Edge AI Development
& Consulting Services Request a consultation

A strategic advisory service that evaluates your AI use case for edge viability, recommending optimal model architectures, hardware platforms, and compliance pathways. Our edge AI consulting helps organizations determine whether edge, cloud, or hybrid approaches best fit their latency, privacy, and cost requirements before committing engineering resources.

Key Service Deliverables:

1. Use Case Assessment & ROI Analysis
   We analyze your inference requirements, data volumes, latency constraints, and regulatory environment to build a business case for edge AI migration with projected cost savings and performance improvements.
2. Hardware Platform Selection
   Vendor-neutral guidance on selecting optimal edge hardware—from low-power MCUs (STM32, NXP) for battery devices to GPU-accelerated modules (NVIDIA Jetson) for computer vision—based on your performance, power, and cost parameters.
3. Model Architecture Recommendations
   Evaluation of model options (CNNs, transformers, decision trees) with benchmarks showing accuracy vs. inference time tradeoffs on target hardware, ensuring you choose architectures that meet accuracy requirements within device constraints.
4. Compliance Pathway Mapping
   For regulated industries, we map the certification requirements (FDA 510(k), CE marking, IEC 62443) and build compliance into the architecture from day one rather than retrofitting later.

A specialized machine learning service that compresses and optimizes AI models to run on microcontrollers and resource-constrained edge devices without sacrificing accuracy. Our TinyML expertise enables complex neural networks to execute on devices with kilobytes of RAM and milliwatts of power budget.

Key Service Deliverables:

1. Model Quantization & Pruning
   Systematic reduction of model size through INT8/INT4 quantization and structured pruning, achieving 4-10x compression while maintaining accuracy within acceptable thresholds for your application.
2. Architecture Search & Distillation
   Knowledge distillation from large teacher models to compact student networks optimized for edge inference, plus neural architecture search to find optimal topology for your hardware constraints.
3. Framework Optimization
   Conversion and optimization for edge inference frameworks (TensorFlow Lite, PyTorch Mobile, ONNX Runtime, OpenVINO) with hardware-specific optimizations for target platforms.
4. Benchmark Validation
   Rigorous testing of optimized models against accuracy requirements, latency targets, and power budgets on actual target hardware—not just simulations—ensuring production readiness.

A safety-critical firmware service using Rust and C++ to build secure, reliable edge AI runtimes. Our embedded expertise handles the unique challenges of running neural networks on bare-metal systems with deterministic timing, memory safety, and secure boot requirements.

Key Service Deliverables:

1. Rust-Based Secure Firmware
   Memory-safe firmware development using Rust to eliminate buffer overflows, use-after-free, and other vulnerabilities that plague C/C++ embedded systems—critical for FDA and IEC 62443 compliance.
2. Real-Time Inference Runtime
   Custom inference engines optimized for your hardware platform with deterministic execution timing, interrupt handling, and priority scheduling for safety-critical applications.
3. Secure Boot & OTA Updates
   Cryptographically signed firmware with secure boot chains and over-the-air update mechanisms featuring A/B partitioning, rollback protection, and differential updates to minimize bandwidth.
4. Power Management Optimization
   Firmware-level power optimization including sleep mode management, sensor duty cycling, and inference scheduling that maximizes battery life for portable and remote edge devices.

A full-stack hardware service from our in-house R&D lab that designs custom PCBs, enclosures, and sensor integration for edge AI devices. This capability differentiates Yalantis from software-only providers. We build the physical device, not just the code running on it.

Key Service Deliverables:

1. Custom PCB Design & Prototyping
   Schematic capture, PCB layout, and rapid prototyping for edge AI devices with optimal component placement for thermal management, signal integrity, and manufacturing efficiency.
2. Sensor Integration & Signal Conditioning
   Selection and integration of sensors (cameras, accelerometers, temperature, pressure) with appropriate analog front-ends, filtering, and digitization for AI model input requirements.
3. Industrial-Grade Enclosure Design
   Mechanical design for enclosures meeting environmental requirements (IP ratings, temperature ranges, vibration resistance) for industrial, medical, or outdoor deployment scenarios.
4. Manufacturing Support & BOM Optimization
   Design-for-manufacturing reviews, bill-of-materials optimization for cost and supply chain resilience, and production partner coordination for volume manufacturing.

A connectivity service that links edge AI devices to cloud platforms for fleet management, model updates, and analytics aggregation. Our edge AI iot expertise ensures devices operate autonomously while maintaining secure, efficient cloud synchronization when connected.

Key Service Deliverables:

1. Device Fleet Management
   Implementation of device provisioning, monitoring, and lifecycle management using AWS IoT Core, Azure IoT Hub, or custom MQTT infrastructure for global edge deployments.
2. Model Update Pipelines
   CI/CD pipelines for edge AI models enabling A/B testing of new model versions, staged rollouts across device fleets, and instant rollback if accuracy degrades in production.
3. Telemetry & Analytics Aggregation
   Streaming pipelines (Kafka, Kinesis) that collect inference results, model performance metrics, and device health data from edge fleets for centralized monitoring and retraining triggers.
4. Hybrid Inference Orchestration
   Intelligent workload distribution between edge and cloud based on model complexity, connectivity status, and latency requirements—ensuring optimal resource utilization across the compute continuum.

A specialized industrial service that deploys machine learning models directly on manufacturing equipment to detect failures before they occur. Our predictive maintenance solutions eliminate cloud latency for real time decision making, enabling immediate alerts and automated responses when ai algorithms identify anomalies in vibration, temperature, or operational patterns.

Key Service Deliverables:

1. Vibration Analysis Models
   Custom ai models trained on your equipment signatures to detect bearing wear, imbalance, misalignment, and other mechanical faults through continuous vibration monitoring with processing data locally on edge devices.
2. Thermal Anomaly Detection
   Computer vision and sensor fusion models identifying overheating components, electrical faults, and thermal degradation patterns before equipment damage occurs—all processed on-device without cloud dependency.
3. Remaining Useful Life Prediction
   Time-series models estimating component lifespan based on operational data, enabling maintenance scheduling that maximizes equipment uptime while minimizing unnecessary preventive replacements.
4. Integration with CMMS/ERP Systems
   Automated work order generation and spare parts alerts triggered by edge device predictions, with direct integration to SAP PM, Maximo, or your existing maintenance management platforms.