Wipro's Self-Improving Customer Service Agent is an AWS-native AI solution that continuously learns from customer interactions to enhance support quality. Built on AWS Bedrock and Connect, it analyzes conversations and feedback to identify service gaps, automate workflows, and deliver measurable CX improvements—reducing response times while increasing customer satisfaction.
Agentic Unit Test Case Generation offer autonomous unit test generation with intelligent test case creation and predictive coverage. These agents achieve 80-90% statement coverage across various programming languages and function coverage through intelligent scenario mapping. The platform utilizes Bedrock LLM models to optimize test complexity and ensures automated test case maintenance and evolution. Currently, it supports Python, Java, JavaScript and TypeScript.
Wipro's Face Emotion Agent is an AWS-native AI solution that analyzes customer facial expressions in real-time to assess product feedback and engagement. Built on Amazon Rekognition and SageMaker, it identifies emotions like happiness, sadness, anger, and surprise with 95%+ accuracy—enabling brands to enhance customer experience through actionable emotion-driven insights via RESTful APIs.
Wipro’s Credit Risk Assessment Agent is an AI-powered solution that automates mortgage loan decision-making by analyzing customer profiles, property data, credit scores, and bank policies. Using multi-source data and intelligent scoring, it delivers real-time risk evaluations to help financial institutions approve or reject loans with confidence and compliance.
Advanced AI agent that detects fake reviews using AWS Bedrock and Comprehend. Provides real-time analysis, sentiment detection, and authenticity scoring for e-commerce platforms with integrated demo shop.
An application developed on AWS SageMaker and utilizing the Intel Xeon AMX processor, which enables customers to inquire about loan-related frequently asked questions, submit loan applications (including document uploads), and allows bank personnel to review, verify, and either approve or reject loan applications.
AI-powered agent that generates comprehensive user stories with acceptance criteria, non-functional requirements, and detailed sub-tasks for Frontend, Backend, and Database implementations from technical and functional requirement documents.
Wipro's Inventory Optimization AI Agent streamlines retail supply chain operations through autonomous inventory planning, demand forecasting, and risk detection. Built on AWS Forecast and Bedrock with multi-agent orchestration, it delivers real-time analytics and enterprise dashboards to reduce inventory costs by up to 25% while maintaining 95%+ service levels and eliminating stockouts.
Market Campaign Buddy is an application powered by AI, developed using AWS SageMaker and the Intel Xeon processor, designed to automate the generation of customized social media content. It allows marketers and brands to create posts tailored to specific platforms (including text and images) for various collateral such as Instagram, Facebook, LinkedIn, and Email, utilizing sophisticated open-source AI models. The application features a smooth, interactive user interface for input, content creation, review, regeneration, and downloading.
Ask questions in plain English, get business-ready insights. Talk2Data translates natural language into governed SQL workflows using a multi-agent AI pipeline built on AWS Bedrock and Agent core.
The product provides an intelligent agent capable of end-to-end incident management across Kubernetes, AWS. It can automatically investigate issues, perform deep log analysis using monitoring tools, identify root causes, and execute remediation actions. The agent also summarizes findings in a clear, actionable format and can seamlessly create incident tickets in ITSM platforms. Additionally, it integrates with GitHub MCP to review configuration and manifest files, suggest fixes, and automatically generate pull requests, enabling faster resolution and continuous operational improvement.
Wipro’s QET GenAI Accelerator augments quality engineering productivity. From user stories, test scenarios, test cases, test automation scripts and test data can be created in an automated manner using Agentic AI. It can also create data mapping tables from design documents and complex SQL queries from the data mapping tables with just some mere clicks of a button. The Accelerator adds immense value in each phase of the Software Testing Life Cycle by creating all the Test Deliverables in an automated manner and thus reducing manual efforts, increasing productivity and decreasing time to market.
