OnTrack · 8 Jan 2026 · 35m · Episode 329
Avinash Harsh, co-founder of Wizerr AI, explains how his platform acts as an AI teammate for hardware engineers by providing a component intelligence layer that bridges the gap between design intent and procurement. He details how Wizerr AI analyzes thousands of data sheets to suggest functionally equivalent alternatives, reducing design risk and optimizing bills of material (BOM) before production. Harsh argues that while software can ship fast, hardware must ship right, and AI agents can help make better decisions faster by carrying design context through the fragmented electronics value chain. The discussion covers the transition from parametric search to intent-based selection, the integration of reliability data, and the vision of embedding this intelligence directly into tools like Altium Designer.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulationWizerr AI8-10 hours to 3-4 minutes
The AI platform analyzes component data sheets and supply chain data to suggest functionally equivalent alternatives with trade-offs, reducing component selection time from hours to minutes.
“if it takes 8 to 10 hours to find a truly deep alternate, uh, which can work with the design, you can find it in 3 to 4 minutes on visor, right? That's how we are looking at it.”
Avinash Harsh · Hear it at 15:38
Engineering & simulationWizerr AImonths of back and forth to a few minutes
The system autonomously optimizes the Bill of Materials by identifying technical mergers of functionally equivalent parts to achieve economies of scale, collapsing months of back-and-forth between engineering and procurement into minutes.
“So, the idea is not to just bomb. There are a lot of bomb tools, which is great, we need. But once you understand to a component functional level, then you can come back with economies of scale, merging different part numbers, different supplier names, but functionally equivalent onto that.”
Avinash Harsh · Hear it at 15:38
Engineering & simulationWizerr AI
The AI engine parses thousands of pages of data sheets, graphs, and pin diagrams at scale to understand component intelligence beyond parametric tables, enabling autonomous suggestions of functionally equivalent components based on design intent.
“when we are looking at graphs, when we are looking at pin diagrams, when we are looking at 1000s of pages of data sheet at scale with 1000000s of those, that's what the component intelligence layer does today, right? We are able to understand it, how an engineer would think about it.”
Avinash Harsh · Hear it at 5:14
Engineering & simulationWizerr AI
The platform uses an Electronic Language and Extraction Engine (ELX) to convert unstructured data sheet information into a machine-readable format, allowing the system to reason through component trade-offs and supply intelligence simultaneously.
“We call it ELX for lack of better words, but it's called electronic language and extraction engine. So with that, we are able to then understand at scale in a machine ready format on that piece.”
Avinash Harsh · Hear it at 13:34
early-stage companies
Other shows on the same ground, closest first.
Matthias Wagner, Founder and CEO of Flux, explains how his company uses agentic AI to democratize PCB design, allowing non-engineers to create hardware via natural language prompts. He discusses the historical bottlenecks of hardware design, the necessity of iterative back-and-forth for complex devices like smartphones, and the economic advantages of designing custom boards for products like vending machines. Wagner also outlines future integration challenges, including firmware generation and enclosure design, and predicts that lowering the cost of iteration will unlock significant innovation in robotics and IoT.
Tomide Adesanmi, founder of Circuit Mind, explains how hardware startups are outpacing large corporations in electronics innovation by leveraging agile design processes and modern tools. He details the traditional electronic product development lifecycle, from requirements and component selection to schematic capture, PCB design, and multiple prototype iterations (EVT, DVT, PVT). Adesanmi highlights the archaic nature of legacy hardware engineering compared to software and introduces Circuit Mind’s platform, which uses deterministic algorithms to automate schematic generation and optimize designs for cost, power, and supply chain availability.
Timon Ruban, Co-Founder of Luminovo, discusses the evolution of AI in electronics manufacturing, contrasting early hype with current practical applications enabled by large language models. He explains that successful implementation requires balancing comprehensive data context with specialized tools, such as part search APIs, to verify AI proposals. Ruban highlights Luminovo's focus on extracting structured data from unstructured sources like PDFs and emails, and developing 'agentic workflows' where AI assists human decision-making in supply chain management rather than replacing it.
Michael Corr, co-founder and CEO of Duro, and Justin Sears, Head of Product Marketing at Altium, discuss how Duro’s cloud-based PLM platform and Altium’s Agile Teams are breaking down silos between electrical, mechanical, and procurement teams. They explain how Duro’s API-first architecture enables specialized AI agents to automate tasks like BOM analysis and part sourcing, addressing the critical need for real-time supply chain visibility. The conversation highlights the acquisition of Duro by Altium, the shift from selling 'vitamins' to 'medicine' for hardware startups, and the resurgence of small, fast-moving hardware companies leveraging modern tooling.
Matt Liberty of Joulescope and Luke Beno of Tundra Labs and Werewolf.us discuss building custom AI-driven ERP systems to manage small-scale hardware businesses. They detail how AI tools like Claude Code and OpenClaw automate firmware debugging, protocol analysis for Juki pick-and-place machines, and inventory tracking via DigiKey and Mouser APIs. The guests emphasize that while AI accelerates software and operational workflows, it currently struggles with hardware design, arguing that engineers must retain fundamental physical intuition to effectively guide and debug AI-generated code and designs.
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