The Operating System for
AI Intelligence.
Don't just route. Predict. Optimize. Scale. Metis Prism brings Foresight Intelligence to every layer of your AI stackโfrom autonomous agents to silicon kernels.
The Optimization Gap
Enterprises are bleeding money on unoptimized GPU spend. Generic routing and default kernels leave 50% of your performance on the table.
Metis Prism lowers AI TCO by 60% by dynamically routing to the cheapest model that meets your quality threshold and optimizing the silicon it runs on.
*Benchmark: Llama-3-70B Training on H100 SXM5
The Control Gap
Deploying autonomous agents without guardrails is dangerous. You risk PII leakage, hallucinated promises, and unbounded cloud spend.
Traditional gateways are too slow (adding latency) or too rigid (regex constraints). Efficiency shouldn't cost you safety.
Unmanaged
Runaway costs, PII exposure, Zero visibility
Aegis Managed
Budget caps, PII scrubbing, Policy enforcement
Unified Optimization
Metis Prism handles the operational complexity so you can focus on your workload.
Foresight
Predictive intelligence that forecasts cost and performance before execution. Stop flying blind.
Aegis Governance
Policy-driven guardrails ensuring compliance, security, and budget adherence without slowing down innovation.
Unified Runtime
One optimized engine for Agents, Training, and Inference. Stop managing fragmented stacks.
Universal Intelligence Infrastructure
Unified Control Plane
Manage: AWS, GCP, Azure, and Edge from a single glass pane.
Predict: Costs and latency before execution.
Your data never leaves your environment.
Universal Runtime
Train on any accelerator, use any LLM providers and run agents on any cloud. We handle the hardware abstraction and optimizations.
Supports BYOC, Metis Prism Managed Cloud, Local Edge, and On-Premises.
from metis_prism import Client
# Initialize client (uses METIS_PRISM_API_KEY)
client = Client()
# Define Multi-Cloud Agent with Auto-Routing
agent = client.agents.create(
name="Production_Swarm",
model="auto",
tools=["search", "code"]
)
>>> Deploying to AWS, GCP & Local Edge...
From Idea to Production
The lifecycle of a Metis Prism workload. Predict, Experiment, and Promote with confidence.
Draft & Predict
Define your workload (Agent, Training, or RAG). Before you deploy, Foresight analyzes your config against 50+ hardware targets to predict cost, latency, and feasibility.
# 1. Create Workload & Analyze Feasibility
spec = {
"name": "agent_v1", "type": "agent",
"config": {"model": "llama3-70b"}
}
ย
estimates = client.foresight.compare_clouds(
workload=spec,
providers=["aws", "edge"]
)
>>> Recommendation: 'Edge-NPU' saves 94% cost
ย
workload = client.workloads.create(**spec)
Experiment & Validate
Don't guess. Run controlled experiments across different models, prompts, and infrastructure targets (Cloud vs Edge) simultaneously. Visualize win-rates and latency distributions.
# 2. Create Experiment from Workload
experiment = client.experiments.create(
name="agent_ab_test",
workload_id=workload["id"],
config={"variants": ["base", "tuned"]}
)
ย
# Execute parallel runs
results = client.experiments.run(experiment["id"])
Promote & Govern
Promote the winner to production with a single click. Aegis automatically attaches governance policies, budget caps, and compliance guardrails to the live workload.
# 3. Promote winner to Production
deployment = client.experiments.deploy(
experiment_id=experiment["id"],
variant_ids=["tuned"],
target="production"
)
ย
>>> Aegis Governance Policy attached (Auto)
>>> Deployed to 500 nodes (Global)
The Intelligence OS
A unified operating system for the entire AI lifecycle. From creating autonomous agents to training SOTA models and deploying inference at scale.
Agent OS
Foresight Agent Intelligence
The Intelligence Kernel. Forecasts response latency, token cost, and tool success rates before execution.
Aegis Governance
Runtime guardrails. Enforce budget caps, PII scrubbing, and policy compliance with low latency sidecars.
Hippocampus Memory
Infinite context. Hybrid short-term vector retrieval and long-term graph reasoning for persistent agent state.
Council Mode (Consensus)
Multi-Model Verification. Reduces errors with confidence scoring and automatic human escalation.
Evaluation Platform
Agentic Simulation
Red Team your autonomous agents in adversarial environments before deploying to production.
LLM-as-a-Judge
Foresight-powered evaluation suites for scalable, consistent grading of complex RAG and agent interactions.
Predictive Quality Scoring
Intercept degraded responses before they reach the user using live semantic similarity checks.
Training OS
Kernel & Compiler Acceleration
Foresight orchestrates JIT compilation and kernel fusion across CUDA, Triton, and ROCm for maximum throughput.
FgAD Model Passports
Capture training telemetry (hardware affinity, precision) to automatically optimize inference deployment.
Cost & Performance
Stop guessing. Predict training costs and throughput (MFU) with Research-Backed accuracy.
Experimentation
A/B test different configurations. Visualize win-rates and latency distributions for fine-tuning.
Inference OS
High-Density Serving
Sub-10ฮผs Rust Sidecars and dynamic FgAD Passport integration deliver 4x throughput gains.
RAG Context Optimization
Adversarial Context Pruning and Privacy-Preserving Compressed RAG eliminate the quadratic cost of attention.
Scaling Advisor
Dynamic Scaling Intelligence. Advice on when to scale up/down or switch to spot instances.