WNS AI Lab joins all the pieces of the AI puzzle to moveswiftly and decisively from the exploratory to the demo stage.

Our AI, data and domain experts collaborate to translate the latest innovations and breakthroughs in deep learning and machine learning into cutting-edge AI and Gen AI use cases. We integrate advanced methodologies such as self-supervised learning, transfer learning and real-time adaptive algorithms for greater success. With agility as our guiding principle, we employ rapid iteration and experimentation to accelerate AI adoption. This iterative process ensures that our clients stay at the forefront of AI evolution with minimal risk, optimized costs and magnified business value.

Research Focus

The primary objective of WNS AI Lab is to experiment with the latest deep learning architectures, and to test their practical business applications. The lab first focuses on deploying algorithms to help machines process and understand structured and unstructured data and test the feasibility of the solution. A successful experiment then gets converted into a component for the AI utilities hub. This enables decision intelligence through an interconnected and interdependent approach.

 

Research Areas

  • 01Language Hover Arrow Default Arrow
  • 02Generative AI Hover Arrow Default Arrow
  • 03Computer Vision Hover Arrow Default Arrow
  • 04Decision Support Hover Arrow Default Arrow

Implementing AI solutions efficiently and at scale through the latest
advancements in transfer learning and cloud computing.

Tools and Technologies

We embrace a technology-agnostic approach that prioritizes delivering best-fit solutions to address an organization’s unique challenges. Our teams leverage a broad range of tools and platforms to provide flexible, customized applications that meet evolving requirements and drive long-term growth and resilience.

Tech Stack

Amazon Web
Services S3

Jupyterhub

Multi-GPU
Workflows

Collaboration
Tools

Define and Review

Our AI Lab runs on the credo of agile execution, where we undertake new experiments in 3-4 week sprints. The shortlist of experiments is finalized by identifying the latest innovations in AI/ML and Gen AI with the most potential for application, along with a grasp of the critical business challenges our clients face.

Experiment

After the initial feasibility and impact assessment, the experimentation stage commences. Although each project targets a specific real-world business challenge, we design the experiment with a wider reach and application in mind. For instance, while one of our experiments may focus on sentiment classification in consumer data, the underlying algorithms are set up to handle the larger domain of text classification, with multiple business applications such as risk classification from news, emotion detection and spam detection.

This agile experimentation approach enables us to assess the practical value of a spectrum of use cases, zeroing in on the ones that can deliver the maximum impact for our clients.

Prototypes

As a result of the experimentation, several key outputs are generated, including comprehensive experiment notebooks, prototypes and knowledge assets. A successful experiment then gets converted into a component for the AI utilities hub.

Report and Publish

These assets and insights are then either utilized internally, helping us bolster our expertise and deliver bespoke solutions for our clients or are made available externally for the benefit of the wider community.