AI Integration
Application-specific AI and system-on-chip (SoC) solutions that enhance data fusion and integration, enable real-time decision-making, and deliver targeted, actionable data — from on-device edge inference to cloud-scale AI services.
Request a QuoteApplication-Specific AI from Edge to Cloud
Novus Labs designs, develops, and tests AI solutions tailored to the constraints of real products. We pair AI engineering with deep domain knowledge of the underlying silicon, sensors, and systems — so the AI behaves predictably under the resource limits, latency targets, and reliability bars of the device it ships on.
From on-device inference at the edge to gateway aggregation and cloud-scale model serving, we cover the full stack. Our AI testing capability surfaces the issues that matter for shipping products: response accuracy, intent recognition fidelity, training-data quality, and the system-resource impact of running AI alongside everything else the device has to do.
- On-device AI tuned for edge resource & power constraints
- Gateway / edge-network AI and cloud AI services
- Sensor fusion algorithms across multi-modal data
- Audio & optical intent recognition with measurable accuracy
- End-to-end performance, accuracy, and resource validation
Where We Deliver
Three focused service lines covering the full AI integration lifecycle — from model development and deployment, to dedicated AI testing, to custom solutions tailored to your industry.
- On-device AI for edge computing
- Gateway & edge-network AI
- Cloud AI services & model serving
- Custom model training & tuning
- SoC & embedded silicon integration
- Audio & optical intent recognition
- Response accuracy & F1 score metrics
- Training/testing data generation
- System resource validation (memory, CPU, BMS)
- Co-existence & concurrency testing
- Healthcare, finance, retail, manufacturing
- Customer service automation & predictive maintenance
- Sensor selection, fusion & validation
- Custom edge solutions for real-time processing
- Cloud infrastructure for model deployment
Coverage Across the AI Stack
From on-device inference to cloud-scale serving — the disciplines that determine whether AI works reliably in shipping products.
- On-device AI inference
- SoC & embedded silicon
- Resource & power tuning
- Gateway aggregation
- Hybrid edge/cloud topologies
- Distributed inference
- Scalable model serving
- AWS, Azure, Google Cloud
- Model lifecycle management
- Multi-modal data integration
- Fusion algorithm validation
- Sensor selection & integration
- Audio & voice intents
- Optical / vision intents
- Multi-modal intent fusion
- F1 score & accuracy
- Latency & throughput
- Reliability under load
- Training/testing data generation
- Edge-case curation
- Labeled dataset pipelines
- Memory, CPU, BMS impact
- Co-existence with non-AI workloads
- End-to-end resource budgets
Ready to Ship AI That Works in the Real World?
Talk to a Novus engineer about model development, AI testing, sensor fusion, edge deployment, or cloud-scale model serving.