Select or build your own agentic digital workforce - Seamlessly orchestrate the workflow across both modern and legacy software and equipment.
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 unbiased 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