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Skymel NeuroSplit™

Build AI that adapts
to any device, anywhere.

NeuroSplit™ is an adaptive hybrid inference engine that intelligently slices and distributes AI models across devices and cloud at runtime. No more brittle, static AI pipelines.

60%
Cost Reduction
10x
Faster Response
5-10x
Larger Models

The Problem with Static AI Pipelines

Today's AI applications are built from static, hard-coded decisions that create brittle user experiences.

☁️

Hard-code to cloud GPU

Locked into high costs, latency, and privacy risks

📱

Hard-code to device

Limited model size, fails on weaker hardware

Static Pipeline (Current Approach)
Step 1
☁️ Cloud GPU
High Cost
Step 2
📱 Device
Limited Models
Step 3
☁️ Cloud GPU
High Latency
❌ Fragile chain of static decisions

NeuroSplit™ Adaptive Solution

Real-Time Analysis

Network Latency 45ms
GPU Availability High
Device Capability Mid-Range
Privacy Policy Local Preferred

Adaptive Execution

Model Part 1
📱 Local Device
65%
📊 Intermediate Result
Model Part 2
☁️ Cloud GPU
35%

Core Technology: Model Splitting

NeuroSplit™ can slice an AI model's neural network connections in real-time, creating two models from one where the output of the first feeds as input to the second.

🎯 The Optimal Split Problem

As networks grow, split possibilities grow exponentially. NeuroSplit's proprietary algorithm analyzes countless possibilities in real-time to find the optimal slice.

📊 The Unpredictable Device Problem

Consumer devices operate under constantly changing conditions. NeuroSplit uses ML to balance accuracy vs efficiency when measuring device state.

Neural Network Splitting
🖥️ Device Processing
Input Layer (784 neurons)
Hidden Layer 1 (512 neurons)
Hidden Layer 2 (256 neurons)
📊 Intermediate: 128-dim vector
☁️ Cloud Processing
Hidden Layer 3 (128 neurons)
Hidden Layer 4 (64 neurons)
Output Layer (10 classes)
✅ Final Result

Simple Integration

Add NeuroSplit™ to your existing AI models with minimal code changes. The SDK handles all adaptive decision-making automatically.

🚀

Drop-in Replacement

Works with PyTorch, TensorFlow, and ONNX models

Real-time Adaptation

Automatically optimizes for each user's device and network

🔒

Privacy-First

Keeps sensitive data on-device when possible

main.py
# Traditional approach (static)
import torch
model = torch.load('my_model.pth')
result = model(input_data)

# NeuroSplit approach (adaptive)
import neurosplit

model = torch.load('my_model.pth')
adaptive_model = neurosplit.enable(model)

# Now automatically adapts to device/network conditions
result = adaptive_model(input_data)

# Advanced: Custom splitting strategies
splitter = neurosplit.Splitter(
    privacy_level='high',  # Prefer local processing
    cost_optimization=True,  # Minimize cloud costs
    latency_target=100  # Target 100ms response
)

adaptive_model = splitter.wrap(model)
app.js
// Traditional approach (static)
const model = await tf.loadLayersModel('/model.json');
const result = model.predict(inputData);

// NeuroSplit approach (adaptive)
import { NeuroSplit } from '@skymel/neurosplit';

const model = await tf.loadLayersModel('/model.json');
const adaptiveModel = await NeuroSplit.enable(model);

// Automatically adapts execution strategy
const result = await adaptiveModel.predict(inputData);

// Configuration options
const splitter = new NeuroSplit({
  privacyMode: 'strict',
  costOptimization: true,
  deviceProfile: 'auto-detect'
});
ModelManager.swift
// Traditional approach (static)
let model = try MLModel(contentsOf: modelURL)
let prediction = try model.prediction(from: input)

// NeuroSplit approach (adaptive)
import NeuroSplitSDK

let model = try MLModel(contentsOf: modelURL)
let adaptiveModel = try NeuroSplit.enable(model)

// Adapts based on iOS device capabilities
let prediction = try adaptiveModel.predict(input)

// Advanced configuration
let config = NeuroSplitConfig(
    privacyLevel: .high,
    batteryOptimized: true,
    networkAware: true
)
let splitter = NeuroSplit(config: config)

Brain + Nervous System Architecture

🧠

Skymel OA (The Brain)

Strategic planner that understands user goals and designs the perfect AI strategy

• Analyzes user requirements
• Creates executable task graphs
• Plans optimal AI workflows
Task Graph
🕸️

NeuroSplit™ (The Nervous System)

Tactical execution engine that brings OA's strategic plan to life with real-time adaptation

• Distributes across device + cloud
• Adapts to live conditions
• Executes task graphs efficiently

Proven Performance Impact

💰
60%
Cost Reduction
Lower cloud compute costs by maximizing on-device processing
10x
Faster Response
Optimized execution paths and reduced network latency
🧠
5-10x
Larger Models
Deploy more capable AI while leveraging end-user devices
📊
50-100
Stub Models
Fit multiple models in space of single quantized model
🔒
Enhanced
Privacy
Process sensitive data locally whenever possible
🚀
Faster
Time-to-Market
Eliminate manual pipeline engineering and maintenance

Ready to Build Adaptive AI?

Join developers using NeuroSplit™ to build more capable, cost-effective AI applications.

Quick Integration
🔧 Developer-First
📚 Full Documentation