Transform your data into
visual insights without code
Powerful explainable AI visualizations for intuitive data understanding
Empowering data decision
makers across industries
From seasoned data analysts to curious enthusiasts, Nallai is for everyone
Visualizations that actually help
We use iSOM (Interpretable Self Organising Maps) technique for our visualizations
Built for Complex Data
Capable of effectively handling large datasets with 10s of dimensions and 1,000,000s of data points.
Human-Interpretable
Unlike other visualization techniques, our visualizations avoid self folding and intersections. Insights "pop out" clearly.
Preserved Relationships
Intrinsic relationships between data points is preserved in the visualizations with similar observations placed together.
Independent Variable Maps
See how each of your variables change across a 2D map and reveal patterns that were previously hidden in your numbers.
Customisable Model
Tailor the AI visualization model to your specific analytical needs by tweaking the preferences for best results.
AI explanations
Coming Soon
Take the guesswork out of your data. AI will help you in understanding the visualization to speed up your process.
Variables
13
Instances
247,876
Status
Variables
8
Instances
132,153
Status
What makes us different
We use Interpretable Self Organising Maps (iSOM) for our visualisations
t-SNE
t-Distributed Stochastic Neighbor Embedding
Preserves Data Topology
Prevents Folds/Intersections
Trade-off Understanding
Maps Input Space to Output
Effective in High Dimensions
Computational Efficiency
RadViz Plot
Radial Visualisation
Preserves Data Topology
Prevents Folds/Intersections
Trade-off Understanding
Maps Input Space to Output
Effective in High Dimensions
Computational Efficiency
cSOM
Conventional Self Organising Maps
Preserves Data Topology
Prevents Folds/Intersections
Trade-off Understanding
Maps Input Space to Output
Effective in High Dimensions
Computational Efficiency
iSOM
Interpretable Self Organising Maps
Preserves Data Topology
Prevents Folds/Intersections
Trade-off Understanding
Maps Input Space to Output
Effective in High Dimensions
Computational Efficiency
Backed by cutting-edge research
The fundamental idea has been published in several journal papers
Meet the visionaries
Driven by passion, led by experts
Palaniappan Ramu
Co-Founder
Dr. Palaniappan Ramu is a Professor at Indian Institute of Technology (IIT), Madras with over 20 years of research experience working on data driven approaches on a variety of domains. He is passionate about visualizing data, converting data into insights and drive intuitive decision making. He is the founder of interpretable Self Organizing Maps (iSOM). He has published several journal publications and patents.
Kannappan Raja
Co-Founder
Raja Kannappan is an Entrepreneur with more than 25 years of overall experience, with 16 years in US and 9 years in India. He is the Founder and CEO of DivIniSoft, which is the company behind Nallai. He is passionate about building software products and is responsible for bringing Nallai to market. This product Nallai is a joint venture between Dr. Palaniappan Ramu (Professor at Indian Institute of Technology, Madras) and DivIniSoft.
Aravind Senthil Vinayagam
Co-Founder
Aravind Senthil Vinayagam is responsible for Nallai technology leadership and innovation. He has more than 25 years of overall experience, both in US and India. He is the Co-Founder and CTO of DivIniSoft, which is the company behind Nallai. This product Nallai is a joint venture between Dr. Palaniappan Ramu (Professor at Indian Institute of Technology, Madras) and DivIniSoft.
Demos to get you started
All the support you need to create and understand your visualisations
Car Side-impact
Interaction among the car dimension and overall
Variables
7
Instances
1000
Application
Automotive Industry
Shark Tank India
Investments made by various sharks
Variables
3
Instances
117
Application
Finance Industry
Demos
Demos help you experience the power of data visualization with real world case studies
Ready to visualize your data?
Frequently Asked Questions
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