HyperSense AI

Collect, structure, and prepare data in one place and build robust and explainable AI machine learning models at scale using configurable capabilities of HyperSense Data Management Studio, Business Modelling Studio, and AI Studio.


Collect, clean, and structure the data for model training and insight generation using the capabilities of HyperSense Data Management Studio, Business Modelling Studio, and AI Studio. Create features based on business rules and perform complex data transformations and imputations using the auto data preparation capabilities of AI studio. Train supervised and unsupervised machine learning models using drag and drop capabilities.



Integrate multiple data formats from multiple sources

  • Ensure compatibility and support for numerous data formats, including ASCII data, Binary data, CSV, ASN.1, and JSON data.
  • Ingest, integrate, and transform data from multiple sources such as FTP & SFTP (files), RDBMS (databases), Hive, S3, and Kafka (streaming data).
  • Facilitate ETL/ELT transformation operations on the input data, such as profiling, filtering, enriching, removing duplicate data, joining, etc. It is then loaded into one or more data lakes.

Data streaming and transformation

  • Ensure seamless and scalable ingestion of the various data formats in batches, micro-batches, and real-time.
  • Apply business rules to perform feature engineering based on domain knowledge.
  • Identify frauds and anomalies based on business rules and orchestrate insights into dashboards and investigation workflows

Data Preparation and Management

  • Create data pipelines quickly using 100+ data operators built for various operations required in the data lifecycle.
  • Leverage auto data preparation to perform complete data preparation based on best practices within no time.
  • Easily store, catalog, and find data for business consumption. Reduce data preparation time to achieve greater efficiency and productivity.

Build AI Models

  • Build complex and large-scale AI models quickly by leveraging distributed computing.
  • Automate time-consuming data preparation and iterative machine learning lifecycles using Auto data preparation and AUTO Cash operators.
  • Allows data scientists and experts to build ML models in collaboration with higher scale, productivity, and efficiency while sustaining the model quality.
  • Generate auto model report and distribute to stakeholders for review and approvals.

Build Trusted AI models

  • Build models with Explainable AI capabilities. Make right predictions using what-if analysis and identify scenario when predictions change using counter-factual analysis.
  • Identify bias in machine learning models before deployment to mitigate model errors. Get recommendations on model de-biasing in various bias scenarios to build fair AI models.


Ensure Data Integrity
Ensure Data Integrity

Perform data integrity checks across complex enterprise data pipelines for accurate and actionable insights and decision-making in real-time.

Increase Efficiency
Increase Efficiency

Automate data preparation and data science lifecycle tasks through AutoML capabilities and improve productivity and efficiency.

Ensure Transparency
Ensure Transparency

Eliminate bias, ensure transparency in model prediction, and build trust through the AI trust and governance framework.

Improve decision-making
Build sustainable AI models 

Proactively perform bias detection and mitigation to build sustainable and unbiased AI models.

Scale AI experiments

Scale up or down your data science team’s AI experiments depending on your business goals and usage.

Quick and better decisions
Faster ML pipelines

Fast and easy usability to create complex ML pipelines and models with no-code/ low-code capabilities.

Build robust and sustainable AI model for your business