The Foundation for Enterprise AI Is Here

Today we're introducing Actualyze AI, along with our $7M seed round from Storm Ventures, Canaan Partners, Morado Ventures, and AME Cloud Ventures.

Actualyze is the foundation for enterprise AI: a platform that sits between your people, applications, and agents on one side and every model they use on the other, so an organization can govern, secure, operate, and optimize all of its AI in one place. Requests pass through the platform and pick up identity, policy, budget enforcement, and an audit trail on the way to the provider. Adopting it is a simple change to an application. We've spent the past year building it. Conversations across the industry shaped it, and so did our own view of where enterprise AI is heading. The next step is refining it with the organizations that will run it, through our Design Partner Program. You can request access now.

#How we got here

Our founding team has spent careers building and running large-scale production infrastructure. We assumed those playbooks would mostly transfer to AI. A year of conversations with platform teams, security leaders, finance organizations, and industry veterans across large enterprises convinced us otherwise. The people in those rooms were as capable as any we've worked with. The problems were just new, and everyone was hitting the same ones.

Everywhere we went, the pattern was the same. AI is becoming a layer of the stack, and it's being adopted like a minor dependency. Within a few years, "does this application use AI" stops being a question. Customer products, internal tools, workflow automation, agents: all of it calls a model, the way all of it uses compute, storage, and networking. But the wiring is a key in an environment variable, an SDK call, an invoice at the end of the month.

A model breaks every assumption behind that pattern. It bills on behavior; a retry loop can multiply spend overnight. It performs best when you hand it your most sensitive context. It's live from laptop to CI to production. And the market underneath it reprices and deprecates fast enough to change your product between your own releases.

#Access is whoever holds a key

The only permission system most organizations have for models today is possession of an API key. The keys themselves get handled with normal discipline: vaulted and scoped per project. That solves storage, not authority. A project-scoped key can tell you which application called. It carries nothing about the person or team behind the call, and it can't express the authority model: which team may use the frontier model, which providers are approved at all. The identity layer you run everywhere else ends at the provider's front door.

We spent the year asking companies which of their applications call which models. There was always an answer, and it was always hard-won. Teams pieced it together from processes and tooling built for other layers of the stack, and it was stale almost as soon as it was assembled. That's not a discipline problem. The surface is genuinely new and it grows on its own. Specialized models are absorbing work inside features that used to be complex algorithms, and each new use adds another model, sometimes another provider or key.

#Every prompt is a disclosure decision

A model performs best on exactly the data you least want leaving the building: customer records, contracts, source code, support history. So that's what gets sent, implicitly, by whoever wrote the calling code. There's no inspection point where sensitive content gets caught and masked on the way out, and no record of what went to which provider under which terms.

And "the provider" isn't one decision. Every provider comes with its own data handling, retention, training terms, and jurisdiction. Do you trust them? Do you trust them all the same? No organization would answer yes, yet that's the position the architecture takes on their behalf, because sensitive context flows to whichever provider each application happened to wire in.

Ask what customer data reached model providers last week. Application logs and provider dashboards each hold a piece, but one consistent account of what left, where it went, and on whose authority is a reconstruction project. The security questionnaire and the regulator both ask that question expecting a record.

#Cost is behavior, and the behavior has no owner

A model bills per token. Spend tracks how the application behaves. A verbose system prompt, a retry loop, an agent re-reading its full context on every step, a feature that simply takes off: any of these moves the bill, and what the provider reports back is usage by account, project, or key. Mapping that back to teams and features happens after the fact, and after the fact can't stop anything.

That grain is the whole problem.

Provider limits stop where provider reporting stops, at the account or the project, while the budgets an organization actually needs sit with teams and features, and nothing in the path can enforce one at that level. Every AI feature now carries a real cost of goods.

#The market moves faster than organizations can decide

New models leapfrog the ones your team standardized on, and last quarter's expensive option is often this quarter's cheap one. Providers deprecate versions on a few months' notice, and a deprecation can quietly change how your product behaves.

The code change was never the expensive part; the model name has lived in config since the first sprint. The expensive part is the decision. A model isn't a library. Swap it and your product behaves differently. So every change means evaluating quality, revisiting cost, clearing security and compliance, and then landing it consistently across every team that ships AI. Without a shared place for those decisions, the work repeats team by team, and what one team learns is hard to carry to the next. Re-deciding costs enough that it rarely happens, and the portfolio drifts from where cost and quality have moved.

#What we built

An organization should be able to say who can use what, on whose budget, with what data. In most organizations today, nothing sits between applications and providers where those answers could live. Every app points directly at its provider, and they fall through. Some platform teams have built a version of that layer themselves, usually around a gateway, and gotten real value from it. But a gateway moves traffic, and these questions are organizational, so the build grows into a distributed-systems commitment that never ends. The providers, the models, the pricing, and the attack surface keep moving. Every engineering hour it absorbs is an hour not spent on the AI that differentiates the actual business. We think it belongs in the stack once, as a platform that enforces one identity and policy model in the path of every call. And it can't belong to any provider, because a provider's controls end at its own edge. Policy has to be set once and hold across every provider and model an organization uses, including the ones it hasn't adopted yet. That's the foundation the model layer is missing.

Before a request reaches a provider, Actualyze knows which person, team, and application is calling, and applies access policy. It inspects the request and masks sensitive content before anything leaves, and keeps a record of what went where. It charges the call to exactly one budget, and an exhausted budget stops the next call. And the decisions about models themselves get one home: which are sanctioned, who may use them, when one retires. The organization decides once and the decision holds everywhere. Keeping up with the market becomes routine operations.

Two things had to be true for any of this to work. Provider keys had to move out of application code and into the platform, so the governed path is the only path. And it had to hold up in your critical path. We've run infrastructure with that job description before.

#Where this goes

Agents raise the stakes on all of it. A single agent task fans out into a chain of autonomous model calls, with retries and tool loops the author never fully predicted. Every ungoverned key and uninspected prompt multiplies with them, and so does the spend nobody can attribute.

We've also seen how this ends, because every layer of the stack has forced the same fix. Databases got secrets management and access governance. The cloud got IAM and an entire industry of cost and posture tooling, built after adoption outran control. The model layer is at that early stage right now, moving faster than the cloud ever did.

The part that surprises people is that control speeds adoption up. The answer to "can this team use this model" becomes yes, with limits attached and enforced. That's what the foundation is for: adopting AI, delivering it, and scaling it. Control is what makes it safe for an organization to say yes.

#Three questions

Who in your organization can call which model today? What data reached model providers last week? What did any single feature cost you to serve this month?

If those can't be answered, the problem is already running in production.

If your organization is living any part of this, we'd like to talk: request access to the Design Partner Program, or reach out directly.

#Where to find us

The funding details are in the press release.

We're at Ai4 in Las Vegas August 4–6, 2026. If you're there, come talk to us.