Tethra
Tethra is a next-generation digital-twin and industrial IoT platform that ingests MQTT streams, enterprise data, and ML outputs into a single source of truth for assets, processes, and environments. It combines a high-performance data warehouse, command-line automation, a fully customizable HMI layer, and model validation workflows so teams can deploy, monitor, and iterate on AI-driven twins safely and continuously.
Our Features
MQTT-Native Digital Twin Engine
The MQTT-native engine subscribes to device and system topics, normalizes payloads, and maps them into digital-twin entities that stay synchronized with the physical world in real time. Quality-of-service levels, session state, and topic namespaces ensure reliable, scalable data flows across thousands of assets, making the twin a robust single source of operational truth.
AI-Ready Data Warehouse and CLI
Tethra ships with a time-series and event-centric data warehouse tuned for industrial and IoT workloads, from millisecond telemetry to aggregated KPIs. ML scientists can use CLI tooling to pull curated datasets, register new model versions, and launch validation jobs directly against production-like data, shortening the path from notebook to production.
Domain-Tailored, 100% Customizable HMI
Unlike fixed dashboards, Tethra offers a fully customizable HMI framework where every screen, widget, and workflow can be crafted to match your domain, users, and operational requirements. UX, data, and control logic are all driven from the digital twin and MQTT layer, so every button, trend, alarm, and procedure is context-aware, role-specific, and aligned with how your plant, fleet, or building actually runs.
Current Capabilities
Unified MQTT data ingestion and routing from sensors, control systems, and edge devices into a central digital-twin data model.
Real-time monitoring and analytics dashboards for asset health, anomalies, and process performance across industrial and IoT environments.
100% customizable HMI framework for building domain-specific, role-aware interfaces on top of digital twins and live MQTT data.
Drag-and-drop HMI designer for creating custom dashboards, process mimics, and control panels mapped to real-time topics and twin states.
Role-based views for operators, engineers, supervisors, and data teams, each with tailored layouts, permissions, and alert policies.
Support for domain-specific widgets such as P\&ID-style graphics, maps, 3D views, trend charts, and forms bound to real-time and historical data.
Scalable time-series data warehousing optimized for high-volume IIoT streams, historical replay, and simulation workloads.
First-class CLI utilities for ML scientists to register models, trigger validations, and promote versions without leaving their workflow.
Integrated model registry with versioning, lineage, and metadata for full traceability from experiment to production deployment.
Automated model validation pipelines that run tests on fresh data before rollouts, with support for staging, canary, and blue–green deployments.
Role-based access and governance controls to manage who can publish data, change twin configurations, or update production models.
Future Roadmap
Low-code twin modeling studio to define asset templates, hierarchies, and KPI semantics visually, backed by MQTT topic conventions.
Native support for Sparkplug/B and advanced MQTT semantics (namespace, state management) for plug-and-play industrial interoperability.
HMI template library for specific industries (FMCG, pharma, discrete manufacturing, smart buildings, energy) to accelerate deployment.
Adaptive HMI layouts that automatically optimize for control rooms, tablets, and mobile devices while preserving operator context and safety.
Built-in UX patterns for alarm rationalization, root-cause drill-downs, and guided procedures that reduce operator error.
Auto-retraining and continuous learning loops triggered by production drift, with registry-aware alerts and approvals.
Built-in A/B testing and shadow deployment for models, with automatic feedback of performance metrics into the registry.
Scenario simulation and “what-if” experimentation on digital twins for capacity planning, energy optimization, and reliability analysis.
Connectors to popular BI, CMMS, MES, and ERP tools to extend twin insights into operations, maintenance, and finance workflows.
Tethra bridges OT and IT by turning MQTT topics, sensor feeds, and enterprise data into living digital twins that update in real time. Organizations gain a centralized operational view and can safely embed machine learning into their assets and processes without stitching together multiple point solutions.
Service Name
The platform treats ML scientists as first-class users, providing opinionated CLIs, registries, and validation workflows that align with modern MLOps best practices. This lets teams iterate rapidly on models, ship changes on the fly, and maintain governance, observability, and reproducibility at scale.
Why Us?
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(Admin Office)
8-1-97, Level-2, Ninithaas Highs, Main Road, Peda Waltair, Visakhapatnam-530017
(Innovation Studio)
Meity Nasscom Center of Excellence to for IoT & AI,
Andhra University North Campus,
Visakhapatnam - 530003 India
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