Tignis

A low-code machine learning enabled data analytics application
DESIGNED FOR PROCESS AND OPERATIONS ENGINEERS.

Proactive Analytics.
by you. for you.

Industry has spent significant capital on implementing historians to collect and store data. These systems are great for reviewing what happened in the past. It's time to turn those rearview mirrors into forward-looking analysis. PAICe Builder powered by the Tignis Digital Twin Query Language (DTQL) enables you to turn your historian data into intelligent machine learning alarms for your critical equipment.

Using Machine Learning on your Data in 4 Simple Steps!

1- DEFINE

Specify the data to use in
your machine learning
models

2- TRAIN

Build your machine
learning models from
historic data

3- APPLY

Run your machine
learning models
against real data

4- REVIEW

Review the output of
your model and tune it
to your liking

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Draw the Curtain on
Black Box Analytics

PAICe Builder is a machine learning analytics application simple enough for anyone to use. It was created to empower process engineers, operations engineers, and subject matter experts to utilize machine learning. With PAICe Builder, engineers that are responsible for machinery and processes can build, explore and test machine learning until they are fully comfortable with the outcomes. Only then do they deploy their analytics to be monitored live and continuously.
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Fast Setup and
Data Connection
PAICe Builder has out of the box data connectors for OSI-PI. Simply supply your OSI historian server and PAICe Builder does the rest.

If you don’t have OSI-PI, that’s fine too. PAICe Builder can ingest flat files (CSVs) from any data historian or connections can be built with our standard APIs.

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Machine Learning
for Anyone

An intuitive user interface enables anyone to create machine learning models.
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Build Analytics Where
You Want
PAICe Builder can be installed and run on multiple platforms. With downloadable applications for Windows, Mac, and Linux it is perfect for customers with strict data security concerns that don't permit process data to leave their network. For users needing more compute resources than are available in their laptop, it can be utilized in the cloud. The choice is yours, based on your needs.