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.