Industrial AI: From Idea to a Product Customers Are Piloting in 5 Months

This engagement is real. The client asked to stay anonymized — no name, no company details beyond industry — so the story and the numbers below are exactly as they happened; only the identity is withheld.

Segment	Manufacturing · equipment maintenance and field service
Markets	India and US
Our role	Fractional CPO, plus our build team
Outcome	Idea to live MVP in 5 months · 3 customer pilots · 1 converted to paid

The situation

An early-stage industrial AI startup was building AI-powered diagnostics and remote-expert support for the technicians and operators who keep machines running in factories and field operations.

The founders knew the problem deeply. What they needed was someone to turn that knowledge into a product — and to build it on several fronts at once:

• An AI diagnostics assistant that helps a technician work out what's wrong with a machine
• An expert knowledge base that captures what experienced engineers know
• A live operations dashboard for the teams running the equipment

Building three surfaces at once is where early products usually go wrong: either the team is sized for everything up front and pays for capacity it can't use yet, or it's sized for one surface and stalls when the second becomes urgent.

What we did

Paranjay stepped in as fractional CPO. He shaped the product with the founding CTO — what to build first, what to leave out, and what a customer pilot needed to prove. The founding CTO stayed in the same decision loop throughout, so every call was made with full product context.

We started building immediately, with one engineer. No multi-week ramp-up while a full team assembled. The first commit landed in the first sprint.

The team grew with the scope, not ahead of it. As each surface became clearer, AI/ML engineers joined for the assistant and knowledge base, full-stack engineers for the platform and dashboard, and QA once there was enough product to test. The team reached seven people over ten sprints.

The result

Ten sprints in, the MVP was live — not an internal demo, but a product real customers were using.

Metric	Result
Time from idea to live MVP	5 months
Sprints delivered	10
Team	Scaled from 1 to 7 (AI/ML, full-stack, QA)
Customer pilots	3 running
Converted to paid	1

"I knew our customers. Rightshift knew how to turn that into a product that works on real machine data. That's the gap we couldn't close on our own." — Founder, industrial AI startup

Why it worked

1. Senior product judgment from week one. A fractional CPO in the seat meant the founders didn't have to guess what to build first — or hire for a role they couldn't yet define.
2. The team matched what was actually being built. AI/ML for the intelligence layer, full-stack for the platform, QA once there was surface area to test — not a generic team applied uniformly.
3. The founders stayed in charge. The founding CTO was part of every decision, so the product reflected their knowledge of the customer, not our assumptions.

Building something similar?

If you're turning industry expertise into a physical AI product — for machines, vehicles or energy — tell us what you're building. Paranjay replies personally within 1 business day.