Rahul Reddy

Non-Engineering Roles at Early-Stage Startups Offering $120K+ Base Salary with Meaningful Bonus and Equity: Chief of Staff @ Milo (Consumer), Remote Biz Ops Lead @ Fractal Agriculture (FinTech), Remote; Backed by Virta Ventures Chief of Staff @ CakeAI (AI SaaS), NY; Backed by Primary Venture Partners Demand Generation Specialist @ Rootly (Consumer), Remote; Backed by YC, 8VC, Gradient Ventures Account Executive @ CommandBar (Enterprise SaaS), Remote; Backed by Soma Capital, YC, Insight Partners Product Marketing @ Slope (Enterprise), Remote; Backed by YC, Union Square Ventures, Liquid 2 Ventures Supply Planning Manager @ PROVEN Skincare (Consumer), Remote; Backed by YC, FJ Labs, Soma Capital, Social Capital Account Executive @ Accrue Savings (Consumer), NY; Backed by Ground Up Ventures, Tiger Global Chief of Staff @ UNIGRID Battery (Climate), CA; Backed by Union Square Ventures, LiquidMetal Ventures Job details and the full list (40+ opportunities) are in the comments below. If you're interested in non-tech jobs focused on specific industries like HardTech, HealthTech, Climate, etc., then DM me or let me know in the comments.

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LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

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🔥 Hot off the press: “What's 🔥 in Enterprise IT/VC - Issue #406” The race to be the last mile AI platform for enterprises - lessons from Palantir's Bootcamp model (BLG is the new PLG), Glean, and others... Full post here: https://lnkd.in/ednmvtCc "Well, this is exactly what Palantir is doing - fixing the last mile problem, aiming to be the platform from which every enterprise builds every single application. What does that tell you? The value is not in the foundational model or LLM, it’s in the workflow and the last mile to make it super easy for enterprises to conceive, build, and deploy GenAI in the enterprise in a rapid, accurate, cost-conscious, privacy preserving, and secure manner. That is the race of the future and every single company that starts with a wedge solving a specific problem will eventually end up in this race. What’s also interesting is how both of these companies go-to-market. This is real enterprise sales - there is no PLG, freemium play at the moment but…all that being said, both are focused on delivering insane Time-to-value or time to the “Aha” or “Wow” moment for its customers. Look at Palantir’s execution from this past week as it continues to leverage its new model - the AIP Bootcamp - BLG for the win!" Read full post here: https://lnkd.in/ednmvtCc

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Sharing this primarily for markets outside the top 5 in VC as it's a problem I've experienced as well. One can and sometimes should criticize SV founders who have too much ambition in the way some pursue it, but a super serious problem almost everywhere else is not enough ambition, certainly to include NM. It may surprise some to hear that Seattle lacks ambition given headquarters of Microsoft, Amazon, Costco, Starbucks and others, but that's actually part of the problem--golden handcuffs and conformity, versus hungry and competitive when I grew up in WA in business in the 80s. It's also a problem with many VCs around the country now. They have become so focused on flipping to incumbents their (BT) dominance became a self-fulfilling prophecy. That's not the role of VCs -- if that's their goal they should be a CVC. NM has a similar golden handcuff problem to Seattle, but it's worse in some respects. The national labs pay really well relative to the local cost of living, so it's the career goal of thousands to spend entire careers working for the labs. In both markets the level of early stage funding is so low that it's difficult to leave big tech or labs to join a startup. And in both, the experience of big tech and labs doesn't apply well to startups. Very different world. A related challenge is SV rarely invests outside the region in would-be market leaders--most want to see those remain in CA. So we have a problem for certain, and big disconnect in the market.

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Why we invested: Gratsi Wine 🍷💸 We led the seed round of Gratsi back in November of '22. At the time, they had zero distribution and their ecommerce revenue was modest. But we were blown away and ultimately decided to partner with the team for a few key reasons: 1. Metrics and efficiency: the underlying metrics of the business were unlike anything we've seen in the space. Gratsi's ability to acquire and retain customers (online and in retail) is unrivaled. 2. Potential for premium boxed wine: we remain convinced that premium boxed wine will continue to become one of the most important categories in the wine space. The volume, margin, and customer experience implications are massive. 3. Founder vision and execution: Stephen Vlahos has been executing relentlessly against his vision since day 1. As we met and got to know Stephen in the early days, it quickly became obvious to us that he would stop at nothing in his mission to build one of the most important wine companies in the world. It's been an amazing two years for Gratsi since we invested and I'm incredibly proud of what the team has accomplished. But I'm perhaps even more excited for the years to come. This team is truly just getting started. Exciting times ahead for Gratsi and the team! cc: Top Shelf Ventures Noah Slakter Jason Sherman

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PE - How to Use AI to Unlock Growth in Portfolio Companies All PE firms and portfolio companies should be thinking about unlocking GTM and growth via AI (i.e. not merely ChatGPT or other LLMs but actually implementing apps that are "AI Agents"). 1. What are "AI Agents"? - AI agents are autonomous systems powered by ML and natural language processing, designed to perform tasks with minimal human intervention - They can interpret data, determine the workflow & direction (i.e. make decisions), and execute actions across various functions, continuously improving through self-learning 2. Examples of using AI Agents in PE portfolio companies: - For Sales: AI agents handle lead qualification, respond to inquiries, book meetings, and provide personalized product recommendations, ensuring no opportunities are missed - For Marketing: Generate campaign briefs, segment audiences, create tailored content, and analyze performance to optimize outreach efforts - For Customer Success & Support: Provide 24/7 support, resolve customer issues autonomously, escalate complex cases to human agents with full context, and ensure consistent, brand-aligned responses 3. How PE portfolio companies benefit: - Efficiency: Automate repetitive tasks, enabling teams to focus on high-value activities - Scalability: Handle growing customer volumes without increasing headcount - Personalization: Deliver tailored experiences using real-time data insights - $ Cost Savings: Reduce operational costs by streamlining workflows 4. Business Impact for PE Portfolio Companies: - Accelerate GTM Execution: Faster lead conversion, better customer retention, and enhanced marketing ROI - Improve Metrics: Boost Customer NPS, CSAT, and efficiency - Enable Growth: Scale operations seamlessly while improving team productivity. 5. How PE Firms Must Think About Adoption - Define clear portfolio objectives to use AI agents (not just LLMs) - Ensure integration with existing CRM and data systems - Prioritize high-quality data and ongoing performance monitoring - Use AI agents to complement existing GTM teams, focusing them on automation ------------------ #pe #privateequity #investing #ai

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P2S.vc News: Datuum Raises $200K to Streamline Data Integration with AI Automation Datuum.ai, a US-based leading AI-powered no-code platform for data onboarding, has raised $200K through the Pre-Seed to Succeed program. Investors in this round include I2BF Global Ventures, Yellow Rocks, AltaIR Capital, and Smart Partnership Capital. This new funding from the P2S investment program will enable Datuum.ai to expand and fine-tune its platform for broader use. With data volumes expected to reach 175 zettabytes by 2025, companies face mounting costs, time, and human resources challenges when integrating data from multiple systems like CRMs, ERPs, and IoT devices. Datuum's platform offers a seamless solution, automating the process of mapping and transforming data, reducing the need for manual intervention, and making data onboarding fast and easy for users—regardless of their technical expertise. Founded in 2021 by CEO Leonid Nekhimchuk and CTO Dmytro Zhuk, Datuum has solved some of the biggest challenges in data processing. The platform simplifies data workflows, allowing businesses to scale without increasing operational complexity. An impressive track record of innovation backs the team of seven. Before this round, Datuum secured $850K from SID Venture Partners and Sigma Software Labs. P2S.vc targets ambitious early-stage startups and focuses on several industries, including AI, Productivity Tools, B2B, SaaS, FinTech, the Future of Work, Digital Health, and HR Tech. The investment program offers initial funding from participating venture firms, ongoing support from seasoned experts and mentors, and 50% of the next funding round for proven startups.

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Old school and probably not popular but I still believe products should be customer-satisfaction focused/led rather than technology-led. Use cases for AI should be byproducts of the product improvement process primarily. The thing though is that founders need to fundraise so they're likely to sprinkle some AI on their decks to even look serious. Not sure that's how the most enduring companies have been built. Of course, if sources of capital become AI-biased, founders have little choice. Happy to keep an open mind and dive in when a use-case that can 10x customer experience for Good Tenants customers and users shows up.

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An interesting article below, that demonstrates out how San Diego is punching above its weight.  And how Connect's strategy and execution over time, contintues to be central to that success. The article describes the current state of the market in Seattle.  (And i love Seattle.) It's a market that we tend to track with relative to venture fundings.  They used to kick our butts, we outraised them each of the last three years.  This despite the fact they have 3x as many funds there, and 9x the amount of resident capital there. (per pitchbook) Some key takeaways:  * They continue to compare themselves to SIlicon Valley. Instead, we leverage our proximity. *They whine there's not enough local investors (see note above - they have more than us). We bring over 200 VCs to SD annually! * They say founders are not connected with one another. We bring CEOs together regularly, in a variety of ways - private dinners and through our Springboard program. * They say they need to elevate their image on a national & international stage. Why we created and continue to build Five.Ten.Thirty (aka Inno Day).  * And the last paragraph - they need to concentrate on making their region a great place to live. Our mantra: "It's about Better, not Bigger." (See XEO, TL Fund). THANK YOU FOR ALL OF YOUR SUPPORT. WE ARE ON A MISSION TOGETHER!!!  (Comments, whining, suggestions on SD always welcome.) https://lnkd.in/g6Rq_f2Y

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🌟 Highly recommend this course from Working Backwards! 🌟 One of the highlights of my year was the incredible opportunity to work with someone I’ve admired and studied for years. Colin’s and Bill's insights and approach are not just inspiring—they're transformative. This course stands out because it’s practical, actionable, and makes it easy to start implementing right away. The power of input metrics has always fascinate me. In a world where businesses are often fixated on output metrics, understanding how input metrics drive and amplify results has been a game-changer. It’s a perspective every organization should embrace to unlock their full potential. If you're eager to learn from the best and rethink how you measure success, this course is a must! 🙌

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In vertical SaaS, the very best companies landed with a product solving a specific, burning pain point that horizontal solutions solved badly and then became broad product suites over time (ServiceTitan, Veeva Systems, Doximity, etc). My partner Gordon Ritter calls this “layering the cake”. In vertical AI, the landing wedges are much more brittle. While the painpoints they solve are very real (and often unaddressable by SaaS alone), they’re also much more likely to commodotize in the near term as the tech advances. 👀 Therefore, the need to layer the cake in vertical AI is much more urgent than in vertical SaaS. 👀 The best vertical AI CEOs not only recognize the dynamic, but talk about it openly. Helps those around them keep up the speed of innovation and their eyes on the bigger prize.

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Hot take on a lot of early-stage digital health companies out there: You're not a tech company. Most tech-enabled care companies I've seen over the past two years are only tech-enabled in the sense that they use telehealth as their delivery mechanism for care. There are exceptions to this, of course - companies who build computer vision and applied AI tools to better the doctor-patient experience, companies who build tooling around risk scoring and stratification to enable more personalized medicine, companies who build truly novel intakes to better route patients to the appropriate care, etc. These companies have real technology chops and, if they sell it right to the market, can get credit for this from investors. But if you're truly a telehealth provider, you need to realize now that the goal post has shifted dramatically from 2021 and you're going to be valued at maturity at something like 3x EBITDA. That's profit. Not revenue. Throw a revenue multiple out the window. That's not the world we live in if you're a virtual doctor's office. Think of how that impacts venture math. If you're raising a seed round at $15M post-money (or $12M pre-money), it means you'll have to generate something like $150-200M in annual EBITDA to be a fund returner for your seed investors. Maybe even more with dilution, since telehealth businesses are not traditionally capital efficient and will likely need to raise material additional capital to reach that scale. You need to be ridiculously profitable (probably on $800M-$1B in annual revenue since you're likely going to be a 40% gross margin business). Here's the good news: you can set yourself up well (and probably be a contrarian founder relative to your peers), by talking about EBITDA growth and how you can grow into a fund-returning outcome based on realistic mature market comps. This will stand out for the investors you're pitching, even at the early stage. This is how you can beat the malaise of the tech-enabled care market. Focus on the right metrics and the right type of growth. You don't have to have all the answers (no founder does), but you need to make sure the ship is headed in the right direction. A focus on profitability for "tech-enabled care" (*ahem* telehealth) businesses is a good first step.

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"You need to be loud and assertive to be influential." This is the biggest myth in product leadership. Last month, we hosted an incredible Supra speaker session with Yue Zhao (fmr Product Exec at Fuzzy, Meta, and Thumbtack). Her counter-intuitive insight? The most influential PMs aren't the loudest or most aggressive. They focus on three pillars: 1/ Be knowledgeable ↳ Master your domain  ↳ Know your metrics cold ↳ Understand adjacent areas ↳ Stay current with industry trends 2/ Be accessible ↳ Make it easy for others to reach you ↳ Share context proactively ↳ Break down silos ↳ Build bridges across teams 3/ Be kind ↳ Lead with empathy ↳ Give credit generously ↳ Support others' growth ↳ Create psychological safety The reality?  True influence comes from being someone others WANT to work with. Not from being the loudest voice in the room. What other traits have you found essential for building influence?

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Team San Diego! I though you may find this as surprising as I did...we are not alone. Read this quick article from Jon Prentice's (Silicon Valley Bank) interview with with James Newell (Voyager Capital), published in GeekWire on Seattle's innovation ecosystem....they lament the SAME issues as we do in San Diego: * Lack of angel investors, (Especially, it calls out, given the local giant tech co's) * "We need low-conviction check writers." * Lack of "cachet associated with being an angel investor" - We have similar efforts (as described in the article) to drum up angel activity: NuFund Venture Group and, Interlock Capital (read the article and you'll see why the Interlock effort is tops as relates to "paying back into the ecosystem") Connect.org strives to 'connect' innovative companies with investors of all types (angel, VC, PE...) - just see "Innovation Day". Their Springboard program provides another great way for experienced entrepreneurs to give back through mentorship...as for the companies going through Springboard: "Ask for advice, and you often get $$" Your thoughts on the article or San Diego innovation ecosystem? https://lnkd.in/gfwq6mfq

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Interesting answer from AlphaSense CEO Jaakko (Jack) Kokko on why they acquired Tegus - all about access to proprietary data, in this case the transcript library on private companies. You recently acquired a company called Tegus. Was this about having more proprietary data to feed into your models? First and foremost, it was about providing our customers with the best possible intelligence on the market, especially on private companies. A couple of years ago, we acquired a company called Stream, the No. 2 player in what you might think of as a library of interview transcripts between investors and experts on a company — their former executives, competitors, customers, partners. It allowed us to produce information that you can’t access anywhere else, and our customers loved it. Tegus is the market leader in that sector and was the natural next step for us. They have the world’s best library of private company content, and we immediately wrapped our search capabilities, our generative AI, our monitoring capabilities around their content and made it much more valuable. We now have the broadest coverage of insights on both public and private companies, allowing our AI to provide even better answers to our customers.

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I've seen countless startups stumble on regulatory strategy - it's painful, expensive, and often avoidable. The seminar shared by David Pudwill and Geetha Rao hosted by UCSF Innovation Ventures, absolutely nailed what matters most. Here are my key takeaways that every founder should know: 1. Understand your scope - this is where it all starts or stops You've got to nail this foundation: --Map out ALL potential clinical uses (yes, all of them) --Find your "regulatory siblings" (similar approved devices) --Get crystal clear on your impact level: 1)Wellness management (easiest route) 2)Screening/monitoring (middle ground) 3)Diagnostics/therapy (time to bring your A-game) 2. Pick your lane: regulatory pathways from "Easy" to "Buckle Up" Here's what you're looking at: --510(k) Exempt: The startup favorite (for good reason!) --510(k): Tried and true path --De Novo: The road less traveled (but sometimes necessary) --PMA: The regulatory marathon (pack your endurance) 3. Strategic roadmap: This part separates winners from the pack Smart execution is everything: --Start minimal, but make it smart --Grow strategically (think chess, not checkers) --Nail the execution sequence: Define → Design → Test → Produce → Sell Pro tips from the investor seat - know your audience and speak their language: --Investors need that solid IP story --Clinicians want real-world outcomes --Regulators love familiar territory (use it) --Claims strategy (this is crucial) Three rules to live by: --Start small (seriously, resist the temptation) --Expand methodically (no shortcuts) --Document everything (future you will thank you) Why this matters to investors - here's the bottom line: "Your regulatory strategy IS your market strategy. Plain and simple - you'll only get to sell what you get approved for. Plan accordingly." (Full credit goes to David and Geetha for sharing their invaluable insights)

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If someone says their "data is integrated with AI," it ironically means a RAG chatbot behind a vector database 90% of the time. The real deal is - You cannot rely on RAG to get aggregative insights from a large corpus of unstructured data. RAG is for point-wise data access. - You need to bring unstructured data closer to the data team. Most are in some random S3 that your data team has no access to. - You need to enable your data team to do quantitative data analytics on unstructured data with LLM, not letting them build one chatbot after another. 🛑 Stop investing in chatbots; invest in quantitative unstructured data analytics. #dataanalytics #dataengineering

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