AI & Machine Learning

We engineer practical data-driven algorithms to power machine intelligence for startups by separating the AI hype from computational realities.

Capabilities

  • Machine Learning & Pattern Recognition

    Building a solution involving machine learning is much more than the model. It is a complex mix of data structures, model training, model integration and architecture. We engage in end-to-end delivery of a machine learning solution tailored to bring product features to life.

  • Natural Language Processing

    There are many NLP APIs and services available today. Some of these services could give 80% accuracy on extraction tasks involving generic data. However, to solve really hard problems involving natural language understanding, especially with proprietary and small data sets, we need to skillfully use machine learning techniques along with traditional NLP algorithms.

  • Computer Vision & Image Processing

    Deep learning techniques have given a fillip to computer vision and image processing solutions. However, training models for proprietary and domain-specific data sets is a challenge. We find innovative ways to transform the domain-specific part of a problem into a generic computational problem in order to deliver practical solutions.

  • Mathematical Optimization

    Optimization algorithms are the foundation of modern-day machine learning. However, there is a rich history dating back to many decades. We strive to use these fundamental algorithms to deliver solutions to problems involving allocation, balancing, routing.

Insights

Technical Paper | September 14, 2018

Learning to Fingerprint the Latent Structure in Question Articulation

In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented.

Blog | February 20, 2018

Data first or algorithm first?

Many startups wish to incorporate Machine Learning (ML) algorithms in their products. But they do not have large data specific to their business. Our research to tackle this dichotomy shows that one must roll out ML solutions incrementally.

Technical Paper | January 5, 2018

Solving a Network of Sensors Problem using Gradient Descent

In this research report, we highlight a problem formulation involving multiple sensors that collectively determine "characteristics" of targets in an environment. We show how the formulation can be solved with Lagrangian relaxation.

Blog | March 21, 2017

Handling Categorical Features in Machine Learning

You can’t fit categorical variables into a regression equation in their raw form in most of the ML Libraries. If it is not included in the modeling, then you do not get an accurate model. It’s crucial to learn the methods of dealing with such variables.

Case Study | September 20, 2015

Rationalizing Real Estate Investment Decisions Using Data Science

Using Analytics for multi parameter investment worthiness of properties and neighborhoods

Meet theExpert

Ravindra
Dr.Ravindra GunturData Scientist
  • Applied mathematical optimization
  • Natural Language Processing
  • Machine Learning & Pattern Recognition
  • Recognition algorithms for Video

Careers

Data Scientist

Using analytical techniques, identify patterns and anomalies in data. Apply collective insights to derive predictive and analytic solutions.

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