I made a decision that saved me six months and probably $200,000: I put the AI on the device, not in the cloud.

In this episode, I walk through the architecture of OrbCare—a HIPAA-compliant health monitoring robot I'm building—and show you exactly why edge-first architecture isn't just technically superior for hardware startups, it's the only approach that actually works at scale.

What you'll learn:

• The mental model shift from cloud-centric to edge-centric thinking (this changes everything)
• Why "what needs to happen locally?" beats "what's the minimum to send to cloud?"
• Real technical decisions: NVIDIA Jetson Orin Nano, YOLO, MediaPipe, encrypted local storage
• How edge-first architecture makes HIPAA compliance easier, not harder (video never leaves the device)
• The trade-offs nobody talks about: hardware constraints, updates, debugging, cost structure
• Edge vs. Cloud decision framework you can use today

The case study:

• All AI inference on-device (object detection, pose estimation, breathing analysis)
• 20-30 FPS processing with <50ms latency
• Works during internet outages (designed for disconnection, not connection)
• Near-zero cloud costs per device
• Privacy by default—no PHI transmission

Why this matters:

Most hardware startups default to cloud-first because that's what they know from web development. Then they hit Series B and realize they need to rebuild everything. That's $2-5M and 12-18 months you don't get back.

This is the architectural decision that determines whether you scale smoothly or spend 18 months on a rewrite when you should be growing revenue.

For founders building robotics, medical devices, IoT, or edge AI—this episode shows you how to avoid that wall.

Next episode: HIPAA compliance at the edge—how to build medical device systems without sending sensitive data to the cloud

ferreiracto.com

Primary:#EdgeComputing#EdgeAI#HardwareStartups#RoboticsArchitecture#MedicalDevices#HIPAACompliance#IoTArchitecture

Secondary:#NVIDIAJetson#ComputerVision#StartupCTO#DeepTech#EmbeddedSystems#AIHardware#TechnicalArchitecture

Platform/Ecosystem:#BostonStartups#SeriesA#TechnicalDueDiligence#StartupEngineering

SEO/Discovery:#EdgeVsCloud#LocalProcessing#RealTimeSystems#PrivacyByDesign#AutonomousSystems

$20 million Series B. Six months into production. Total architecture rebuild required.

This happens more often than you think. And it's completely avoidable.

In this episode, I walk through the 7 architecture red flags I've learned to spot immediately when reviewing hardware startup systems:

• Security as "Phase 2" (why this destroys hardware companies)
• Cloud-first thinking for edge devices (your robot shouldn't be a brick when WiFi drops)
• No fail-safe architecture (things break - have a plan)
• Real-time requirements as an afterthought (milliseconds matter)
• Ignoring the firmware update problem (you can't update your update system if it's broken)
• Underestimating data pipeline complexity (that arrow on your diagram is hiding chaos)
• No hardware-software testing strategy (manual bench testing doesn't scale)

PLUS: Big announcement - this podcast is now "Before the Rewrite" and I'm bringing VCs onto the show to talk about what they actually look for in technical due diligence. We're creating frameworks you can use to assess your architecture against what investors evaluate.

For hardware founders building robotics, medical devices, or AI at the edge - this is architecture guidance before it becomes a fundraising obstacle.

Host: Peter Ferreira | 30 years software, 5 years robotics, currently building OrbCare (HIPAA-compliant health monitoring robot)

ferreiracto.com

Primary Tags:#HardwareStartups#RoboticsArchitecture
#TechnicalDueDiligence#EdgeComputing#StartupCTO#VentureCapital#SeriesA

Secondary Tags:#MedicalDevices#AIHardware#EmbeddedSystems#FirmwareEngineering#StartupTech#BostonStartups#DeepTech