HCLTech powers 3 source-linked AI deployments documented in AIUseCaseHub, across 3 industries and 3 countries. Documented deployments include AI agents, RAG.
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Use Cases
3
Industries
3
Countries
3
Agent Cases
1
Hyperscaler mix
Filter HCLTech's implementations by cloud provider evidence.
How HCLTech builds AIBuild, Buy & Compose — what they mean
HCLTech partnered to build a GenAI-powered Intelligent Insurance Intake solution leveraging Amazon Textract, Amazon Bedrock Nova Pro Large Language Model, Amazon Bedrock Agents, AWS Lambda, and Amazon DynamoDB to automate processing of complex insurance forms.The solution handles diverse form layouts with a hybrid approach combining structural AI with contextual LLM understanding, achieving about 95% extraction accuracy and up to 20X process time reduction.Deployed for a leading Canadian insurance provider to automate workers' compensation form processing, reducing manual staff time from 60 minutes per form, lowering error rates, and improving customer satisfaction.Serverless architecture enables scaling and flexible workflow configuration with data securely stored and compliant with HIPAA and GDPR regulations.
A leading multinational agriculture producer partnered with HCLTech to address operational inefficiencies caused by siloed data across three major business units. These data bottlenecks led to higher operating costs and slow decision-making across global supply chain, mining, and corporate/regional operations. Leveraging Microsoft Azure AI, Cloud Scale Analytics, Synapse, Azure Data Lakes Gen 2, and Power BI, HCLTech integrated data sources to enable near real-time insights and automation. This AI-powered platform streamlined global operations, improved delivery cycles by up to 25%, and reduced overall operating costs by up to 20%. The integrated solution created a single source of truth, supporting better strategic decisions and scalability in the agriculture sector.
The customer is a leading multinational European pharmaceutical and biotechnology firm headquartered in London, seeking to optimize workflows and improve AI adoption.The company faced siloed data and teams, limited AI adoption especially among less tech-savvy users, scattered unstructured data, and a need to maximize ROI on AI tools like Microsoft Copilot amid economic challenges.