The Intelligence Operating System.
A unified control plane for the entire AI lifecycle. Manage Agents, Training, and Inference from a single developer's laptop to a Hyperscale Accelerator cluster (NVIDIA, AMD, TPU, AWS Silicon).
Autonomy at Speed
Container Ready in Seconds
Traditional deployments take 10-15 minutes. Metis Prism utilizes intelligent container acceleration and P2P distribution to scale to 100+ nodes instantly. Your code is ready before you can switch windows.
Metis Foresight™
Predictive Intelligence
Our Active Learning kernel predicts workload cost, latency, and failure probability before a single Accelerator is provisioned. It builds a localized Roofline Model of your specific model architecture to identify bottlenecks (Memory Bandwidth vs Compute Bound) instantly.
Uncompromised Safety
Enterprise-Grade Governance
Innovation typically introduces risk. Metis Prism reverses that. With Aegis, you have built-in approval workflows, trust levels, and complete audit trails for every action your AI Workloads take.
Core Technologies
Foresight ML Engine
Predict the future before you pay for it.
Foresight is our predictive analytics kernel. It analyzes your workload's operational intensity and builds a custom 'Roofline Model' to forecast performance.
- •Pre-flight Cost Analysis: Know exactly what a training run will cost before you hit 'start'.
- •Optimization Recommendations: AI-powered suggestions to switch instance types or quantization levels.
- •Performance Forecasting: Estimate completion times with >95% accuracy.
# Predict costs before execution
from metis_prism import Client
client = Client()
cost = client.foresight.predict_cost(
model="claude-3-5-sonnet",
providers=["aws", "gcp"]
)
print(cost.recommendation)
Aegis Safety Layer
Control what your AI can do.
Aegis sits between your AI models and the world. It intercepts every tool call, API request, and database query to enforce your organization's compliance policies.
- •Smart Routing: Routing simple queries to smaller models and complex ones to frontier models.
- •Speculative Decoding: accelerating generation by predicting future tokens.
- •Request Batching: Dynamic micro-batching to maximize Accelerator saturation without spiking latency.
# Define safety policies
client.aegis.update_config(
mode="REQUIRE_APPROVAL",
budget=50.00,
scope="finance_agent"
)
Hybrid Runtime
Run anywhere. Manage everywhere.
Metis Prism decouples the Control Plane from the Data Plane. You manage your policy and observability in one place, but the compute happens where it makes the most sense.
- •Local Edge: Run latency-sensitive models on local hardware (Apple Silicon/NVIDIA) with zero cost and complete privacy.
- •On-Premises: Keep data within your physical perimeter for GDPR/HIPAA compliance.
- •Cloud Bursting: Seamlessly spill over to AWS/GCP/Azure when you need infinite scale.
# Auto-route to optimal target
agent = client.agents.create(
name="swarm_node",
model="auto", # Adapts to complexity
tools=["web_search"]
)