Picominer®
A Lightweight AI/ML Shell
AI applications need not be ultra large scale. We aim to keep it simple with rule-engines.

Coding the business context
The majority (some reports say nearly 60%) of AI/ML projects never make it past the PoC (proof of concept) stage, despite the fact that the data models look promising in the lab.
The data lifecycle is very different from the software development lifecycle. Typically, the cost of deploying AI/ML models is not considered in initial estimates.
The Model-To-Business phase of the AI/ML project lifecycle requires additional processes to adapt the models to the target environment (application context). It serves as the motivation for Picominer's design.
Out of the box
Picominer is an out-of-box solution to extend the capabilities of Edge gateways and Cloud applications. The platform can be integrated to link the collected data to enterprise / customer applications.

MOTIVATION
Binding the expert knowledge for the target application context.
SOLUTION
A lightweight semantic layer, and AI/ML model serving software for the edge data.BENEFITS
Flexible MLOps. Easy integrations with data applications.Salient Features
Connectivity
Data acquisition from field devices with Modbus/TCP, OPC-UA, CAN, MQTT, BACnet, Websockets and many other data sources.
Integrations
Unified data simplifies integrations with a wide range of automation software, MES / ERP / HMI, RPA, Web and Mobile applications.
Implementation
The Picominer platform designs and reference implementation are open source.There is no vendor lock-in.
Benefits that come with our engagement
Reference designs
Valuable reference design, implementation and support for AI applications.
Flexible
Adapt the AI to the needs of the applications. No opinionated framework.
DevOps
A flexible but uncomplicated DevOps foundation for running and scaling up the AI models.
Open source
We have made all of our reference implementations available as open source. No vendor lock-in.
AI R&D
Access to our ongoing research and development on AI/ML sources and reference designs.
Team training
Get the staff a systematic AI training (online or offline, as needed).
Innovation
Our strategy is to empower the teams to innovate in response to market demands.
Standards based
Continuous and incremental process improvement with industry standards and protocols.
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