About Embedded AI Academy | AI-Assisted Development & TinyML Training

Our Approach

Where AI Works for Embedded Systems

AI training for embedded systems typically falls into two traps - either it’s cloud-focused tutorials that don’t translate to kilobyte-scale constraints, or it’s generic “use Copilot” advice that ignores the realities of safety-critical firmware. We’re different.

Embedded AI Academy trains you in two dimensions - deploying ML models on resource-constrained, real-time, safety-critical devices (AI IN embedded), AND using AI tools throughout the development lifecycle—coding, testing, requirements, architecture (AI FOR embedded development). Both grounded in embedded reality, not cloud hype.

Our instructors are embedded practitioners first, AI experts second. They’ve debugged race conditions at 3 AM, fought with flaky hardware interfaces, and navigated ISO 26262 audits. They know that “just add a neural network” isn’t a solution when you have 64KB of RAM and hard real-time deadlines. Every technique we teach has been battle-tested in real embedded projects at companies like Medtronic, ARRI, Mercedes-Benz, and many others.

From Embedded Systems to AI-Native Development

With 15+ years of embedded systems experience and an aerospace engineering background from TU Dresden, Luca has performed every role in the software development lifecycle - developer, tester, product owner, scrum master, agile coach. This hands-on experience across safety-critical systems (ISO 26262, DO-178B), real-time constraints, and hardware-software integration gives him a unique perspective on where AI can actually help embedded teams.

As founder of both Agile Embedded Academy and now Embedded AI Academy, Luca recognized a critical gap - embedded engineers were being left behind in the AI revolution. Cloud-focused training didn’t address their constraints. Generic “AI for developers” courses ignored safety regulations and real-time requirements. Meanwhile, the opportunity was enormous—not just AI in products, but AI transforming how embedded teams work.

Luca’s passion is AI FOR embedded development—the 10% vs 10x insight. Most teams use AI as “glorified code completion” and get 10% productivity gains. Deep integration throughout workflows—requirements, architecture, testing, debugging—can deliver 10x gains. But only if you understand embedded realities - hardware constraints, safety requirements, real-time behavior, and the skepticism that comes from decades of overhyped tools.

As co-host of the Agile Embedded Podcast and now the Embedded AI Podcast, Luca regularly connects with practitioners solving similar challenges across automotive, medical, aerospace, and IoT domains. He has successfully implemented transformations at companies like Medtronic, ARRI, Fresenius Medical Care, Mercedes-Benz, and many others. He is a DevOps Ambassador for DASA and regularly speaks at conferences like the Agile Embedded Conference and Deutsche Luft- und Raumfahrtkonferenz.

Luca Ingianni

Founder, Embedded AI Academy

How We Work

The Academy’s Approach

Embedded AI Academy is part of the do.institute family, from the creators of Agile Embedded Academy. Just as Agile Embedded Academy brought agile practices to embedded teams with a practical, skepticism-welcome approach, Embedded AI Academy brings AI to embedded engineers and their managers—grounded in embedded reality, not cloud hype.

The 10% vs 10x Insight

Most teams use AI as “glorified code completion” - GitHub Copilot autocompletes a few lines, ChatGPT writes a function. That’s 10% productivity gains. Real transformation comes from deep integration throughout your workflow - AI-assisted requirements analysis, architecture design reviews, test generation, debugging strategies, code reviews. That’s 10x gains. But getting there requires understanding both AI capabilities AND embedded constraints - safety regulations, real-time behavior, hardware limitations, toolchain quirks. We teach both.

Two Dimensions of AI Mastery

AI IN Embedded Systems - Deploy ML models on resource-constrained, real-time, safety-critical devices. Learn TinyML, model optimization (quantization, pruning), real-time inference on MCUs, hardware selection for ML, and safety considerations (ISO 26262, IEC 61508).

AI FOR Embedded Development - Use AI tools throughout the development lifecycle. Master AI-assisted coding (beyond basic autocomplete), requirements engineering with AI, architecture design reviews, test generation and debugging, and validation of AI-generated code. Equal or greater emphasis—broader applicability, immediate ROI.

For Engineers AND Managers

Technical training alone isn’t enough. Managers face strategic questions - Which AI opportunities are actually feasible for our embedded products? How do we build team capabilities without disrupting delivery? How do we navigate resistance from skeptical engineers (who’ve seen tool hype before)? Our manager-focused content provides assessment frameworks, ROI evaluation, team development strategies, and change management approaches—all specific to embedded AI adoption.

Embedded-Native, Not Cloud Adapted

We don’t teach cloud-scale Python tutorials adapted for embedded. Every course starts with embedded constraints - kilobytes not gigabytes, milliseconds not seconds, safety regulations not “move fast and break things.” Our instructors are embedded practitioners who’ve solved these challenges in real projects. When we say something works, it’s because we’ve proven it under the pressure of hard real-time deadlines and regulatory scrutiny.

15

Years Embedded Experience

66

Satisfied Clients

6000

Hours of Training Delivered

90

Podcast Episodes

Who We Serve

Teams We Transform

What embedded teams have in common - realization that generic AI training doesn’t address their reality. They need someone who’s been in their shoes and found practical solutions.

The Product AI Integrator

Senior embedded engineers (10+ years experience) tasked with adding AI to their products. You’re comfortable with C/C++, RTOS, and hardware interfaces—but now management wants machine learning in the device. Cloud-focused tutorials talk about gigabytes of data and Python frameworks, but you have 64KB RAM and hard real-time deadlines. You need embedded-native training on TinyML, model optimization, inference on MCUs, and how to maintain safety/security standards when adding AI to regulated products.

The AI-Assisted Developer

Experienced embedded engineers who’ve tried GitHub Copilot or ChatGPT and got disappointing results. The tools autocomplete a few lines (10% gains), but hallucinate when you need embedded-specific code, ignore your hardware constraints, and generate unsafe patterns for interrupt handlers or memory management. You know there’s potential here, but you need to learn deep integration throughout your workflow - going from basic code completion to 10x productivity gains through AI-assisted requirements, architecture, testing, and debugging. All while keeping the AI grounded in embedded reality.

Engineering Managers Navigating AI Adoption

Three common situations - Eager teams where engineers want to use AI tools but management is uncertain about risks and compliance. Management pressure where upper management pushes AI adoption but the team is resistant (they’ve seen tool hype before). Job security concerns where team members worry about displacement. We provide strategic frameworks for evaluating AI opportunities, organizational change management tools, team capability building approaches, and help you communicate the reality that AI augments embedded engineers’ expertise, doesn’t replace it. Technical training alone isn’t enough—you need organizational strategies too.

Real-World Embedded Teams

Our clients span industries with the most demanding embedded constraints. Automotive Tier-1 suppliers implementing AUTOSAR with AI assistance. Medical device companies navigating FDA approvals for AI-enhanced products (ISO 13485, IEC 62304). Aerospace teams meeting DO-178C requirements while exploring AI for flight systems. Industrial automation companies adding edge ML to PLCs and motion controllers. IoT startups scaling edge ML from prototype to production with battery and connectivity constraints. What unites them - embedded-first constraints (safety, real-time, resources) that generic AI training ignores.