One API · AI on Cloud and Edge · Zero Migration Required

Unified AI Inference Across
Cloud, Edge, and Devices

Unified AI Inference Across Cloud, Edge, and Devices

Unified AI Inference Across
Cloud, Edge, and Devices

Optimized for Performance, Cost, and Compliance

Reduce AI inference spend by 30–70% and lower latency instantly

with a drop-in API compatible with OpenAI

Book a Demo

10-minute technical walkthrough.

See how inference routing works in production.

Trusted by teams building AI at global scale

Inference Is Becoming the Bottleneck

As AI moves into production, inference cost, latency, and compliance become critical constraints

One Unified API Across Cloud, Edge, and Devices

Manage AI inference across cloud, edge, and device environments through a single API layer — without changing application logic or deployment workflows.

One Unified API Across Cloud, Edge, and Devices

Manage AI inference across cloud, edge, and device environments through a single API layer — without changing application logic or deployment workflows.

One Unified API Across Cloud, Edge, and Devices

Manage AI inference across cloud, edge, and device environments through a single API layer — without changing application logic or deployment workflows.

Built for Production Reliability

Automatically route and fall back across models, providers, and regions to keep inference available even during outages or traffic spikes.

Built for Production Reliability

Automatically route and fall back across models, providers, and regions to keep inference available even during outages or traffic spikes.

Built for Production Reliability

Automatically route and fall back across models, providers, and regions to keep inference available even during outages or traffic spikes.

Optimized Performance and Efficiency

Dynamically route requests to the most efficient execution path to reduce latency and maximize throughput per dollar.

Optimized Performance and Efficiency

Dynamically route requests to the most efficient execution path to reduce latency and maximize throughput per dollar.

Optimized Performance and Efficiency

Dynamically route requests to the most efficient execution path to reduce latency and maximize throughput per dollar.

Compliance by Design

Enforce geo-fenced inference execution and maintain tamper-evident audit logs to meet enterprise and regulatory requirements.

Compliance by Design

Enforce geo-fenced inference execution and maintain tamper-evident audit logs to meet enterprise and regulatory requirements.

Compliance by Design

Enforce geo-fenced inference execution and maintain tamper-evident audit logs to meet enterprise and regulatory requirements.

What We Offer

A Unified Inference Stack
Without the Headaches

We sit between your apps and AI models, providing a unified inference stack that handles routing, performance, reliability, and compliance by design

Intelligent Inference Routing

• Route inference requests based on cost, latency, region, or reliability

• Automatically fall back across models and providers when conditions change

Routing live requests…

Cost Routing

Latency Routing

Region Routing

Reliability

Auto Fallback

Routing live requests…

Cost Routing

Latency Routing

Region Routing

Reliability

Auto Fallback

Routing live requests…

Cost Routing

Latency Routing

Region Routing

Reliability

Auto Fallback

→ API-level integration. No application logic changes.

Performance & Cost Optimization

• Offload non-critical requests to smaller, faster models to reduce latency and spend

• Cache high-frequency queries to improve throughput without retraining models

class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"

class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"

class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"

→ No retraining. No model rewrites.

Edge-Ready Architecture

• Start inference in the cloud and extend execution closer to users or devices when needed

• Roll out edge inference incrementally without re-architecting your system

Cloud

Edge

Cloud

Edge

Cloud

Edge

→ Incremental rollout. No migration.

Compliance Controls

• Enforce geo-fenced inference execution to meet data residency requirements

• Generate tamper-evident audit logs for enterprise and regulatory review

Geo-Fence Policy

Region restriction enforced

Audit Log Seal

Tamper-evident record created

Residency Lock

Inference runs in approved zones

Geo-Fence Policy

Region restriction enforced

Audit Log Seal

Tamper-evident record created

Residency Lock

Inference runs in approved zones

Geo-Fence Policy

Region restriction enforced

Audit Log Seal

Tamper-evident record created

Residency Lock

Inference runs in approved zones

→ Control where inference runs and how it is recorded.

By the Numbers

Measured Results

Based on internal benchmarks and early design-partner evaluations.

30–70%

Lower Inference Cost

by routing non-critical requests and optimizing execution paths

40–60%

Lower p95 Latency

with region-aware and edge-based inference routing

2–3×

Higher Throughput per Dollar

compared to centralized cloud-only inference

100%

Enforced Data Residency

inference execution can be restricted to specific regions (e.g., EU-only)

100%

Verifiable Audit Trails

every inference request is recorded with tamper-evident traceability for compliance review

Common Use Cases

Why Teams Choose Us

Cloud Al

Devices & Physical Al

Enterprise & Regulated

AI Agents & Copilots

AI Agents & Copilots Customer Support & RAG Systems

Route simple tasks to efficient models
Cache repeated prompts
Keep UX fast while controlling spend

Customer Support & RAG Systems

Most customer queries are repetitive, but every request still hits expensive LLMs.

Compress and cache common queries
Route by intent and complexity
Reduce LLM spend without degrading accuracy

Physical AI & Robotics

Real-time systems cannot rely on round-trip cloud inference.

Execute inference at the edge for control loops
Maintain fallback paths for safety-critical scenarios
Support offline or degraded-network operation

Smart Devices & Wearables

Cloud inference erodes device margins and introduces user-visible latency at scale.

Run common inference tasks closer to or on devices (wearables, cameras, sensors)
Escalate only complex cases to the cloud, with data locality enforced by default

Regulated & Multi-Region AI Deployments

AI adoption is blocked by data residency, auditability, and compliance requirements.

Enforce geo-fenced inference execution
Maintain tamper-evident audit trails
Enable AI usage without compliance risk

Cloud Al

Devices & Physical Al

Enterprise & Regulated

AI Agents & Copilots

AI Agents & Copilots Customer Support & RAG Systems

Route simple tasks to efficient models
Cache repeated prompts
Keep UX fast while controlling spend

Customer Support & RAG Systems

Most customer queries are repetitive, but every request still hits expensive LLMs.

Compress and cache common queries
Route by intent and complexity
Reduce LLM spend without degrading accuracy

Physical AI & Robotics

Real-time systems cannot rely on round-trip cloud inference.

Execute inference at the edge for control loops
Maintain fallback paths for safety-critical scenarios
Support offline or degraded-network operation

Smart Devices & Wearables

Cloud inference erodes device margins and introduces user-visible latency at scale.

Run common inference tasks closer to or on devices (wearables, cameras, sensors)
Escalate only complex cases to the cloud, with data locality enforced by default

Regulated & Multi-Region AI Deployments

AI adoption is blocked by data residency, auditability, and compliance requirements.

Enforce geo-fenced inference execution
Maintain tamper-evident audit trails
Enable AI usage without compliance risk

Cloud Al

Devices & Physical Al

Enterprise & Regulated

AI Agents & Copilots

AI Agents & Copilots Customer Support & RAG Systems

Route simple tasks to efficient models
Cache repeated prompts
Keep UX fast while controlling spend

Customer Support & RAG Systems

Most customer queries are repetitive, but every request still hits expensive LLMs.

Compress and cache common queries
Route by intent and complexity
Reduce LLM spend without degrading accuracy

Physical AI & Robotics

Real-time systems cannot rely on round-trip cloud inference.

Execute inference at the edge for control loops
Maintain fallback paths for safety-critical scenarios
Support offline or degraded-network operation

Smart Devices & Wearables

Cloud inference erodes device margins and introduces user-visible latency at scale.

Run common inference tasks closer to or on devices (wearables, cameras, sensors)
Escalate only complex cases to the cloud, with data locality enforced by default

Regulated & Multi-Region AI Deployments

AI adoption is blocked by data residency, auditability, and compliance requirements.

Enforce geo-fenced inference execution
Maintain tamper-evident audit trails
Enable AI usage without compliance risk

See It in Action

Same API. Same output. Optimized result.

Book a 10-Minute Technical Walkthrough

No commitment. See how inference is routed, optimized, and audited across cloud and edge — without changing your existing stack.

Schedule a Walkthrough

Unified AI Inference Across Cloud, Edge, and Devices

Unified AI Inference Across Cloud, Edge, and Devices

Unified AI Inference Across Cloud, Edge, and Devices

Optimized for Performance, Cost, and Compliance

Optimized for Performance, Cost, and Compliance

Optimized for Performance, Cost, and Compliance

Inference Is Becoming the Bottleneck

A Unified Inference StackWithout the Headaches

Measured Results

30–70%

30–70%

30–70%

40–60%

40–60%

40–60%

2–3×

2–3×

2–3×

100%

100%

100%

100%

100%

100%

Why Teams Choose Us

See It in Action

Book a 10-Minute Technical Walkthrough

Unified AI Inference Across
Cloud, Edge, and Devices

Unified AI Inference Across
Cloud, Edge, and Devices

A Unified Inference Stack
Without the Headaches