Sycamore Secures $65M Seed Round for Enterprise AI Platform

Sycamore Labs provides a full lifecycle platform for enterprise AI: discover, build, deploy, observe, and evolve.   We're excited to lead the company's $65M seed round and support the team as they build the operating system for autonomous enterprise AI. Learn more below ⬇️

Glean is truly the leader in this space. And they've perfected not only the AI Deployment, but they are also the best with regards to their application connectors and the maintenance of them. Connecting your data sources is not a one time set it and forget it at deployment. those connectors have to be maintained. My opinion is it's better to let someone handle that who has serious expertise then have a stalled in-house deployment and THEN have to continue maintaining all the connectors. Plus, they've got the security and compliance locked down.

Peter Sarlin is launching QuTwo, a startup focused on preparing enterprises for the quantum computing era. Learn how they're building hybrid solutions and AI agents for today's challenges, with insights from Devignitor Insights. https://lnkd.in/dmidbYhq

[𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗶𝘀𝗸 𝗦𝗶𝗴𝗻𝗮𝗹] 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 𝗩𝗮𝗹𝗹𝗲𝘆'𝘀 𝟮𝟬𝟮𝟲 𝗔𝗜 𝗵𝗲𝗮𝘁 𝗺𝗮𝗽 — 𝗹𝗮𝗯𝘀 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝘁𝗼 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝘀. Silicon Valley's leading AI startups focus on cutting-edge research labs driving the 2026 revolution.Why it matters: Concentration in SV labs means founders outside must differentiate on go-to-market speed. Research-to-revenue is the new moat.Founder Takeaway: Partner with SV labs for tech access while owning your distribution edge.Watch Next: Lab spinouts and IP licensing deals accelerating commercialization. https://lnkd.in/gGrJ5bEF #NathanWildman #PhoenixVentureStudios #MarketIntelligence #BusinessFunding #AIForBusiness

[𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗶𝘀𝗸 𝗦𝗶𝗴𝗻𝗮𝗹] 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 𝗩𝗮𝗹𝗹𝗲𝘆'𝘀 𝟮𝟬𝟮𝟲 𝗔𝗜 𝗵𝗲𝗮𝘁 𝗺𝗮𝗽 — 𝗹𝗮𝗯𝘀 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝘁𝗼 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝘀. Silicon Valley's leading AI startups focus on cutting-edge research labs driving the 2026 revolution.Why it matters: Concentration in SV labs means founders outside must differentiate on go-to-market speed. Research-to-revenue is the new moat.Founder Takeaway: Partner with SV labs for tech access while owning your distribution edge.Watch Next: Lab spinouts and IP licensing deals accelerating commercialization. https://lnkd.in/gGrJ5bEF #NathanWildman #PhoenixVentureStudios #MarketIntelligence #BusinessFunding #AIForBusiness

[𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗶𝘀𝗸 𝗦𝗶𝗴𝗻𝗮𝗹] 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 𝗩𝗮𝗹𝗹𝗲𝘆'𝘀 𝟮𝟬𝟮𝟲 𝗔𝗜 𝗵𝗲𝗮𝘁 𝗺𝗮𝗽 — 𝗹𝗮𝗯𝘀 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝘁𝗼 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝘀. Silicon Valley's leading AI startups focus on cutting-edge research labs driving the 2026 revolution.Why it matters: Concentration in SV labs means founders outside must differentiate on go-to-market speed. Research-to-revenue is the new moat.Founder Takeaway: Partner with SV labs for tech access while owning your distribution edge.Watch Next: Lab spinouts and IP licensing deals accelerating commercialization. https://lnkd.in/g4G7ep5i #NathanWildman #PhoenixVentureStudios #MarketIntelligence #BusinessFunding #AIForBusiness

We're excited to announce our $80 million Series A round to further develop the world's first multi-silicon inference cloud. We're at ASPLOS this week, so feel free to reach out if you're attending and would like to know more.

Super proud to announce our Series A today. We are on our way to building the next generation multi-silicon inference infrastructure. Thank you to our investors for believing in the vision. Menlo Ventures Factory Prosperity7 Ventures Triatomic Capital

Thrilled to share today that Gimlet Labs, Inc. has raised a $80M Series A round. Over the past few months, there's been incredible momentum around making frontier AI agents faster and more efficient. As a user of coding agents myself, I don't want to have to pick between high quality and high speed - I want both. The performance/scale needed for these agents won't come from homogeneous infrastructure, but that's today's status quo. At Gimlet, we take a different approach - dynamically slicing up the workload, including fine-grained disaggregation within individual models - and running each slice on the best hardware. This involves deploying workloads across GPUs, CPUs, and specialized accelerators. It's a big challenge and wouldn't be possible without our incredible 30 person team, who bring deep expertise across the entire AI infrastructure stack. I'm grateful to work with all of them. Read more in the blog below, and check out TechCrunch's coverage here: https://lnkd.in/gjfSJBFF

Right now, most AI workloads still run on generalized infrastructure. But as models scale, performance gains increasingly come from something deeper in the stack: how efficiently software can work with the hardware itself. That’s the opportunity Standard Kernel Co. is tackling. When we met Chris Rinard and Anne Ouyang, their clarity around the problem and their ability to solve it stood out immediately. Rare founder–problem fit tackling a massive opportunity. We're proud to lead Standard Kernel Co.’s $20M seed round. We’re excited to partner with Chris and Anne alongside an exceptional group of investors, including General Catalyst, CoreWeave, Felicis, and Ericsson. The AI infrastructure stack is still being built. We believe Standard Kernel will help define an important layer of it.