• Busigence Research

    Building the art of science

    We solve real complex problems through data, design & technology

Research Areas

We choose to work in areas through which we can directly impact thousands of souls everyday

Research with a purpose

The Approach

Every problem is solved on a framework build over three core pillars: Design :: Research :: Implementation

The problem being conceived & conceptualised, is formulated by Design team. Fomulation is validated and better formulated or at times even re-formulated by Research team. Design team takes up and shape the solution meeting ultimate goal and objectives. Implementation team replicates the fabricated solution through best of technology stacks and achitectures optimizing the process and making application scalable, extensible, and usable

Research is not a skill. It's a mindset

Work at Glance

What we have been digging day & night, repeat

Clustering images with Neural Networks

Hybrid of convolutional neural networks and auto-encoder for iformation reduction from image based data to pre-process it followed by applying K-means clustering on reduced features

Speaker diarization using Deep Learning embeddings

Speaker diarization is the task of grouping segments of speech according to the speaker. Replaced this two-step genrative process with a discriminatively trained deep neural networks(DNN) that joinly learns a fixed-dimentional embedding and scoring metric

Intention to purchase through text analysis

Selecting features for designing an efficient PI detection and classification model. We note that a PI text has two major components - Prescence of consumption intent, and Prescense of corresponding consumable object

Outlier Detection in High Dimention Data

Detecting outliers without saying why they are outliers is not very useful in high -D due to many features (or dimentions) are involved in high dimentional data

Missing Value Imputation in Imbalanced Dataset

Class imbalance is major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the numbre of instances in the minority class


That's it for now

We are on a mission

We believe data can make this world wiser and prettier


Research is not an achievement. It's a requirement

Reach us

We prefer e-mail over phone to clarify your queries

    • (+91) 8824 121 121
    • research@busigence.com
    • Researcher never sleeps
    • Chicago | Bengaluru