Industries · Manufacturing
AI for manufacturing that works on the shop floor, not just in the demo.
We help startups building AI for factories, machine shops and OEMs get their machine data live, clean and trusted — so predictive maintenance, diagnostics and quality inspection hold up across every customer site.
Where manufacturing AI gets stuck
Every factory is different. Your AI has to work in all of them.
Every site is different
Different machines, ages, controllers and operating patterns. A model trained on a few machines learns their quirks — and stumbles on the next customer's line.
Sensor data without context
A vibration spike might mean a failing bearing — or a heavier part on the line that day. Without load, speed and product context, alerts get ignored.
Labels live in maintenance tickets
The ground truth — what actually failed and what was replaced — sits in free-text tickets and spare-parts orders, in another system, days after the event.
Legacy machines and patchy connectivity
Older equipment, mixed controllers and shop-floor networks that drop out. Data arrives late, out of order or not at all — and the pipeline has to cope.
What we build
Physical AI products for the machines that keep factories running.
Paranjay leads every engagement as your fractional Head of Data & AI, Product or Engineering. Our team builds alongside yours.
Predictive maintenance
Spot failing components before they stop the line — built on the signals and maintenance history that actually predict failure on your customers' machines.
Vibration · Temperature · Work orders
Diagnostics assistants
AI that helps a technician find the likely fault and next steps, combining sensor readings, service history and manuals — like the assistant we built for an industrial AI startup.
Service history · Manuals · Sensor data
Quality & machine monitoring
Vision-based inspection and machine-efficiency tracking that stay reliable across sites, lighting, camera angles and machine types.
Cameras · PLC data · OEE
Device-to-cloud data platforms
Collect, move and store shop-floor data reliably on ready-made gateways and edge devices — the foundation everything else depends on.
OPC UA · MQTT · Edge
The data we work with
From the PLC to the model.
Our founder has built data platforms at enterprise scale — including one with 1M+ endpoints connected at once — and machine-learning inventory optimization on real data at Nissan. We work with ready-made sensors, gateways and edge devices; we don't design hardware, we make the data coming off it trustworthy.
How we work
Data first. Then AI.
01 — DIAGNOSE
Find the real problem
Two weeks with your team, your machines and your data. A written read on what to fix first — and what to ignore.
02 — BUILD
Ship it with you
Sprint by sprint, with a working demo each time: data pipelines, models that hold up on field data, and integration with your customers' sites.
03 — PROVE
Measure the outcome
Judged on the outcome we named up front — a pilot that converts, downtime avoided, a platform ready for your next round.
Proof · Industrial AI
An industrial AI startup went from idea to a product customers are piloting in 5 months.
Read the case study →5 mo
idea to live MVP
3
customer pilots
1
converted to paid
1 → 7
team scaled with scope
Related reading
Go deeper.
How to Scope a Predictive Maintenance Pilot That Converts to Paid →
Most physical AI pilots end with a polite 'interesting, let's stay in touch'. The ones that convert are scoped differently from day one. Here's how to design a pilot your customer can say yes to.
Why Your Physical AI Demo Works in the Lab and Fails in the Field →
The demo worked on your test data. On a real customer's machines, it falls apart. It's rarely the model's fault — it's the gap between lab data and field data. Here are the five gaps we see most, and how to close them.
What Data to Collect Before You Build Physical AI →
Most physical AI startups collect too little of the right signal and too much of the wrong one. Here's how to decide what to capture from your machines, vehicles or sensors before you train a single model.
Common questions
Questions founders ask us.
Your next step
Building AI for machines, vehicles or energy? Let's talk.
Tell us what you're building and where it's stuck. Paranjay replies personally within 1 business day with a first read on where to focus. No pitch. No pressure.
or email hello@getrightshift.com