How a top electronics manufacturer got to GenAI 80% faster

A global, Fortune 500 technology manufacturer turned 150M+ connected devices into a real-time intelligence engine, in weeks, not years.

See what's possible in 30 minutes.

80%

Reduction in time-to-market for device intelligence and GenAI experimentation

3–4

quarters faster on strategic manufacturing initiatives

50%

less time spent on repetitive data engineering works faster on strategic manufacturing initiatives

See what's possible in 30 minutes.

Bring your data challenges. We'll show you how this manufacturer cut time-to-market by 80%, and what that could look like for you.

The Challenge

Data from more than 150 million devices was piling up faster than it could be used. Previous platform attempts dragged on for 2–3+ years each, adding a new source could take 9 months, and leadership had grown skeptical that a real solution existed. Meanwhile, predictive maintenance, quality optimization, and new revenue opportunities all sat out of reach.

What Changed

DataOS layered on top of existing systems instead of replacing them, turning raw telemetry into governed, reusable "data products” instead of one-off tables. A working prototype was live in five days. Full production deployment followed in four weeks. Within 12 weeks, leadership approved enterprise-wide rollout.

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Finding the next sales opportunity in existing data.

Industrial Manufacturer

The first use case took weeks. The next ones took days.

How DataOS Made the Difference

DataOS layered onto existing systems, turning scattered telemetry into governed data products.

Layer on, don't rip out

DataOS connects to existing infrastructure at any maturity level. No migration project, no torn-out systems, just a unified layer on top of what's already there.

Data products, not one-off tables

Data gets defined once, as a version-controlled, reusable data product, with quality checks, documentation, and governance built in, instead of engineers hand-building the same pipeline over and over.

AI-ready from day one

A built-in semantic layer describes data in business terms automatically, so natural-language queries and AI agents work immediately, with no separate modeling effort.

Governance without the overhead

Access controls, lineage, and quality monitoring deploy with the platform itself, not as a bolt-on project added months later.

Weeks, not quarters

Traditional builds ran 12-18 months. DataOS reached a working prototype in five days and completed full production deployment in four weeks, ahead of the original six-week target.

The Five-Day Breakthrough Made The Difference

Friday

Nearly 50 sample telemetry files delivered

Weekend

Data structure analyzed, environment built

Tuesday

Working demo environment ready

Following week

Results at a Glance

150M+ devices

Unified telemetry, streaming in real time

4 weeks

Full production deployment, ahead of a 6-week target

40+ engineers

Trained on a new, product-first way of working with data

"DataOS fundamentally changed how we think about data. Instead of building one-off solutions, we're now creating reusable data products that serve multiple business needs simultaneously."

- Sr. Director of Data & Analytics

Why It Worked

Executive sponsorship kept the project moving. A rapid-prototyping approach replaced months of upfront planning. And a deliberate shift from project thinking to product thinking turned one-off fixes into reusable, governed data assets, unlocking not just faster maintenance and quality wins, but new data-driven revenue streams like premium warranty offerings and partner analytics services.

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