Partnering with Preview: Lights, Inference, Action

There’s something magical about watching a developer in flow state, fingers flying as they type into Codex, Claude, or Cursor. A powerful creation interface unfolds in response to every keystroke, powering a workflow that would have been unrecognizable, even alien-like, to any professional software engineer just two years ago. The entire IDE has been torn down and rebuilt from scratch for the age of AI, and developers are flying as a result.

Video models have raced ahead over the same two years. Yet unlike coding, video creation remains stuck in the stone age, with its entire toolchain still built around capturing, uploading, storing, and editing enormous files.

Generative video is at a threshold moment: the models are now expressive enough to become the new camera. The bottleneck is no longer the technical art of animation or the $100M budgets for film crews and visual effects; it’s human creativity and token inference. But video creators are not yet flying.

Without a proper AI-native tool suite, or a “Cursor for video,” the people who make films, TV series, and commercials for a living remain stuck bolting model outputs onto legacy editing pipelines. Generation, review, and production tracking each live in a separate application; workflows buckle under the sheer volume of material; and a finished take often can’t be traced back to its source for legal clearance. It’s a gaping problem and is the biggest roadblock on the industry’s path to truly embracing AI.

Preview clears that block. An AI-native video creation and production platform built for professional creators, Preview combines a video timeline with an infinite canvas for ideation into one collaborative multiplayer workspace. Teams can generate with any model and track every character, location, and prop, with complete records of how every asset was made. It enables both halves of a hybrid production to work together seamlessly. There is simply nothing like it in the market.

Co-founders Stefan Fejes and Veljko Tornjanski have been best friends since high school in Serbia, and were seemingly born for this mission. They were founding team members at video creation pioneer Veed; later, at Vizcom, Veljko was the first hire and became Head of AI, while Stefan led Product. They understand well the tricky nuances of video, they have deep empathy for their customers, and they have delivered sophisticated, polished work on impressively short timelines.

More than 100 studios are already actively using Preview, from agencies making commercials for Fortune 500 brands to AI-native studios to Hollywood feature filmmakers. Simply put, they love it. Customers tell us it has become the most significant tool in their work, and the single source of truth from which all downstream inference flows. 

We are at the precipice of a video token tsunami, and many billions of dollars of inference will run through this category, driving the next wave of iconic films and IP. Value will accrue to where the work actually happens—the platform a creative team opens in the morning and closes at night, where the decisions get made and the record gets kept. We believe Preview is that platform, and that this is the right team, the right product, and the right moment. We are thrilled to partner with Stefan and Veljko and to lead their seed round.

Partnering with Corma: Closing the Defensive Cybersecurity Gap

Cybersecurity is a perpetual fight between the good guys and the bad. The age of agentic AI and Mythos-class models has tipped the scales in favor of the bad guys, opening a Pandora’s box for offensive capabilities. An explosion in CVEs has since ensued. 

Cyberattacks are about technical exploitation, which requires coding and reasoning prowess. Anthropic’s Mythos can discover and exploit zero-day vulnerabilities. Any model paired with a harness brings the cost of social engineering attacks down to near zero, unleashing them at unprecedented scale. The danger is heightened with every new model release. 

Defensive security isn’t improving in parallel. The relevant data lives in logs, events, traces and telemetry, a fundamentally different modality than the bodies of text LLMs usually train on, which barely show up in pretraining. Unlike offensive cybersecurity, which involves goal-directed reasoning towards a defined objective, defense requires open-ended reasoning, continually finding anomalies across vast amounts of normal-looking activity, a distinct capability that typically demands extensive domain-specific training. In short, defense is out of distribution for general-purpose frontier models: neither the data nor the intuition it requires is well-represented in training. In the race toward AGI, we cannot expect the frontier labs to take the detour of overhauling their training pipelines for defensive cybersecurity. And we certainly can’t afford to bet our security on a hope that they do.

Corma has illustrated this “defense gap” in testing. They ran red/blue team simulations where an attacker plants a hidden backdoor and a defender tries to find it. The defender failed to find the backdoor 78% of the time, even when the defender was an identical copy of the very same model that planted it. Holding model quality constant, attackers will always have an advantage.

The only answer to scaling laws on offense is better scaling laws on defense. Recent events have shown that frontier AI for defensive cybersecurity is one of the crucial unsolved problems of the AI age. Solving it is the generational mission Corma was founded on.

Corma founder and CEO Alon Pluda and his team are training a foundation model to power defensive cybersecurity agents. Cybersecurity, like Go or chess, is a two-player, zero-sum game with a clean reward (were you breached or not?) and an endless supply of games to play. Reinforcement learning and self-play have produced superhuman results before in this kind of paradigm. We are seeing history repeat itself. Corma pushes this further with large-scale reinforcement learning across cybersecurity environments that replicate real enterprise networks, with all their tools, telemetry and noise – as part of a training pipeline that produces frontier defensive capabilities, outperforming general-purpose foundation models, with much lower per-token inference costs. 

In the cat-and-mouse game of cybersecurity, we believe Corma’s vertical integration and “sovereign AI” approach will win. Cost matters (always-on inference costs can rack up quickly). Owning the model weights matters (they aren’t subject to restrictions on cybersecurity-related usage set by the closed model labs). And specialization and inference speed really matter (fastest to protect against a new threat wins). 

Corma’s foundation model is deployed and productized as an agentic Security Workforce. These workers operate across different security tools and take on roles spanning the entire security organization: from security operations and identity management to cloud and network security and beyond. They are on the job at Fortune 500 companies and large enterprises across healthcare, finance, critical infrastructure, retail and more.

Corma’s foundation model powers agents that complete defensive work end to end. We spoke to one CISO whose Corma agent notified him of a pending attack through his Garmin watch while he was on a walk with his dog in the evening; with one confirmation, the agent was able to shut the attacker down, all while he was still walking. In another instance, within its first hour on the job, Corma uncovered, contained and remediated an active attacker campaign inside a customer’s network that their security team had missed for 52 days. 

Training frontier foundation models for defensive cybersecurity requires world-class hackers and world-class researchers – almost no company on earth has both. Alon is personally one of the most elite hackers in the world, and has assembled a cohesive and interdisciplinary team of hackers and researchers that is hill-climbing on technical challenges and building impressive commercial momentum. 

We are thrilled to lead Corma’s seed round and to be their partner as they chart new territory in what is sure to be one of the most dynamic races in AI.

Partnering with Bunkerhill Health: AI Agents that Improve Patient Outcomes

Healthcare hasn’t historically been known as a fast or early technology adopter. Researchers struggle to access external data, collaborate with research institutions, and navigate complex regulations. Hospitals and health systems try to create their own tools or implement narrow point solutions from vendors, but they too often stall in experimentation and never reach patient care. 

Health systems need the right platform to quickly adopt AI, so they can refocus their time back on improving patient outcomes and providing better care.

Bunkerhill Health is that platform. Their core product, Carebricks, lets health systems create and deploy AI agents across any clinical or operational use case – turning the data a hospital already generates into timely action for the patients who need it. We first backed Nish Khandwala and David Eng at the seed, and we are proud to continue to double down. 

Nish did not read about this problem; he lived it. As a graduate student at Stanford, he helped build an algorithm that could estimate a patient’s coronary artery calcium score from a routine chest scan – a signal of cardiovascular risk that normally requires a dedicated, more invasive test. The algorithm was good enough to be published in Nature Digital Medicine. And then it went nowhere. Around the same time, Nish’s father suffered a heart attack, and his CT scan carried the very calcium signals the Stanford algorithm was built to catch. It is one thing to know that proven science sits unused in academic papers. It is another to watch it fail to reach your own family.

That experience taught Nish something non-obvious: the bottleneck in healthcare AI is not the technology, it is everything around it. So Bunkerhill initially built a system that gets an idea from research to the bedside – sourcing data from a consortium of leading academic medical centers, validating the model, shepherding it through FDA clearance, and installing it into the hospital where a clinician and a patient can actually use it. But they ran into another problem: health systems do not have the time or resources to deploy one-off AI solutions. So Bunkerhill built Carebricks to allow any health system to bring their ideas to life with AI agents across clinical, operational, and administrative work in one central platform. Health systems bring the ideas and the clinical judgment, and Bunkerhill turns them into agents.

We have watched this compound in the field. At UTMB Health, Bunkerhill rapidly went from one agent in production to more than twenty, spreading from one specialty to the next as clinicians saw what it could do. And just as importantly, it has allowed UTMB to consolidate what used to be multiple vendors’ point solutions into a single platform with Carebricks. 

Bunkerhill’s agents run quietly in the background, surfacing the findings that matter and catching the cases the system would have missed. Not to automate clinicians away, but to automate manually-intensive work so clinicians and their teams can focus more time on what they do best: caring for their patients. 

Nish and David are outlier founders with the technical depth and the relentlessness this problem demands, and they are pulling healthcare toward faster iteration and better outcomes for every patient, not just the average one. We are excited to partner with Nish, David, and the entire Bunkerhill team as they build the agentic AI platform for healthcare.

Partnering with Sable: Closing the Diffusion Gap

The frontier of AI is moving at a pace most of the economy can’t match. Labs ship new capabilities every month, while the Fortune 500 scrambles to absorb what came out last year. Watching this gap widen, it’s clear that the bottleneck isn’t only intelligence, it’s helping customers understand what AI can actually do for them. It’s the reason we’re thrilled to partner with Sable.

Imagine your business wanted every buyer to fully understand your product or service, without the realities of time and scale. What would you do? You’d put your best product expert in every conversation. They’d learn each customer’s goals and deeply empathize. They’d demonstrate the product instead of describing it. They’d answer every question, adapt to every level of experience, and stay until the customer understood not just what your product is, but what it could do for them. And they wouldn’t do this for your largest accounts alone. They’d do it for every prospect.

Sable makes this possible. It created “Aidan,” the first AI employee, who leads its own customer calls with vision, voice, video and real-time browser interaction. Sable is less than one year old, but frontier companies are already using it to explain their products, including Notion’s custom agents and Decagon’s agent-building platform, alongside large public enterprises. There is a staggering range of applications and organizations eager to deploy AI employees, from the world’s fastest-growing companies looking to keep pace with demand, to large enterprises seeking to accelerate growth with AI. We’ve been blown away by the market pull for this new capability, with +150 companies already on Sable’s waitlist.

Enterprise deployments of multimodal AI employees require a combination of customer obsession and technological breakthroughs at the frontier of the field: low-latency browser use, real-time vision and simultaneous human-agent interaction. Problems like these are ultimately won by people. Sable is exceptionally well-positioned to solve them.

The four founding team members are friends from Harvard, where they studied post-training, reinforcement learning and multimodality. They worked at SpaceX, Google, Meta and Together AI. Nim, co-founder and CEO, is a rare combination of a leader and visionary. He has an innate talent, and impressive experience recruiting the highest-caliber people to work together and achieve the unlikely. That philosophy has shaped Sable from the beginning: the company is obsessed with hiring exceptional talent. Among their first hires is a unique mix of applied AI researchers and customer-obsessed engineers, including ten Harvard alums, former quantitative traders and International Math Olympiad winners.

The combination of an all-star team, undeniable market demand and bold vision is why we were thrilled to lead Sable’s seed round and co-lead its Series A. We believe Nim, Leon, Linda and Itamar are building foundational infrastructure for companies to manage their AI employees, and we couldn’t be more excited to be on this journey with them.

Partnering with Probook: AI for the Trades

There’s no substitute for authenticity. In a world of AI, real, genuine human connection matters more than ever. The story of Probook starts as real as it comes. 

After decades on the job, John, a New York City police officer, was injured on duty and couldn’t serve on the force any longer. Some time later, he took up power washing to stay busy and tapped his son, George Eliadis, to help alongside him. George worked weekends and summers in high school, then continued to power wash while he pursued the prestigious M&T dual-degree program at the University of Pennsylvania.

George noticed how much logistics got in the way of what his dad loved most: power washing. Years later, George spent time inside TR Miller, a home services business in Illinois that became Probook’s first customer, and saw the same problems at scale. He learned that what slowed his dad down affected millions of electricians, HVAC techs, plumbers and other workers. The administrative load, from assigning jobs to following up on website inquiries, cuts into already-tight margins and hurts both customer and employee satisfaction. 

AI was supposed to take all that on, but reality hasn’t lived up to the hype. Operators bought a tool for every task, and ended up with a stack that couldn’t talk to itself. Nothing connected; costs ballooned. Customer experience stayed broken, and no one’s life got any easier.

George realized his dad’s life—and an entire industry—would benefit from better, more connected software. So he and co-founders Lewis Zhang and Ben Cervantez launched Probook, the AI Operating System for home services—built around dispatch, the foundation everything else depends on. It is a single platform that shares context across the entire customer experience, automating from intake to dispatch and beyond, and expanding margins for the businesses that keep America’s homes running. Probook is personal for George, of course—but it’s not only George who takes this work personally. All three founders are the real deal. Their authenticity shines through, and it’s the reason why we at Sequoia wanted to partner with them from the seed round and now in their Series A.

Probook works like this: your water heater goes out, and you call a local plumbing company that runs on the platform. Probook’s AI picks up immediately, already knowing each technician’s experience, availability and distance from your home, along with their close rates and ticket sizes. It assigns the right tech to the job, alerts them, and keeps you in the loop with an ETA. You get fast help; the company runs leaner; its team earns more with less stress.

Since childhood, George, Lewis and Ben have been scrappy and relentlessly curious. George is a near lifelong entrepreneur—between power washing jobs, he sold everything from coins to vinyl records. He attended Penn’s M&T program along with Ben, who was valedictorian and captain of his high school football team, and interned at McKinsey during college before dropping out to join Probook. Lewis started coding when he was 7, earned degrees in Electrical Engineering and Computer Science from Berkeley in two years, and was part of the critical player-server matching team at Roblox. Dedication is what drives the team: to say they show up for customers is an understatement. They care deeply about tradespeople and are obsessed with taking the drudgery out of their work, flying all over the country to onboard and work with new businesses in person. And for those companies, Probook has been nothing short of life-changing.

Probook’s growing team is scaling quickly, with a mission to transform the trades. When the nerve center of home services is streamlined by AI that actually drives outcomes, operators can run better businesses and build better lives for their staff. We are proud to support George, Lewis, Ben and team.

Partnering with Auctor: AI Autopilot for Software Implementation

For every dollar spent on software, six are spent on services. Why? Because buying enterprise software is easy. Implementing it is hard, and still leans heavily on labour. Across the top 10 software ecosystems (ServiceNow, Salesforce, SAP, AWS, and others), 9 million implementation consultants represent more than $500 billion in annual labour spend, still growing 10%+ a year. It’s one of the largest markets in technology, and one of the least disrupted.

Enterprise platforms have thousands of components that change daily. A single deployment can span hundreds of requirements, dozens of stakeholders, and months of back-and-forth between what a business needs and what the system can actually do. Today, consultants hold all of this together through experience and pattern recognition. But they forget context between meetings. They miss dependencies across systems. They can’t track thousands of platform updates simultaneously. Unlike LLMs, their context windows are limited by biology. 

Auctor is the autopilot for software implementation, from the moment a customer requirement is captured all the way through to delivery. It brings together the requirements, decisions, and context that typically live across meetings, documents, and dozens of disconnected systems, and translates them directly into the outputs needed to move a project forward. What used to take teams weeks of scoping now gets done in a single sitting.

We first partnered with Auctor at the Seed, shortly after the team graduated from YC. At Sequoia, each year we select a handful of portfolio companies and take their founders to meet executives at some of the most important technology companies in Silicon Valley. For an hour, these leaders effectively join the team, helping us and the founders think through strategy, positioning, and go-to-market. In our very first meeting with the C-suite of a major enterprise platform, the executives asked for a pilot halfway through the conversation, before we’d even demoed the product. 

What gave us conviction beyond the market was the team. This is a category where speed of execution determines the winner given the first mover advantage. Will, Sky, and Matt are the hardest-charging, fastest-moving team we’ve met in this space. They shipped their platform and landed their first major enterprise contract within months of graduating YC. They’ve set up shop in New York and are pulling in talent the way only a team with this much momentum can.

We’re leading Auctor’s Series A and couldn’t be more excited to partner with Will and the team to bring software implementation into the age of AI.

Partnering with Scanner: Every Log Tells a Story—If You Can Find It Fast Enough

A while back, I was deep in research on the next generation of security infrastructure, talking to CISOs and security engineers at some of the most technically sophisticated companies in Silicon Valley. I asked them all the same question I’d asked a decade earlier when I worked in enterprise software: What’s your biggest headache? The consistency of their answers surprised me. “We drown in logs we can’t afford to keep,” as one security leader put it, “and go blind on the logs we can’t afford to search.”

Enterprise security today is a story of impossible choices. The tools that teams rely on generate enormous amounts of log data—every API call, every login event, every network connection. To investigate cyber threats, they need all of it, often going back a year or more. But storing everything in a SIEM like Splunk is prohibitively expensive; costs could easily consume 15% of a CISO’s entire budget. Instead, companies make a compromise: they keep only the most recent 10 to 30 days of logs in their SIEM and park the rest in Amazon S3, where storage is cheap, but the data is effectively frozen. When a breach, a compliance audit, or a forensic investigation happens, security teams discover too late that the evidence they need is out of reach, opaque and unsearchable. 

I first heard about Scanner from a member of the security team at Temporal, one of our portfolio companies, who called it, “crazy fast.” I looked into it, and reached out to Cliff Crosland right away.

What Cliff and his co-founder Steven Wu have built is elegant in its insight. They asked: what would a log search engine look like if you designed it from scratch for object storage? The answer was a purpose-built inverted index that maps field values directly to file regions in S3. Rather than combing through billions of rows, Scanner narrows each query to only the relevant slices of data. A petabyte of logs becomes interactive. Queries that took hours now run in seconds. And a streaming detection engine runs hundreds of detection rules continuously across tens of terabytes a day, without re-scanning the world for each one.

Cliff and Steven are exactly the kind of founders we look for. Both Stanford CS alums, they were engineering leads together at Accompany (acquired by Cisco), where they built core data infrastructure under demanding, production-scale conditions. They have an obsession with performance that borders on the philosophical; they don’t tolerate systems that feel slow. And they have the expertise to build something better.

What’s most striking about Scanner isn’t the technology—though that is genuinely impressive. It’s the customers. The companies using Scanner today read like a who’s who of the cloud native world: Notion, Ramp, Benchling, Confluent, Lemonade, BeyondTrust. And they’re not just using it—they love it. Benchling replaced another product after a forced tenfold price increase, and their head of security engineering called it one of the best technical decisions their team had made. Ramp started with security logs and then expanded to application logs, reducing their SIEM bill in the process. Notion’s detection and response team built an internal AI agent that autonomously runs security investigations using Scanner. 

That last example signals what’s to come. We are entering a new era of security operations, where AI agents will do much of the investigative work that today consumes hours of human time. But agents need to rapidly iterate, ask questions and follow threads; queries can’t take minutes, much less hours. Scanner’s speed is enabling these agentic security workflows across a wide range of companies: within weeks of their MCP release, nearly a third of Scanner’s customers were already using it in production, and agents now account for 80% of queries on the platform. That is not a prototype or a promising beta. That is the future arriving ahead of schedule.

Sequoia is proud to lead Scanner’s Series A, and we’re thrilled to partner with Cliff, Steven and their team as they work to transform a market overdue for reinvention. Scanner is winning hearts and minds among the most technically forward organizations today, and together, they will define the next decade of security infrastructure.