Autonomous Control Apps
with effortless data streaming with seamless ML integration with scalable edge deployment with built-in safety guardrails
Build Intelligent Control applications using simple, open tools.
A powerful easy-to-use platform
Uncover the core elements of our platform: fast app development, large-scale edge deployment, and safe autonomous control.
Contextualized Data
Stay in sync with your existing internal systems with a flexible approach for modeling assets and data streams. Import using CSV or programatically through the API.
Development Speed
Simple to use Python SDK to develop and test control apps that can seamless integrate with your machine learning models, rules, or physics-based logic.
Safe Control
Enable autonomous control actions on your edge equipment with real-time guardrails, full audit trail and integration with existing infrastructure.
Seamless Data Streaming
Easily connect and stream data to apps from a wide range of industrial protocols, including OPC UA, ROC, ModBus, MQTT, SQL, and OSI PI.
Offline Operation
Deploy your apps close to your edge equipment for continuous 24/7 operation, free from connectivity concerns. We securely store data and sync it once it's back online.
Flexible Deployment
Kelvin runs on any commercial off-the-shelf hardware, any cloud provider, or on-premises. With Kelvin, you have complete control over your data, ensuring privacy and security.
Supervised Control
Trusted control through app recommendations
Apps provide customized control recommendations that seamlessly integrate with current operational workflows, enabling transparent and secure decision-making for equipment control.
Feedback Loop
Improve app performance with user annotations
How it works
Develop your SmartApp in minutes
Pure Python simplicity — effortlessly stream data into your model and generate control recommendations using simple primitives.
kelvin app create
import asyncio
from kelvin.application import KelvinApp
from kelvin.krn import KRNAsset
from kelvin.message import Recommendation
async def main() -> None:
app = KelvinApp()
await app.connect()
# Receive streaming data from your assets
async for asset_name, df in app.rolling_window(count_size=100).stream():
# Replace with your model predictions
result = model.predict(df)
# And generate Control Recommendations
await app.publish(
Recommendation(
type=result.recommendation,
description=result.description,
resource=KRNAsset(asset_name),
control_changes=[],
)
)
if __name__ == "__main__":
asyncio.run(main())
Deploy at scale with Kelvin Cloud
We handle everything for you — from integrating with your existing infrastructure and streaming contextualized data to apps, to making secure control actions on your edge equipment.
- Full Platform Access
- Tailored Support
- No Credit Card
GitHub
Speed up development with App Templates
Develop in minutes by leveraging the pre-built code samples for common control use cases.
Machine Learning
A Scikit-learn SmartApp Template for showing how to do Machine Learning inference.
Image Recognition
A TensorFlow-based template for performing image recognition.
Multi-Objective Optimization
A template for applying multi-objective optimization techniques to improve efficiency in the paper-making process, balancing multiple objectives simultaneously.
Event Detection
A template for seamless integration of MLFlow with Databricks, enabling efficient deployment of machine learning models.