In development
AI Model Manager: governance for the AI you're actually running
You probably can't name every AI model running in your environment right now. Different teams use different providers, different versions, different on-device models in different apps. AI Model Manager is the inventory, performance, policy, and audit layer that pulls it all together.
Capabilities
Six surfaces for running AI responsibly at scale.
Know every model running
A single inventory of every AI model in your environment — cloud APIs, self-hosted open models, on-device models shipped in your apps. Provider, version, deployment target, last updated, who approved it.
Version control + rollback
Treat model versions like code. Promote new versions through staging, watch performance, roll back instantly if regressions show up. No more 'we updated the model and nobody knows what changed.'
Performance benchmarks per model
Continuous accuracy, latency, cost, and safety benchmarks per model version. Compare candidates side-by-side. Catch silent regressions before users hit them.
Policy enforcement
Define what each model is allowed to do — which prompts, which data classifications, which user roles. Block calls that violate policy before they reach the provider.
Audit trail for compliance
Every inference logged: model, version, prompt hash, response hash, user, timestamp. The audit log your SOC 2 / HIPAA / ISO 27001 auditors will actually ask for.
Cloud + on-device, one console
Manage Anthropic, OpenAI, xAI, Google, your own fine-tunes, and on-device Core ML or TFLite models from the same interface. No tool sprawl per provider.
How Teams Use It
Multi-provider AI ops
Your team uses Claude for reasoning, GPT for code, Grok for live search, and a local Llama fine-tune for sensitive workloads. AI Model Manager gives one pane of glass for all of them.
Model rollout governance
Compliance team needs to approve every model going to production. Set up a 'requires approval' policy on the production environment; staging is open. Promote with one click and a recorded justification.
Drift + regression detection
A model update silently degrades classification accuracy. The benchmark suite catches it within hours. Auto-rollback to the previous version while engineering investigates.