No friction. No funnel. No follow-through.

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