Engineering Services Built for Product Companies
From silicon to systems, DNCL Technologies partners with product companies on Semiconductor Systems Design, Product Design Engineering, and flexible staff augmentation to help you ship faster.
What We Offer
Four connected service lines — from semiconductor design to the app your customer holds — each backed by experienced engineering teams and Edge AI capability.
Semiconductor Systems Design
End-to-end ASIC design and verification, design-for-testability, physical design, and analog layout services for advanced silicon nodes.
Explore Semiconductor Systems Design
Product Design Engineering
Hardware and firmware engineering for connected products — from schematic design and PCB layout to embedded software and firmware bring-up.
Explore Product Engineering
Digital Engineering
UI/UX design, app development and web development in one team — device HMIs and design systems, Android and iOS apps, IoT dashboards and secure cloud APIs, all with Edge AI built in.
Explore Digital Engineering
Staff Augmentation
Scale your engineering team quickly with vetted semiconductor, embedded, and software professionals who integrate directly into your workflow.
Explore Staff AugmentationWhat We Do
A consistent way of working across every engagement, no matter the service.
Customized Solutions
Every engagement is scoped around your product, timeline, and technical constraints — no one-size-fits-all templates.
Cutting-Edge Innovation
Our teams stay current with the latest tools, process nodes, and design methodologies across silicon and embedded systems.
Technological Guidance
We act as an extension of your engineering leadership, offering guidance on architecture, tooling, and delivery.
"DNCL feels like an extension of our own engineering team — they understand our roadmap, not just our tickets."
— VP Engineering, Product Company ClientWhy Choose DNCL Technologies
What clients consistently point to when asked why they keep coming back.
Responsive & Committed
Engineers who stay engaged with your roadmap, not just the ticket in front of them.
Rigorous Selection Process
Every engineer is vetted for hands-on project experience before ever being proposed to a client.
Regular, Proactive Communication
Structured check-ins keep delivery visible, so surprises are caught early, not at the deadline.
Cross-Timezone Delivery
Our teams work overlapping hours with US, EU, and APAC clients to keep projects moving.
Industries We Serve
Our engineering teams have shipped products across a wide range of sectors.
Engineering Insights
Perspectives from our design and delivery teams.
Why DFT Matters Earlier in the Design Cycle
Building testability in from the start reduces cost and schedule risk downstream.
Choosing the Right MCU for Connected Products
Balancing power, cost, and peripheral needs when selecting a microcontroller.
When to Augment vs. Outsource Engineering Work
A practical framework for deciding how to scale your technical team.
Physical Design Signoff Checklist
Key checks our teams run before tapeout on every project.
Silicon Bring-up: What to Plan For Before Tapeout
Why boot sequencing and peripheral validation plans matter long before first silicon arrives.
Designing for Certification From Day One
How early regulatory planning avoids costly hardware redesigns later.
Frequently Asked Questions
Answers to common questions about working with DNCL Technologies.
Which service is the right fit for my project?
Can DNCL support multiple service lines on one project?
How quickly can a project or engineer start?
Do you sign NDAs and handle IP protection?
Where are your engineering teams located?
Do you support post-silicon validation and embedded bring-up, or just design?
Can DNCL also train our internal engineering team?
Edge AI Built Into Every Service We Deliver
Edge AI is not a separate product line at DNCL — it runs through all six services. We design the accelerator into the chip, deploy the model in firmware, and surface the results in your app and dashboard, so intelligence runs locally with low latency and low power.
- NPU and AI accelerator integration at semiconductor level
- On-device inference in embedded firmware and mobile apps
- Model selection, quantisation and pruning for constrained hardware
- Computer vision, sensor fusion and predictive maintenance
- Adaptive interfaces and live Edge AI analytics in dashboards