In sectors without a compliance mandate forcing the issue, AI governance isn't about passing audits. It's about protecting product margins. Cortega gives engineering and product leaders the control plane to stop runaway API spend, centralize shadow AI usage, and scale GenAI features profitably.
Generative AI features can quickly erode software gross margins. Calling a frontier model for every basic search, summary, or support ticket creates unpredictable monthly bills.
Product and engineering teams spin up separate accounts across providers. API keys end up hardcoded in microservices, with no visibility into total usage or spend.
Without a regulator forcing the issue, source code, financial projections, customer lists, and strategic roadmaps still end up in prompts sent to third-party models.
Building a customer-facing product on one model provider leaves you exposed to outages, rate-limit throttling, or sudden deprecations.
Route traffic across providers and models, with automatic failover if one experiences latency or an outage, so a single vendor problem doesn't become your outage.
Hard spend limits enforced at the gateway, by team, so a single feature or integration can't quietly run away with your cloud bill.
Cortega issues its own virtual keys, so provider API keys stop getting hardcoded into microservices and scattered across repos.
Local, rule-based detection for AWS keys, API tokens, secrets, private keys, and connection strings with embedded passwords, blocked before they leave your network.
Local, rule-based detection and redaction for PII and payment data (PCI), enforced before a request completes.
See what's actually being called, by whom, and at what cost, across commercial providers, open-source, and internal models, in one view.
Tell us what's driving the risk, whether that's API spend, shadow AI, credential sprawl, or provider reliability, and we'll show you exactly where Cortega fits.