For enterprise custom AI vision hardware, the biggest fear isn't technical difficulty, but going in the wrong direction from the start. When many teams receive requirements, their first reaction is to find the most powerful chip, the most expensive lens, or directly open a mold for the enclosure—but the real first step should be translating the "scenario problem" into an "engineering problem."
For example, if you want a camera that can identify defects on a production line, the first things to clarify are: What is the line's takt time? What is the minimum defect size? Is the ambient lighting stable? Are there restrictions on detection distance? These parameters directly determine whether you should use a global shutter or a rolling shutter, choose visible light or infrared, and even whether to add a polarizer. Listing all the information scattered in business people's minds and reviewing it with algorithm engineers and mechanical engineers is when the project truly "begins."
Next comes hardware selection. Here's a suggestion: Don't customize from the start. Try to use mature modules available on the market—industrial cameras, lenses, and light sources—they have been validated at scale, ensuring stability and supply. Customization can be focused on computing units, interface boards, and mechanical structures, as these are often strongly related to your specific scenario. If you even need to customize the image sensor, it usually means the requirements haven't been fully sorted out, or the project itself is better suited for validation with general-purpose products first.
During the prototype stage, you have to "embrace the ugly." Using a 3D-printed shell, jumper wires, and a desk piled with cooling fans is all fine. The goal is to get the algorithm running and the data flow working in the shortest time, so the client can see real results. Many projects die from "trying to get it perfect in one shot," and after three months, they can't even capture a decent test image.
Finally, supply chain and certification come into play. Only then should you consider molds, thermal design, protection ratings, EMC testing, and other matters. The selection of generations, the logic of upgrades, and the pace of customization should all be handled step by step. AI vision hardware isn't rocket science, but it requires you to break down requirements like you're building a product, not pursue parameters like you're doing research.
So, back to the question at the beginning: Where should you start? Start by chatting with production line workers in the meeting room, by repeatedly confirming the "can we actually detect it" question, and by acknowledging that "getting it to work first" is more important than "making it look good." Once the path is clear, every subsequent step will follow naturally.