Bain Capital Ventures (BCV)

The Enterprise Tech 30 list is out for 2026, and we’re proud to see four BCV portfolio companies recognized. From cutting-edge AI models to industry-specific SaaS platforms, the 2026 list spans the full enterprise stack across five core categories, reflecting just how quickly the landscape is evolving. Crosby: The AI law firm built for execution with lawyer-assisted AI to review MSAs, DPAs and NDAs in under an hour. Decagon: Building, optimizing, and scaling AI agents that treat every customer like the only one. Legora: Building the world’s first truly collaborative AI for legal professionals. Mintlify: Helping teams create and maintain world-class documentation built for both humans and AI. Congrats to these teams for earning a well-deserved spot — and for pushing the boundaries of what enterprise software can be. https://lnkd.in/gkyUKjye Wing Venture Capital

Christina Melas-Kyriazi at BCV, joined the Get the Check podcast to share lessons from scaling Affirm through IPO to now backing the next generation of AI companies. From recruiting an Olympian to her high school cross country team to what she learned working closely with a founder — holding both vision and detail at the same time — to where value will accrue across AI labs vs. applications, and why mission-driven companies may outperform, it’s a candid look at how she thinks about building and investing. 🎧 Worth a listen: https://lnkd.in/gvhjeUnz

The Information named Rak Garg to their 2026 List of Next General Partners. Selected by founders and investors for being able to spot high-growth AI startups, the list includes rising stars in venture capital. Rak has been putting in the work for years and we are thrilled to see it recognized. Congratulations to Rak and the full 2026 class — a list worth watching.

We’re ready for AI that doesn’t just assist you—but thinks like you. Meet The Sentience Company. In our latest Outlier Briefings, Sentience founder and CEO Sam Kececi shares the vision for a more personal form of AI—one that captures your conversations, remembers your context, and can act on your behalf. As AI becomes more widespread, most models default to the same outputs for everyone. Sentience takes a different approach: starting with the individual, preserving what makes each person unique, and building systems that reflect how you actually think and work. This is AI that reflects how you think, not just what you ask. The future of AI shouldn’t flatten what makes people unique—it should extend it. We’re leading their seed round, and are excited to introduce the world to The Sentience Company. cc: Kevin Zhang

Fast Company's Most Innovative Companies list is out for 2026, and we’re proud to see several of our portfolio companies recognized. 🏆 Congrats to Crusoe, Docusign, Gather AI, MagicSchool AI, Redis, ShopMy, and Unstructured for pushing their categories forward. Well-deserved recognition for the teams building and shipping at a high level. Proud to be on the journey with you. 👏

Food delivery has been treated as distribution. Swish treats it as a system. Kitchens, recipes, automation, and logistics designed together — to deliver food faster, cheaper, and more reliably. India’s quick commerce boom has reset expectations around speed and convenience, but food delivery is still optimized for planned, higher-value meals. Swish is built around everyday eating, not just lunch and dinner: breakfast, snacks, chai, late-night, and solo meals. The team is not trying to out-marketplace incumbents, but compete with the home kitchen itself. With a full-stack model and dense network of micro-kitchens, Swish can deliver fresher food in minutes at price points the traditional model struggles to reach. It’s not just pulling demand from elsewhere — it’s unlocking new demand. In under 18 months, the company has scaled rapidly in Bangalore, with some of the busiest kitchens anywhere and clear signs of repeat, habit-forming usage. Today, we announced that we co-led their $38M Series B with Hara Global Capital Management. With this model, food delivery doesn’t just improve — it becomes infrastructure. Congratulations to Aniket Shah, Ujjwal Sukheja and Saran S. The pace and focus of this team stands out, and we’re proud to be a part of it. More here: https://lnkd.in/gmtwtQFF Saanya Ojha Ajay Agarwal

Everyone talks about how AI will change software development. We need to talk about what won’t change. If anything, AI is forcing a return to first principles, increasing the importance of the SDLC: ➰ If code is generated, specs need to be precise. Bad instructions = scalable garbage. ➰ If output is probabilistic, reviews need to focus on intent, not syntax. ➰ If no one wrote every line, testing becomes your only source of truth. AI is less a replacement for engineering rigor than an efficient machine for punishing its absence. The abstraction is rising, but the surface area of complexity is exploding underneath: more services, more dependencies, more non-deterministic behavior. Someone still needs to reason about architecture, failure modes, and tradeoffs. This isn’t a critique of the technology. It’s excellent and improving quickly. But using it well still requires systems thinking and technical judgment. Generating code is not the same as building software, in the same way producing words is not the same as making a legal argument. Engineering starts after the code exists: validating it, integrating it, deploying it, and ensuring it doesn’t break something downstream at 2AM. Today, everyone can build - but few can build systems that last. That distinction is about to get expensive. Engineering orgs run by non-technical leadership are especially likely to learn this lesson the hard way. Expect a wave of premature cost-cutting followed by rehiring cycles once the Sev 1s start stacking. If you don't understand the distinction between “more code” and “more reliability,” AI will eventually explain it to you in production. This dynamic is amplified by the internal politics of large orgs. Scope gets claimed by overpromising, timelines get pulled forward to win resourcing. AI pours fuel on this - making it easier to show rapid early progress and paper over weak foundations, at least temporarily. The bill still arrives. On the ground, engineers are already seeing the pattern. AI works best in existing codebases with strong patterns - it learns structure and extends prior decisions. But when used to “vibe code” from scratch, it often produces brittle systems: inconsistent abstractions, awkward interfaces, and code that looks fine until it’s asked to scale. So teams have two choices: 1️⃣ Let the system emerge through prompting → fast, messy, brittle 2️⃣ Define architecture and constraints upfront → slower, but durable Most teams are choosing (1). Not because it’s better - but because it’s incentivized. When output is measured in PRs and velocity, there’s little reward for designing clean systems or thinking ahead. Complexity gets deferred and paid back later with interest. AI is making coding easier but it won't make engineering less ‘technical’. Eventually, every builder will be forced to internalize the tenets of the SDLC: version control, testing discipline, structured iteration.

Cellular networks are some of the most important infrastructure in the modern world and some of the least secure. For decades, telecom has been optimized for reliability: can you make the call, send the text, keep the tower online. Identity, location, and metadata were treated as exhaust. That design made sense in another era. In today’s world, where these networks underpin enterprise operations, civilian life, and national security, it’s no longer enough. That’s why we’re leading Cape’s $100M Series C alongside IVP. Instead of replacing global telecom infrastructure, Cape rebuilt the part that matters most: the core. By controlling how devices authenticate, how signaling is routed, and what data is exposed, they turn existing cellular networks into programmable, secure infrastructure without replacing a single tower. The implications are broad: secure mobility for governments, resilient connectivity for autonomous systems, and privacy-first networks for enterprises and individuals. Re-architecting telecom requires deep technical conviction and a willingness to take on systems most assume can’t be changed. John Doyle and team have spent years doing exactly that. If reliability defined telecom in the last century, security and survivability will define it in this one. We’re partnering with Cape and John Doyle as they build the next layer of global connectivity. https://lnkd.in/gsqBTDgn cc: Saanya Ojha, Enrique Salem

Memo is a robot that uses AI to perform household tasks effectively. Today, Sunday announced its Series B, and we’re proud to be investors. The home is one of the hardest environments to automate — messy, dynamic, and full of edge cases. That’s why Sunday Robotics is training robots directly on real households. Founders Tony Zhao and Cheng Chi have spent years advancing how robots learn from human demonstrations. Their glove-based system allows hundreds of contributors to record everyday tasks in their own homes, creating high-fidelity demonstrations that feed directly into robot learning. Home robotics will be defined by companies that learn fastest from real homes. Sunday Robotics is building that learning loop. 🔗 More at the link in comments. cc: Aaref Hilaly Amanda Huang

RJ Scaringe built Rivian from scratch. Today we’re proud to back his next company, Mind Robotics. RJ built Rivian with a deeply integrated approach across hardware, software, manufacturing, and supply chain. That kind of system creates powerful feedback loops between the product and the factory floor. Mind Robotics starts with a similar philosophy — and with something most robotics companies spend years trying to access: a real industrial environment where the technology can train, improve, and compound over time. We’re proud to partner with RJ and the Mind Robotics team as they work to expand what automation can do. cc: Ajay Agarwal Rak Garg