HyperSense AI
AI in Retail

The buyer’s journey in retail is complex, with multiple touchpoints. Any friction in the shopping experience can lead to customer churn and impact sales. With HyperSense AI, retailers can make accurate ML predictions and generate actionable insights from their data to make better, profitable decisions.

Challenges to AI adoption in Retail

Building AIML prediction models

Building AI/ML prediction models require niche AI and data science skills.

Identifying all contributing factors that

Identifying the factors that lead to specific shopper behavior is not easy.

Real-time analysis to understand fintech data better and mitigate risks, if any

Real-time insights from data help engage and convert customers.

Inherent complexities

Inherent complexities in applying AI/ML algorithms to large retail data sets.


Benefits of HyperSense AI for Retail

Manage Complexities
Improved Efficiency

Manage complex models and create ML pipelines faster through Automated Machine Learning capabilities.

Built for Scale
Improved Scalability

Build and run ML models quickly on large data sets by leveraging distributed computing to solve business use cases.

Increased Productivity _2
Increased Productivity

Faster data-to-insights time improves productivity and helps business users to make better business decisions.

What can
you do with
HyperSense AI?

Drive Retail Conversations with AI Orchestration Platform