The Seattle startup teaching the enterprise security queue to move at the speed of the products it's supposed to protect.
The double-C mark, drawn as one unbroken line - fitting for a company whose whole pitch is that security should be a continuous path, not a wall you slam into the day before launch.
Here is a thing that is true inside almost every large company and that almost nobody says out loud: there is a queue where good ideas go to wait. It is called the security review, and it is where a new product, a new vendor, or a new AI feature sits while a small team of experts figures out whether shipping it will get everyone sued.
The queue exists for excellent reasons. Regulated enterprises - banks, carmakers, telecoms - genuinely cannot ship whatever they want, whenever they want, because the downside of getting privacy or security wrong is measured in regulators, headlines, and very large numbers. So someone has to review things. The problem is not that review happens. The problem is how it happens: spreadsheets, tickets, and a lot of Slack messages that begin with "quick question." A reviewer spends most of their time not making hard judgment calls but gathering context - who owns this, what data does it touch, where does it live - before they can even start the part that requires a human brain.
Clearly AI, a Seattle company founded in 2024, looked at that arrangement and asked the obvious question that obvious questions are famous for: why is a highly paid security engineer doing the first eighty percent of this by hand? Its answer is a platform that automatically gathers the context, runs a baseline risk assessment, and then flags the specific places where a human actually needs to weigh in. The company says it cuts review time for Fortune 500 clients by as much as ninety percent. You should treat any "90% faster" claim the way you treat a restaurant that describes itself as "world famous" - with interest and mild suspicion - but the direction is not in dispute, and paying customers apparently agree.
That sentence is doing a lot of quiet work. The traditional relationship between a security team and everyone else is adversarial in the way a bouncer's relationship with a line is adversarial: the security team's job is to say no, and everyone else's job is to route around the security team. Clearly AI's bet is that if you make the secure path the easy path - fast, embedded in the tools people already use - then the incentive to route around it mostly evaporates. Nobody sneaks past the bouncer if the front door is quicker.
Clearly AI is run by a married couple, Emily and Joe Choi-Greene, who met at Amazon and share the specific kind of expertise you cannot fake: they spent years doing the exact job they are now trying to automate. That matters. Plenty of people build tools for problems they have read about. Fewer build tools for problems that used to ruin their afternoons.
Previously led Alexa AI Security at Amazon and data security at Moveworks. The natural-language and device-security background shows up in the product: a lot of Clearly AI's job is reading unstructured context and deciding what matters.
A former senior engineer at Amazon who led satellite telemetry for Project Kuiper and worked on security and large language models. Kuiper is roughly the opposite of a low-stakes system, which is a useful temperament for a security company.
Team size is listed as 16 in firmographic data; the company's own YC materials cite roughly 12. Either way, this is a small team selling to very large ones.
The unglamorous genius of enterprise software is finding a chore that everyone hates and nobody defends, and turning it into a workflow. Clearly AI has picked a cluster of them - threat models, privacy assessments, vendor reviews, AI risk checks - and put them behind a single AI-native platform that plugs into Jira, GitHub, and Confluence, so the review happens where engineers already work instead of in a separate tool they resent.
Automatically gathers context, scores risk, and flags where a human reviewer is genuinely needed - for products, features, vendors, and AI deployments.
Generates structured threat models before launch, surfacing the failure modes teams tend to find only in hindsight.
Reviews LLM and AI deployments for security and privacy exposure - the newest and fastest-growing category of "wait, is this safe?"
Turns the dreaded PIA spreadsheet into a repeatable, documented workflow built for audit readiness.
Evaluates third-party vendors and integrations without the endless questionnaire volley.
Prioritizes the backlog so scarce expert time lands on the highest-risk items first, not whatever is loudest.
The company reports up to a 90% reduction in review time for Fortune 500 clients. The bars above are illustrative, not audited - the point is the shape, not the pixel.
Founded in Seattle; joins Y Combinator's Summer 2024 batch.
Finalist in the Okta Startup Challenge and Amazon Bedrock Builders' Challenge; goes live with Fortune 500 customers.
Announces $8.4M seed round to scale product security.
Named a top-10 finalist in the RSAC 2026 Innovation Sandbox Contest.
A cybersecurity-heavy cap table - Crosspoint is a security specialist, and the angels skew toward people who have run big security and product orgs - is the venture equivalent of a referral. It signals that the buyers understand the pain.
Reported customers span manufacturing, automotive, finance, and technology - which is telling, because these are exactly the sectors where a security mistake is most expensive and where sixteen people would normally have zero chance of getting in the door. Enterprises this cautious do not adopt an AI-driven security tool for fun. They adopt it because the queue hurts.
Customer list compiled from public reporting and company materials; roster and counts are approximate.
The word doing the work there is "every." The old model scales badly because human reviewers are finite; the queue grows faster than the team. If you can automate the baseline, "review everything" stops being an aspiration a CISO writes in a strategy deck and becomes something like an actual default. That is the whole thesis. Whether the AI is good enough to be trusted with that default, at scale, in a regulated shop, is the question the next few years will answer - and it is a real question, not a rhetorical one.
Clearly AI is a Seattle-based, Y Combinator-backed startup that automates security and privacy reviews for regulated enterprises. Its AI-native platform gathers context, assesses risk, and flags where human review is actually needed - so security, privacy, and compliance teams can clear their review backlogs and ship products faster without sacrificing trust. Founded in 2024 by former Amazon security engineers Emily and Joe Choi-Greene, the company raised an $8.4M seed round in February 2026 and counts Fortune 500 brands like Ericsson, Rivian, and HID Global among its customers.
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