Edge AI Co-Design Services
Edge Computing Product Design and Development Consultants: AI Hardware & Software Co-Design Services
Bring Intelligence to the Edge with Co-Designed Edge Computing and AI Solutions
In a world where real-time insights, ultra-low latency, and offline decision-making are essential, edge AI is leading the next evolution of intelligent electronics. Promwad is your trusted partner for AI software development services and edge computing hardware consulting, offering an integrated approach to building edge-native devices — from silicon to cloud integration.
We are committed to data security at every stage of project execution, implementing industry certifications, confidentiality agreements, and robust security protocols to safeguard your intellectual property and sensitive information.
Our team combines AI software development solutions with deep expertise in embedded platforms, delivering tailor-made edge AI solutions that balance performance, power efficiency, scalability, and achieve optimal performance. We also specialise in integrating IoT devices with cloud platforms and developing applications for real-time control and monitoring as part of our comprehensive edge AI offerings.
Why Co-Design Matters in Edge AI Projects
Unlike traditional cloud-based AI, edge AI systems must work under strict constraints:
- Limited power and memory
- Real-time response requirements
- Intermittent or no connectivity
- Regulatory and privacy concerns (GDPR, HIPAA)
That’s why we focus on co-designing hardware and AI software together, ensuring:
- Efficient ML model execution on specific MCUs, NPUs, and ASICs
- Optimised thermal, battery, and size performance
- Seamless integration of sensors, RF modules, and logic blocks
Our Edge AI Co-Design Capabilities for Real Time Data Processing
AI-on-Edge Hardware and Software Development Services
- Edge computing systems development
- Data gathering and labelling
- AI model selection, training, pruning, and quantisation
- Model deployment using TensorFlow Lite, ONNX, TVM, and Edge Impulse
- OTA-ready inference pipelines
- Real-time data processing close to the data source for time-sensitive industrial IoT applications
Edge Computing Hardware Consulting
- Platform selection: NXP, Qualcomm, Ambarella, NVIDIA, Lattice Semiconductors, AMD, Altera, Microchip
- Low-power system architecture (MCU, MPU, SoC, FPGA)
- Custom board design, thermal optimisation, RF & analog layout
- Scalable functionality to support growing or changing industrial IoT requirements
Edge AI System Integration
- Sensor fusion: vision, voice, IMU, biosignals, RF
- AI accelerators (NPUs, DSPs, tinyML on MCUs)
- Connectivity and Networking / MQTT, RTP, ONVIF, IPMX
- Bridging edge devices with central systems via gateways, including encryption, authentication, storage and processing
Data Management for Edge AI
Effective data management is at the heart of successful edge AI deployments. As edge devices generate massive volumes of sensor data, the ability to process and analyse this information in real time is essential for improving operational efficiency and gaining valuable insights. Implementing edge computing solutions requires a robust data management strategy that supports seamless integration, optimised performance, and secure data transfer across the entire edge computing infrastructure.
To support comprehensive data management, edge computing solutions incorporate big data analytics platforms, data lakes, and data warehouses, enabling advanced ML and AI capabilities at the network edge. These tailored solutions meet specific business objectives, such as predictive maintenance, real-time data analysis, and improved operational efficiency. Decentralised processing at the edge allows for faster data processing, reduced latency, and enhanced performance, empowering organisations to make data-driven decisions.
Integrating Cloud Services with Edge AI
Integrating cloud services with edge AI unlocks the full potential of edge computing solutions by enabling seamless data transfer, scalable storage, and advanced analytics across connected devices and cloud platforms. Cloud integration is a cornerstone of modern edge AI applications, providing the flexibility and scalability needed to support real-time data processing, machine learning, and predictive analytics.
Edge computing solutions that prioritise cloud integration enable smooth operation and maintenance services, ensuring that real-time data is always accessible for analysis and reporting. Legacy systems can also be seamlessly connected to edge AI applications through cloud services, facilitating the transition to digital transformation and supporting the latest industry trends in big data analytics and AI.
Our AI Hardware/Software Co-Design Workflow
1. Feasibility Study
Define goals, data availability, power and latency requirements
2. Hardware Platform Selection
MCU vs SoC vs FPGA + accelerator review
3. Model Design & Training
Based on public data or your proprietary datasets
4. Firmware & ML Integration
Edge inference pipeline setup
5. Prototype & Test
Bring-up + validation of accuracy, throughput, power
6. Industrialisation Support
DFM, test plans, certification, EMS onboarding
Ready to Build Edge Intelligence Into Your Edge Devices?
Whether you're starting from a concept or scaling an existing design, our artificial intelligence software development and edge computing product design consultants can help you:
- Shorten time-to-market
- Reduce BOM and energy footprint
- Deliver real-time performance where it matters
Let's build smarter embedded systems — together.
Contact us to discuss how we can support your next Edge AI project!
Drop us a line about your project! We will contact you today or the next business day. All submitted information will be kept confidential.