Connect your first device this afternoon.
No IoT engineer. No hand-written schemas. No weeks of setup. Nabha reads any protocol, infers your tags, and builds your dashboards — so you go from a live sensor to real insight the same day you sign up.
- Field protocols in — MQTT, Modbus, OPC-UA, S7, EtherNet/IP, MELSEC and HTTP
- Security-first, isolated per organization
- Live dashboards from any protocol, built in
Most IoT platforms make you model your data first. Nabha infers it.
This is the whole difference, and it is an architectural one. Conventional IoT platforms assume a person defines the data model before any telemetry means anything — which is why they are sold with an integrator attached. Nabha starts from the payload instead and works outward. That is what we mean by AI-native: the AI is not a feature bolted onto a modelling tool, it is the thing that removes the modelling step.
The conventional stack
Modelling-first
- Scope a data model with an integrator.
- Hand-write tag maps for every device type on site.
- Build the dashboards against those tags.
- A firmware update changes a payload. The mapping silently breaks. Back to step 2.
Weeks of setup, and someone else's calendar.
Nabha
Inference-first
- Point a device at the gateway — MQTT, Modbus, OPC-UA, S7, EtherNet/IP, MELSEC and HTTP.
- Nabha reads the payload and infers the tags. There is no step where you write a schema.
- Dashboards build on the inferred tags and start showing live values.
- A payload drifts. Nabha re-infers it and proposes the correction for you to confirm.
Live the same afternoon, with no integrator in the loop.
Tags inferred from the payload
LiveNabha reads what a device actually sends and derives the tags from it. No mapping files, no per-device configuration, no schema written by hand before the first reading appears.
Tags that repair themselves
LiveWhen payloads drift from their established shape, the platform re-infers them on its own and raises a proposal. Nothing changes without your confirmation.
Dashboards versioned like code
LiveBuild, duplicate, export and import boards; each carries a version and a last-updated stamp, so a change you regret is a revision rather than an incident.
Dashboards you build by talking
LiveChat with the AI agent and it builds the board with you — ask for a panel, change a threshold, regroup by site, and watch it happen. Not a template picker and not a prompt box that returns a guess: an interactive session that ends with a dashboard in your account.
ML and diagnostics on your telemetry
LiveRun ML jobs directly against the time-series store, with no export pipeline: anomaly detection on any tag, and predictive analytics on your history. Diagnostics read your telemetry and the model output together, and you choose which AI model does the reading — Mistral, DeepSeek, Claude or ChatGPT.
So — an AI-native IoT platform is one where inference replaces the data-modelling step, rather than one that adds a chat box to a platform still built around hand-written tag maps. Everything above is in the running product.
Where Nabha runs, and who runs it.
The platform runs at app.nabha.cloud. The screens in the product tour are recordings of it.
- Platform
- app.nabha.cloud Multi-tenant, isolated per organization. Hosted in Mumbai, India.
- Access
- Self-serve, free tier to start Connect a device and go. Paid plans from $150/month plus your tags — data kept forever, dashboards, alarms, AI and ML included.
- Company
- Varalix Digitech Solutions Private Limited India. CIN U62011KA2026PTC228684. GSTIN 29AAMCV7522M1ZJ.