Andrew Pinelli

We are announcing Potpie AI's $2.2M pre-seed fundraise to advance Spec-Driven Development for large enterprise codebases. The round is led by Emergent Ventures, with participation from All In Capital, DeVC, and PointOne Capital, along with the support of some amazing angel investors from companies including Atlassian, OpenAI, Meta, Razorpay and Flobiz. As AI accelerates code generation, the constraint inside large enterprises has shifted from coding to maintenance and assurance. The limiting factor is no longer writing code, but understanding complex systems, aligning teams around intent, and safely evolving large, interdependent codebases. In most organizations, specifications exist as static documents, while production systems evolve independently. Context is fragmented across repositories, tickets, logs, reviews, and floating documents making reliable AI adoption difficult. We are building the foundational layer that makes Spec-Driven Development executable at scale. By unifying engineering context and operationalizing the spec as a structured source of truth, we enable AI systems to reason with architectural awareness rather than surface-level code completion. We are already working with large enterprise customers, including Fortune 500 organizations. This milestone allows us to deepen those partnerships and support more teams transitioning from experimental AI usage to structured, production-grade AI-first engineering workflows. If you are leading engineering at scale and evaluating how AI should integrate into mission-critical systems, we would love to chat with you!

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As some of you know, I’ve been working quietly on a new startup called elsie AI. It’s part of 1848 Ventures, where our mission is to help small and midsize businesses. The backbone of America. While I bring experience from consumer products and marketplaces that reached millions, building a B2B SaaS from zero to one is a (fun) new game for me. I’m learning fast. Here are a few early takeaways. 1. Building AI prototypes as lead gen and learning tools Tools like Replit and Lovable make it quick to test ideas and get feedback. We’ve been using small AI tools as both learning experiments and ways to attract early interest in elsie. Here are three we’ve built so far: https://lnkd.in/d3j65bjs - to create cool social posts based on your e-commerce products. www.site-roast.com - tongue-in-cheek website health checker https://lnkd.in/dzyj5ey6 - to help you monitor subreddits and find where to engage We’re still figuring out the right tactics, but it’s been a fun and scrappy process. 2. Community building on Reddit I never really used Reddit before, but our audience hangs out there. So that’s where I’ve been spending time. Two weeks in, 46 contributions, 37 karma points. No bans yet. That's good, right? It’s noisy, but when you find the right subreddits, the conversations are gold. 3. Learning from e-commerce experts and design partners I don’t pretend to be an e-commerce expert. Luckily, there are many who are. We’re spending time with solo e-commerce founders and industry experts who can test our ideas and help us shape the product. If you know someone I should talk to, please tag them or send them my way. 4. Building in public We’re a small but mighty team. And we want to bring people along from the very start. Even when our MVP isn’t all that “viable” yet. We’ll launch a public beta early, share what’s working (and what’s not), and keep learning from the feedback. 5. The advantage of being part of 1848 Ventures Being part of 1848V gives us access to best practices across the portfolio. The companies may look different on the surface, from food compliance to e-commerce, but the growth and GTM lessons overlap more than you’d think. It helps us move faster and more precisely. And luckily, my colleagues are way smarter than me. If you’d like to follow along, sign up for the waitlist at www.elsie-ai.com. And I’d love to hear from you. What strategies or tools helped you go from zero to one? #AI #Startups #B2B #SaaS #Entrepreneurship #Founders #BuildInPublic

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📌 Health, experimentation, and shipping faster: here's what I'm focusing on in 2026 📌 As we enter 2026, I want to reflect on last year and express gratitude to everyone who made it meaningful. To my family, my colleagues at Amazon, and the startup founders and technopreneurs I connected with, for their support, inspiring conversations and shared learning. Health is my #1 priority this year. Without it, I can't show up fully for my two young daughters, my team, or our customers. I'm committed to finding time for healthy eating and exercise, even while balancing the demands of parenting and career. It's not easy, but it's non-negotiable. With that foundation, here's where I'm focusing my energy professionally in 2026. 1️⃣ First, fostering a culture of strategic experimentation. AI technology is still evolving, and we don't yet know its full potential or gaps. I'm taking a pragmatic approach: testing it on a handful of well-defined customer problems with clear goals in mind. I want to encourage my teams to experiment, but with constraints and intention. If something doesn't work, we pivot quickly without falling into the sunk cost fallacy. This is where Amazon's leadership principles of Learn and Be Curious, Bias for Action and Are Right a Lot come together. We think carefully, move decisively, and treat these as two-way door decisions. Learning by doing is how we'll discover what truly works. 2️⃣ Second, once those experiments prove valuable, shipping production-grade code faster. My biggest frustration in 2025 was having PRFAQs and BRDs ready but waiting on dev resources to bring them to life. We will continue to leverage AI coding assistants combined with robust software development practices to solve this bottleneck. I've been reading a few great resources - "How Big Things Get Done", "Diving into system design on AWS", and working through "The DevOps Handbook" to ensure we're building on solid fundamentals, not just moving fast for the sake of speed. 2026 will be the year AI experiments face real ROI scrutiny, which means we need to work backwards from goals and outcomes, not from the technology itself. Think slow during experimentation, move fast when shipping. 3️⃣ Third, helping my team unlock their potential. In an environment where the pressure to deliver is constant, I want to create space for people to be builders, tinkerers, and experimenters. Celebrate failures and learnings along with success. This ties back to that measured Bias for Action approach: taking smart risks on reversible decisions, learning what works, and iterating. When we empower people to experiment and learn by doing, that's when real innovation, beyond what we can imagine today, truly happens.

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