OT-Native. Production-Proven. No Generalist Detours.
Bhagawati was built by engineers who came up through operational technology — SCADA, PLCs, and process control — before they ever trained a model. That lineage shapes every deployment we ship.
We Speak PLC Before Python
Our engineers carry backgrounds in process control, SCADA architecture, and industrial network design. Machine learning arrived later — layered onto deep OT knowledge, not the other way around.
That sequence matters. Edge models trained without operational context produce alerts operators ignore. Ours are calibrated to your actual runtime conditions from day one.
From Single-Site Pilots to Multi-Facility Deployments
Across manufacturing, utilities, and infrastructure operations, our engagements follow one path: instrument the existing assets, establish a runtime baseline, deploy edge inference, and scale. No rip-and-replace. Measurable uptime economics at every step.
Asset-Agnostic Integration
Runtime Learning at the Edge
Proven Across Verticals
Models train on live operating data, establishing what normal looks like across shift patterns, load cycles, and seasonal variance.
Manufacturing, utilities, and smart infrastructure deployments with documented uptime improvements and production economics on record.
Existing PLCs, historians, and sensor networks stay in place. We instrument around what's running, not what we wish were there.
If you're evaluating a production deployment — not a proof-of-concept — we're the conversation worth having. Bring your operational context; we'll bring the engineering depth.