Why SVAIO
Industrial expertise + technology + AI
Three arguments, and the third is the one that matters: we start with the number you are judged on, and choose the technology afterwards.
End-to-end capability
- Machine
- Sensor
- Edge
- AI
- Platform
- Insight
Six disciplines that are usually six suppliers. The handover between them is where an industrial project loses a quarter of its time, and most of its accountability.
Industrial IoT
Reading the machines and controllers already installed, whatever they speak.
Edge Computing
Compute next to the process, so a decision does not depend on a link staying up.
AI / ML
Anomaly detection and prediction built on that plant's own history, not a generic model.
Data Engineering
Pipelines and storage that keep industrial time series usable years later.
Analytics
The measures a plant is already judged on, computed the same way every time.
System Integration
Joining what we build to the ERP, CMMS and historians already in use.
Built for real industrial environments
Not a greenfield plant, not a lab. The machines are already there, the systems around them are already in use, and the network is not as good as the drawing says.
Retrofit existing machines
No requirement to replace a controller that has run correctly for twenty years.
Integrate existing systems
The SCADA, historian and maintenance system stay; they gain a source rather than a rival.
Edge-first architecture
The plant keeps working when the network does not, because the decision is local.
Cloud / On-Premise flexibility
Where the data lives is your decision, and it is not one you make once and cannot revisit.
Scalable deployment
What is proven on one line is what gets rolled out, rather than rebuilt for the next.
Industrial-grade reliability
Built for a cabinet on a shop floor, not a rack in an office.
Business-first approach
Technology follows the business problem
Two lists, and the order between them is the entire method. Everything on the right is chosen only once something on the left has a number against it.
We start with
- Downtime
- OEE
- Maintenance cost
- Asset reliability
- Production losses
- ROI
Then we select
- Sensors
- Connectivity
- Edge
- AI / ML
- Platform
- Analytics
Our engagement model
Assess, pilot, measure, scale
Four steps, and the third is the one that is usually skipped. Measuring before scaling is what turns a pilot into an argument instead of an anecdote.
Step 01
Assess
Understand the problem — which losses are real, and what they cost.
Step 02
Pilot
Prove the solution on a line small enough to be honest about.
Step 03
Measure
Validate the ROI against what was measured before it started.
Step 04
Scale
Expand across machines, lines and plants once there is something worth repeating.
Where we are
SVAIO TECH is an early-stage industrial technology company based in Noida, Uttar Pradesh. We are engineers, and the work described on this site is work we do — it is not a product you can buy off a page.
There are no customer names or performance figures anywhere on this site, and that is deliberate rather than an omission. When there is a plant willing to be named alongside a number, both will appear together.
Turn industrial data into measurable business outcomes.
Have a downtime or OEE problem?
Start with one machine, one line or one number you cannot explain. A short conversation is usually enough to say whether we can help, and how.