Why we're building Osyra — the keystone of enterprise AI

Every Fortune 1000 company we have spoken to in the last six months has the same shape of AI problem. They are not asking which model to use — they are asking how to govern thirty different teams, all using a different model, all paying different vendors, with no audit trail, no policy enforcement, and no way to predict next month's bill.

That is the gap Osyra closes. We are the control plane for enterprise AI — the keystone that sits between every team and every provider, enforcing the rules the security team wrote, optimizing the costs the finance team wants, and producing the audit log every regulator now demands.

The shape of the problem

The pattern repeats across every customer conversation. A platform team buys an Anthropic seat for the legal department. Three months later, marketing buys a separate OpenAI seat. By the end of the year there are eight different LLM vendors with billing relationships across the company, no shared identity, no shared policy, no shared observability. The CISO does not know which prompts crossed which compliance boundary. The CFO does not know which workload is responsible for the $80K month-over-month increase. The CTO is left to reconcile twenty dashboards that all say different things.

Vertical AI vendors do not solve this. A point solution that does customer support automation does not unify your billing. A vertical that does code review does not enforce your data-residency rules. The control plane has to live above the providers, not next to them.

What Osyra is

Osyra is one wire to one gateway. Every team's request — Claude, GPT-4, Bedrock, a fine-tuned in-house model — goes through that gateway. Every request is authenticated, authorized against a workspace policy, redacted of PII at the boundary, routed to the cheapest provider that satisfies the capability and compliance constraints, and recorded against the workspace audit log.

The architecture is intentionally horizontal. We do not pick winners between providers — we route to whoever is cheapest for the task at hand, which is where the cost savings come from, especially on simple, high-volume workloads. (We do not quote a blanket savings percentage — the real number depends on your traffic mix and is best measured against your own workloads.) We do not lock you into a single inference vendor — your policies and your audit log are portable. The product surface — the dashboard, the SDKs, the playground — is the part you adopt; the underlying providers stay swappable.

The keystone, not the cathedral

We do not build models. We do not host inference. We sit between the people running enterprise AI and the dozens of providers they depend on, and we make that boundary governable. The vision is simple: every Fortune 1000 should be able to answer four questions in the same dashboard, in real time —

1. Who used what model on what data this quarter?
2. What did it cost, and could we have spent less?
3. What policy rules fired, and did anything cross a compliance line?
4. If we had to swap a provider tomorrow, how long would it take?

Today none of those questions has a clean answer at the customers we talk to. By the end of 2026 they should be a tab in the Osyra console.

What is next

The platform shipped its V1 in April. Over the next two quarters we are wiring the audit log to immutable storage, finishing the policy editor, opening the Osyra Labs publication track for our research group, and onboarding the first cohort of design partners. If you are a security or platform leader at a large enterprise running AI workloads at scale, request a demo — we want to hear what your control-plane gap looks like.

The keystone goes in the arch last. Most of the engineering inside Osyra is unglamorous infrastructure that nobody notices when it works. That is fine — the goal is to make the AI estate of an enterprise feel like the AWS estate did in 2014. One pane of glass, one identity, one audit log, one bill. Everything else is downstream of getting that right.