Tech Mahindra and IBM transform document processing with speed, accuracy and governance
Tech Mahindra, a technology consulting and digital solutions provider, realized their clients were drowning in information hidden within massive, unstructured documents. These documents, including financial reports, contracts and onboarding forms, often span over a hundred pages and combine text, complex tables and images. Extracting value from this data is slow, manual and error-prone, with every variation in format or layout adding new layers of complexity.
Tech Mahindra knew automating these processes was key, but they recognized that the real opportunity was intelligence. By applying generative AI (gen AI) to understand and reason over complex document structures, their clients could accelerate decision-making and unlock new efficiencies. Yet, many organizations remained cautious, concerned about governance, transparency and control. Addressing this dual challenge—AI capability and trust—became the foundation for Tech Mahindra’s collaboration with IBM.
In collaboration with IBM, Tech Mahindra created Document AmplifAler—a next-generation gen AI platform designed to transform how enterprises manage and understand complex documents. Built with IBM® watsonx.ai® AI studio, IBM Granite® 13b foundation models and IBM Deep Search, Document AmplifAler uses multi-agent, multimodel AI to extract, analyze and enrich insights from massive, data-heavy documents that combine text, tables and images.
What sets the Document AmplifAler platform apart is its foundation of responsible AI. With the IBM watsonx.governance® toolkit for AI governance embedded at the core, Document AmplifAler helps ensure every AI-driven process is transparent, explainable and compliant—addressing enterprise concerns around trust and accountability.
With Document AmplifAler, built with IBM watsonx® portfolio of products, Tech Mahindra is helping enterprises turn document-heavy workflows into intelligent, automated processes—driving efficiency, accuracy and speed. By combining advanced AI with embedded governance, their clients are realizing faster outcomes while maintaining full control and compliance.
Tech Mahindra’s clients have seen tangible business results across industries:
By integrating watsonx.governance, every AI output in Document AmplifAler is transparent and explainable, empowering these enterprises to adopt AI confidently, meet regulatory expectations and scale innovation responsibly. Together, Tech Mahindra and IBM are helping Tech Mahindra’s clients unlock the full potential of trusted AI to transform how business gets done.
From corporate client onboarding to contract review and invoice reconciliation, Document AmplifAler is helping their clients across industries accelerate decisions, boost operational efficiency and uncover hidden insights in their data. Tech Mahindra and IBM have built more than just a solution—they’ve established a joint AI center of excellence (CoE), trained more than 500 practitioners and are continuously advancing enterprise-grade AI adoption that’s scalable, secured and future-ready.
Tech Mahindra offers technology consulting and digital solutions to global enterprises across industries, enabling transformative scale at unparalleled speed. They have more than 148,000 professionals across over 90 countries, serving more than 1,100 clients worldwide. The company offers a full spectrum of services, including consulting, information technology, enterprise applications, business process services, engineering services, network services, customer experience and design, AI and analytics, and cloud and infrastructure services. They’re the first Indian company in the world to have been awarded the Sustainable Markets Initiative’s Terra Carta Seal, which recognizes global companies that are actively leading the charge to create a climate and nature-positive future. Tech Mahindra is part of the Mahindra Group, founded in 1945, one of the largest and most admired multinational federation of companies.
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.