FraudGuard (Real-time Anomaly Detection)

Problem: Financial institutions are

increasingly targeted by sophisticated fraud attempts in digital transactions.

Existing rule-based systems are often reactive, generate high false positives,

and struggle to adapt to new fraud patterns, resulting in significant financial losses.

Challenge: Develop a real-time AI-powered

solution that detects and flags suspicious transactions with high accuracy,

minimizes false positives, and provides actionable insights to fraud analysts.

FinBot Connect

Problem: Customers frequently experience long wait times or difficulty in obtaining specific information through traditional banking support channels. Banks aim to provide 24/7 personalized

Challenge: Design and implement an AI-powered conversational agent (chatbot/voice bot) that can efficiently handle common customer inquiries, offer personalized financial

Insurance - Smart Claims Processing

Problem: Manual Auto insurance claims processing is often slow, labor-intensive, prone to errors, and vulnerable to fraud. This leads to customer dissatisfaction, increased operational costs, and delayed settlements.

Challenge: Develop an AI-driven solution to automate and expedite various stages of the insurance claims lifecycle, from First Notice of Loss (FNOL) to settlement. The solution should include intelligent document processing, fraud detection, and automated routing/triage of claims.

Insurance - Customer Value Enhancement

Problem: Understanding and maximizing customer lifetime value (CLV) is challenging for insurance companies due to fragmented data and a lack of predictive insights. This impacts targeted marketing, retention strategies, and profitability.

Challenge: Develop an AI solution to analyze diverse customer data (policy history, interactions, demographics, external data) to predict customer churn, identify high-value segments, and recommend personalized engagement strategies to enhance customer loyalty and maximize CLV.

Compliance & Document Intelligence

Problem: Manual review of regulatory circulars (for example issued by RBI and others) is slow and error-prone

Challenge: Parsing native and scanned PDFs, extracting obligations, finding action items and impact areas relevant to the financial organization, ensuring compliance.

GreenCraft

Problem: Manufacturers are under increasing pressure to reduce their environmental footprint, optimize energy consumption, and minimize waste across their production processes, but identifying precise areas for improvement is complex.

Challenge: Develop an AI solution that monitors energy consumption, waste generation, and resource utilization in real-time within manufacturing processes, identifies inefficiencies, and recommends actionable strategies for reducing resource usage and carbon emissions.

Worker Safety & Efficiency

Problem: Ensuring worker safety on the factory floor is a constant challenge, especially in hazardous environments. Additionally, optimizing human-machine collaboration can significantly improve operational efficiency.

Challenge: Build an AI-powered system to enhance worker safety by detecting hazardous situations (e.g., un-worn PPE, unauthorized zone entry, fatigue) using computer vision and IoT sensors. Simultaneously, optimize human-robot/machine collaboration for improved efficiency using real-time data analytics.

AI HR Buddy

Problem: Employees feel disconnected in remote setups.

Challenge: Create an agent that tracks employee sentiment via conversations/emails (privately), nudges for wellness breaks, suggests policy clarifications, and books leave—autonomously.

API Governance & Modernization

Problem: Manual API discovery, documentation, and scoring is inefficient

Challenge: Extracting APIs across systems, ensuring accuracy, scalability, and adaptive learning.