Integrit's On-Device AI Platform Capabilities
Integrit builds next-generation AI infrastructure for real-time robotics and autonomous agents through its proprietary SynaAI multimodal LLM platform and AirPath on-device AI boards.
Our architecture is designed for edge autonomy — enabling intelligent actions without cloud dependency, ensuring low-latency performance and operational reliability even in disconnected or extreme environments.
Core Technology Architecture for Hybrid On-Device AI
Build smaller, faster, and smarter VLMs—without sacrificing intelligence.
Integrit’s hybrid optimization framework combines quantization, distillation, and pruning to reduce memory and compute demands, while preserving over 95% of performance across tasks.
AirPath dynamically switches between multiple model sizes (7B / 3B / 1.5B) based on system context, using our proprietary NPU–GPU orchestration scheduler.
From TensorRT to Qualcomm DSP, our platform-native kernel modules ensure that Edge LLMs run fast, lean, and reliably—making real-time intelligent robotics possible today.
- SynaAI is Integrit’s lightweight multimodal LLM platform that enables contextual reasoning by fusing audio, video, and sensor inputs.
- AirPath is our embedded AI platform optimized for Qualcomm and NVIDIA SoCs, providing real-time inference, sensor fusion, and mission control.
- Our Hybrid AI Stack enables seamless orchestration across on-device, edge-cloud, and on-prem environments with integrated reinforcement learning and LLM-based logic.
Build your robot. Powered by intelligence.
Driven by the edge
We provide a sophisticated trade-off management mechanism that minimizes performance degradation while reducing the size of VLM models through a combination of quantization, distillation, and pruning.
AirPath supports dynamic model selection with scalable options (7B / 3B / 1.5B), orchestrated by a runtime scheduler that efficiently distributes workloads across NPU and GPU resources.
Kernel optimization tailored to AI board platforms—such as TensorRT, OpenCL, and Qualcomm DSP—is a core competence of Integrit. Through this capability, AirPath realizes real-world Edge LLM applications with proven efficiency and reliability.
Integrit On-device AI Architecture
Engineering AI & Enabling Edge
1. Application Layer
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User-facing AI agent interface and UX
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Vision-Language-Action (VLA) orchestration
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Autonomous mission planner with LLM-driven control
2. AI Reasoning & Runtime Layer
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SynaAI lightweight LLMs (0.5B–3B), optimized via quantization, pruning, and knowledge distillation
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Multimodal prompt parser
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Behavior reasoning engine (RL-enhanced)
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Session memory/context compression and management
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Trust validator with uncertainty estimation
3. Multimodal Sensing Layer
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Advanced STT using convolutional encoders and skip-connections
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Visual detection: objects, faces, gestures
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Environmental sensing via LiDAR, Radar, IMU
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Tactical comm parsing & sensor integration
4. On-Device Edge Platform Layer
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AI SoCs: Qualcomm QCS8550 / QCS9075, NVIDIA Orin NX
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Multi-core scheduling: GPU, HTP, NPU
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Embedded AI runtimes: TensorRT, OpenCL, DSP libraries
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Hardware kernel optimization
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Embedded OS (Linux, RTOS) with I/O and runtime drivers
AirPath® empowers your robotics development with cutting-edge AI intelligence—supporting open global middleware and accelerating your path to deployment.
AirPath®의 AI Stack
Engineering AI & Enabling Edge
Integrit provides a unified on-device AI platform that powers the cognition, decision-making, and responsive actions of next-generation robots—combining LLMs, vision and multimodal AI, Robot Foundation Models (RFM), and physical AI control engines. All modules run on our proprietary frameworks and middleware, supporting seamless integration with open systems like ROS2, ONNX, TensorRT, and Android, while also enabling enterprise-customizable behavior generation and multimodal action orchestration through a local, edge-based architecture.
Integrit’s proprietary lightweight framework and middleware provide:
Pipeline for AI Model Development & Optimization
VLA-Based Framework (SynaAI VLA)
AI Agent Framework & Behavior Controller
VLA-Based Autonomous Navigation Engine
Matter-Compatible Smart Home Connectivity Manager
ROS / ROS2 Wrapping & Porting Environment
SynaTree® — VLA-Powered Autonomous Navigation Platform
A next-generation autonomous driving platform that combines camera and multi-sensor fusion, optimized dataset processing, and VLA (Vision-Language-Action) reasoning to enhance path planning and driving safety in real-world environments.
VLA-based iSLAM, data fusion, lightweight mapping, robust safety navigation
Viscept® — Real-time Vision AI & Multimodal Perception
A real-time vision inference solution based on the VLA model, capable of detecting abnormal and hazardous behaviors, crowd density, security threats, and environmental risk factors. Supports smart spaces, surveillance, and safety operations.
Real-time vision, risk detection, multimodal analysis, VLA perception
Flyinglet® — Digital Twin & Remote Control Cloud Platform
기기의 실시간 제어와 원격 관제를 제공하는 임베디드와 클라우드 플랫폼, DBMS 와 분석, 위험관리를 제공하는 데이터 사이언스 시스템
VLA-based iSLAM, data fusion, lightweight mapping, robust safety navigation
ForestVoice™ — Hybrid On-Device & Edge LLM Platform
A hybrid AI platform that combines lightweight SLMs (3B/4B/8B) on-device with edge-based large LLMs (e.g., 70B). Manages multi-session dialogue flow, prompt injection control, and adaptive language generation for robotic assistants.
LLM hybrid, multimodal dialogue, SLM-Edge orchestration, prompt injection
Physical AI & Robot Foundation Model (RFM)
A robot-specific foundation model trained with real-world physical interaction data and behavioral patterns. Executes LLM-aligned robotic reasoning and motion planning, powered by SynaAI’s VLA and Reinforcement Learning-based training.
RFM, embodied AI, physical interaction, robot behavior generation
SynaAI™ — Lightweight Korean LLM & VLA Models
Integrit’s proprietary Korean-optimized LLM and VLA models, fully adapted for on-device inference. Built on compressed LLaMA architecture, SynaAI enables high-speed multilingual AI agent execution with low power consumption.
Lightweight LLM, Korean-optimized, on-device AI, multi-agent models
AirPath® is an open, on-device AI platform built to flexibly
integrate a wide spectrum of AI models—designed for real-world robotics.
On-Device VLA with SynaAI® Stack
SynaAI® Stack is Integrit’s patented solution designed to deliver full on-device Vision-Language-Action (VLA) reasoning and execution. It provides a highly optimized lightweight multimodal AI stack capable of perception, inference, decision-making, and behavior execution—all running directly on local edge hardware without relying on cloud connectivity.
The stack supports real-time multimodal AI inference optimized for Qualcomm® and NVIDIA® SoCs, enabling rapid service deployment, system-level validation, and efficient performance across power-constrained edge environments.
Core VLA Reasoning with SynaAI SLM + VLM
At the core of the SynaAI Stack lies a quantized 3B LLM (SynaAI SLM v2), tailored for on-device use. Paired with vision-language encoders (ViT, CLIP), speech recognition (STT), and a tokenizer, it forms the reasoning engine that transforms vision and audio contexts into actionable robotic behavior.
This combination enables low-latency context-aware inference, interpreting visual environments and conversational prompts to determine the robot’s next actions. It’s power-efficient, fast, and purpose-built for embedded robotics.
VLMM Framework & Behavior Execution Layer
The Vision-Language Multimodal Model (VLMM) Framework integrates with the SLM to bridge perception and action. It includes:
Action De-Tokenizer: Converts LLM-inferred outputs into executable control commands.
Status Manager: Monitors system and environment status to conditionally regulate actions.
The action pipeline is then handed over to the ROS2-based Robot Controller, which performs:
Waypoint Generation
Path Planning
Behavior Execution
This modular execution layer enables advanced robotic behaviors such as autonomous navigation, object interaction, and dynamic environment responses—all based on real-time vision-language understanding.
VLMN and RFM Integration for Multimodal Planning
The second processing pipeline includes VLMN (Vision-Language Model Navigation) and Robot Foundation Model (RFM) logic:
Vision is processed via CNN/ViT encoders.
Embedded with a LLaVA Adapter, the VLM aligns visual embeddings with language prompts.
The Multimodal Prompt Builder constructs structured prompts for SLM inference.
The output is a behavioral action plan, refined through a quantized on-device LLM and executed via a lightweight Behavior Controller—enabling multimodal robotic decision making, even in compute- or bandwidth-limited scenarios.
Fully Open & Extensible AI Platform
AirPath® offers a modular open AI runtime supporting third-party or proprietary:
LLMs (e.g., 3B–70B),
VLMs (e.g., vision encoders, custom models),
Voice/vision AI modules.
Developers can deploy their own models using a provided SDK and optimized APIs. The platform accommodates varying robot form factors, power envelopes, and application use cases—ranging from humanoid agents to mobile AMRs and AI companions.
All AirPath versions (V2–V9) come pre-integrated with:
STT, LLM, VLA runtime
Motion control libraries
Real-time kernel drivers
Multimodal orchestration engines
“AirPath® is an open on-device AI platform designed
to integrate diverse AI models and support scalable, multimodal robotic intelligence.”
Integrit’s SynaAI Stack brings together visual, language, and motion understanding in a cohesive runtime, optimized for edge deployment and ready for real-world autonomous robotics.
Key Features & Differentiators
Engineering AI & Enabling Edge
| Category | Details | Key Features |
|---|---|---|
| 1. LLM Model Compression | Quantization / Pruning / KD |
- Up to 75% memory savings (INT8) - Up to 70% reduction in computation - ≥95% MMLU accuracy retained - Supports 3B / 1.5B / 0.5B models |
| 2. Tactical Behavior Reasoning | RL + Controller Mapping |
- Real-time action prediction - Reinforcement learning in simulation - Controller output mapping |
| 3. Multimodal Sensor Fusion | Vision / Audio / Lidar / Radar / Comms |
- Real-time streaming fusion - Unified multimodal prompt encoder - Temporal sync ≤ 20ms |
| 4. STT & Audio Preprocessing | Conv Encoder-Decoder + Global Context |
- Latency ≤ 20ms - 40% computation reduction vs STFT - Long-context transformer modeling |
| 5. On-Device Runtime Optimization | Kernel Porting & SoC Tuning |
- ARM64 compatible - HTP / TensorRT acceleration - E2E latency ≤ 150ms / ≥30 tokens/s |
| 6. Session Memory Management | Long-context Memory System |
- 2hr session continuity - QA accuracy +20% vs non-session - Memory leak ≤ 0.5% |
| 7. Reliability & Safety | Uncertainty Estimation + Safety Rules |
- Action safety constraints - Risk-aware decision output - Trust module for hallucination control |
Real-time Edge Optimization
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Full pipeline latency under 150ms, first-token latency ≤ 300ms
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Streaming input with 30 tokens/sec throughput
Lightweight LLMs for Embedded AI
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Up to 75% memory savings (INT8), supporting models from 0.5B to 3B
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Optimization via quantization, pruning, and knowledge distillation
Tactical Behavior Reasoning
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Context-action mapping from multimodal sensor input
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Reinforcement-learning powered behavior generation
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Session memory compression & long-context Q&A
Built-in Reliability & Safety
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Uncertainty estimation and trust-based output validation
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Mission continuity without network dependence
With its fully integrated on-device AI stack—spanning lightweight LLMs, multimodal fusion, tactical reasoning, and real-time embedded execution—Integrit empowers the next generation of intelligent robotics to think, perceive, and act autonomously, even in disconnected and mission-critical environments.