Use Cases
How teams use Igris Inertial for AI workloads across cloud and edge environments.
Use Cases
How teams use Igris for AI workloads across cloud and edge.
Use Cases
How teams use Igris for AI workloads across cloud and edge.
Enterprise AI Operations
Multi-tenant cost control, policy-driven routing, and trust-aware provider selection for teams managing AI at scale.
Hybrid Cloud–Edge Reliability
Cryptographically bound decision-execution with automatic failover. Keep applications online when cloud providers fail.
Edge-First AI Systems
Deterministic execution and local model inference for robotics and autonomous systems with poor connectivity.
Air-Gapped & Restricted Environments
Isolated operation with encrypted storage and no external telemetry, designed for secure facilities.
AI Reliability Engineering
Decision traces, provider verification, and replayable execution paths for production AI systems.
The Challenge
Multi-tenant teams struggle with AI provider costs, quality consistency, and governance. Different departments use different providers, making budget control difficult. Provider behavior varies, and there is limited visibility into actual performance versus reported metrics.
How Igris Helps
Overture provides multi-tenant isolation with policy-driven routing decisions informed by observed performance. Teams get isolated access with configurable spending limits and real-time cost tracking. Trust-aware selection blocks providers below quality thresholds.
Key Features
- •Multi-tenant isolation
- •Policy-driven routing
- •Real-time cost tracking
- •Trust-aware selection
- •Budget enforcement
Enterprise AI Operations
The Challenge
Applications need both cloud performance and edge continuity. Cloud providers fail occasionally, network connectivity is unreliable, and downtime impacts critical operations. Manual failover requires code changes and operator intervention.
How Igris Helps
Overture makes routing decisions in the cloud while Runtime executes on edge devices. Hybrid cryptographically binds decisions to execution, preventing policy bypass. When connectivity fails, Runtime continues with local models using the last approved routing policy.
Key Features
- •Cryptographic binding
- •Automatic failover
- •Policy continuity
- •Edge execution
- •Unified API
Hybrid Cloud–Edge Reliability
The Challenge
Robotics, autonomous vehicles, and field equipment need AI inference in environments with poor or no connectivity. Cloud APIs fail in remote areas, underground facilities, or during outages. Systems must maintain operation with deterministic behavior.
How Igris Helps
Runtime executes AI workloads directly on edge devices with deterministic execution envelopes and resource limits. Local models provide fallback when connectivity is unavailable. Peer-aware execution hooks enable coordination between nearby devices.
Key Features
- •Deterministic execution
- •Local model inference
- •Offline operation
- •Resource safety limits
- •Peer coordination
Edge-First AI Systems
The Challenge
Secure facilities, classified networks, and regulated environments cannot send data to external services. AI workloads must run within isolated boundaries with no external communication. Models and data require encrypted storage.
How Igris Helps
Runtime operates independently after provisioning, with no external telemetry by default. Encrypted storage protects models and execution data. Works within isolated networks using local coordination after initial setup.
Key Features
- •No external telemetry
- •Encrypted storage
- •Isolated operation
- •Local coordination
- •Provisioned deployment
Air-Gapped & Restricted Environments
The Challenge
Teams need to evaluate AI providers, compare performance, and prototype workflows without production constraints. Switching between providers requires code changes. Cost tracking and performance comparison are manual processes.
How Igris Helps
OpenAI-compatible API enables existing code to work unchanged. Configuration-driven provider switching and shadow testing compare strategies side-by-side. Cost tracking shows actual spend by provider and model.
Key Features
- •OpenAI-compatible API
- •Configuration switching
- •Shadow mode testing
- •Cost tracking
- •Performance comparison
Research & Evaluation
The Challenge
Production AI systems require visibility, auditability, and reproducibility. Teams need to understand routing decisions, verify provider behavior, and replay execution paths for debugging and compliance.
How Igris Helps
Overture provides decision traces and observed vs reported provider metrics. Hybrid enables policy versioning and replayable execution paths. Every routing decision includes full reasoning and applied constraints.
Key Features
- •Decision traces
- •Provider behavior verification
- •Policy versioning
- •Replayable paths
- •Audit logging