Industrial AI Doesn't Answer Questions – It Captures Expertise

01 /The Concept

"Your best people can't be everywhere," says Circuit, "but their expertise can" In other words, the startup has built a platform that makes deep technical knowledge available to every employee – regardless of whether a subject-matter expert happens to be nearby.

Circuit serves two distinct audiences.

The first is manufacturing companies, where the platform helps teams quickly diagnose and resolve production-line problems and maintain equipment before it fails.

The second is field service companies – HVAC, plumbing, and electrical contractors – where the platform speeds up diagnostics so technicians can complete repairs and maintenance on the first visit.

Somewhat surprisingly, one of the most prominent features on Circuit's homepage isn't troubleshooting at all – it's a proposal generation module that helps these businesses win new jobs.

It starts with rapid feasibility assessment: does the company have the competencies and capacity to take on a particular job? Can those competencies be demonstrated convincingly enough that the bid is worth submitting, rather than wasting effort on a fight it can't win?

From there, customer-facing staff can build technically accurate proposals – with full equipment configurations and parts lists – without pulling engineers away from the floor. The platform guides them through every step of the process. If a customer's request is missing critical details, Circuit formulates the right clarifying questions to move things forward.

The platform's more intuitive features follow: any question about repair procedures, equipment configuration, or troubleshooting can be asked conversationally, and the system walks users through the answer.

For technicians using the mobile app in the field, Circuit doesn't just answer questions – it guides them through each step of a repair or maintenance procedure with short, plain-language instructions: "replace this component," "check whether voltage has returned to the generator," "if yes, do this next."

For customer service reps working in Salesforce, the platform surfaces as a chat widget inside the CRM – with buttons that invoke specialized AI agents to handle specific tasks, step by step.

Circuit was founded in 2024 and raised its first $20M immediately after graduating from an accelerator. Two more rounds followed. The startup has now raised an additional $30M.

02 /Why It Matters

As one of Circuit's customers put it, the market is already flooded with "general-purpose" AI products capable of answering everyday questions. Circuit's value is that it's purpose-built for manufacturing and field service – a domain that's been conspicuously underserved.

That observation echoes something the founder of Squint ([related review](/review/zdes-u-tebja-100-shansov-pobedit-glavnogo-konkurenta)) told Fortune: "For some reason, industrial manufacturing is a neglected topic in the technology world. Even now, it's nearly impossible to gather a roomful of founders building startups in manufacturing, because there are almost none in Silicon Valley. So when we talk about our main competitors – they aren't other startups. They're binders full of paper manuals. "

Squint built a platform that lets manufacturers digitize their operational knowledge and gives factory workers an app for running machines correctly, diagnosing failures, and handling maintenance on their own. Squint has raised $59M; the comparable platform DeepHow ([covered here](/review/programmirovat-nuzhno-ne-kompjutery-a-ljudej)) has raised $37.1M; and Zaptic has raised $19M.

The growing appetite for these platforms is backed by data: a Deloitte report found that even entry-level manufacturing jobs now demand a significantly higher baseline of technical competency than a decade ago, driven by increasingly complex equipment and production processes.

That skills gap compounds a labor shortage. Between 2024 and 2033, manufacturing employment in the US is expected to grow by 3.8 million jobs – but an estimated 1.9 million of those positions will go unfilled due to a lack of qualified candidates.

AI can bridge that gap – allowing companies to fill open roles with less-experienced workers who are guided through their tasks in real time, without putting expensive equipment at risk.

The same dynamic is playing out in field service. In February of last year, XOi ([related review](/review/tema-v-kotoroj-mozhno-i-horosho-zarabatyvat-i-horosho-prodatsja)) raised $200M in a single round for an app that lets a technician photograph an equipment nameplate and immediately receive a step-by-step diagnostic sequence followed by a step-by-step repair sequence.

03 /Opportunities

The broad direction is clear: building AI platforms for manufacturing and field service companies that give workers the expertise they currently lack.

But there's an important nuance from Circuit's founder worth keeping in mind: "Manufacturers don't need another AI chatbot that answers questions. What they need are systems that turn expertise into repeatable actions."

That framing resonates beyond industrial settings. It surfaced in a [recent review](/review/razrabotal-prilozhenie-zarabotal-deneg) of Layers – a platform from a completely different category. Layers handles the full marketing lifecycle for mobile apps: connect your GitHub repo, and the platform analyzes the market, maps the competition, devises a go-to-market strategy, and executes it within a defined budget.

Layers eliminates the need for developers to switch between a dozen separate tools – or keep pinging ChatGPT for market research and ad strategy in between.

So it seems the era of "yet another AI chatbot" is ending not just in manufacturing but across many areas of business – wherever AI is still used primarily as a question-and-answer assistant rather than a system that drives action.

Where else can AI platforms be built that turn expertise into repeatable workflows?