Breakdown Siloes of information

Get the best insights from the most comprehensive and reliable energy materials data, speed up device integration, and make every development step matter.

An Environment For Coupling R&D Data

The core power of ViMi is a tailored, cognitive-driven, big data technology, all built in a cloud-based software platform. It offers a unique domain specific knowledge with the goal of integrating data flows from various sources, all in one solution.

Materials Recommendation

AI and deep learning are utilized to gather technical and commercial information of materials from open and proprietary databases, comparing these materials data with commercial targets, and employing analytics on user defined applications to provide un-
biased ranking and recommendations on the most viable materials.

Actionable Visualization

We simplify scientific process automation and workflow management by seamlessly importing datasets from multiple sources and exporting results for presentations, decision making or further processing.

A Secure Data Network

We collect and curate data from in-house and 3rd party sources in real-time.
Our platform utilizes advances in M2M communication, machine learning, advances in graph and non-relational databases, and highly secured data management protocols.

Image Processing for Empowering Visual Assets

The imaging processing module will take in images as input and extract information from it by classifying and segmenting different aspects of the image to provide visualization of the data.

Data Discovery and Enrichment

Inter-operable Data Management

Fully automated data acquisition and processing from instruments, wherever they are located.

Automated Data Extraction

Extracting technical and commercial data from text, figures, and tables using ML / DL.

Single Search Across All R&D Data Assets

Smart search and data extraction using comprehensive provenance, meta-data tracking and associated data discovery.

Decentralizing Data Management

Lab scientists can access structured data for project-level analysis, while data scientists can conduct ad-hoc analysis of data within and across projects