About 95% of Indian IT leaders believe that generative AI will soon have a prominent role in their organisations, according to a Salesforce report released earlier this year. Indeed, the Indian IT sector has been quick to leverage the technology and is upskilling its workforce for a generative AI-powered future.
At Nagarro, generative AI has opened new opportunities allowing it to improve its offerings and deliver more value to its clients, according to Anurag Sahay, managing director – AI and data science, Nagarro. Founded in 1996, the global technology consulting and digital product engineering company made €856.3 million in 2022, up from €546.0 million in 2021 — a growth of 56.8%.
“We tailor generative AI tools to meet our clients’ specific needs, enhancing their data value and optimising their use of this technology,” Sahay told AIM.
With a workforce of around 19,000 and operations in 36 countries, the company’s expertise and services span areas such as digital product engineering, technology consulting, AI/ML, IoT, API management, and cloud services, among others.
Making a difference with generative AI
“We’ve integrated large language models (LLM) with enterprise knowledge bases. This makes LLMs more useful, their answers more grounded and the experience is more consistent in general. Grounding LLMs with specific information has resulted in a wide variety of contextual use cases getting enabled in the enterprise,” Sahay said.
Nagarro also has enhanced its AI-based chatbots, utilising extensive organisational knowledge for more profound and precise interactions and solutions. The integration of an LLM’s conversational skills with specific knowledge and context contributes to a more natural and enjoyable chatbot experience.
Moreover, Sahay said the company is developing interfaces that understand natural language, making our software easier and more intuitive to use, streamlining tasks and improving efficiency. “We believe that natural language is going to be one of the more powerful interfaces for all enterprise applications.”
Integration with Genome and Forcastra
The company integrates various LLMs such as the GPT models, DALL-E and Whisper, among others into its Genome and Forcastra AI platforms. While Nagarro developed Genome AI to revolutionise customer experience, Forcastra AI focuses on intelligent forecasting and informed decision-making.
The Genome AI platform serves as a transformative tool for brands and enterprises, leveraging LLMs, knowledge graphs, and foundational AI models to facilitate playbook-based automation across the ecosystem of suppliers, partners, and customers.
“The platform’s capabilities extend to generating advanced AI-based recommendations for products, customers, offers, promotions, and AI-based demand planning,” he said.
Moreover, Genome ensures interoperability with a diverse array of foundational AI models and effortlessly integrates with managed AI services specific to hyperscalers. “The foundational LLM models, integrated across hyperscalers like Azure, and Google further amplify its versatility to enhance forecasting accuracy.”
Forcastra AI, on the other hand, employs intelligent segmentation, feature engineering, and a funnel-based approach, encompassing a variety of models such as multi-SKU neural networks, machine learning models, hierarchical, zero-inflated, etc, Sahay said.
“Notably, Forcastra AI goes beyond traditional structured data inputs by harnessing the power of LLMs. This integration allows it to incorporate real-world signals from unstructured datasets, including product descriptions, reviews, social mentions, images, and more.
In addition, Forcastra AI utilises LLMs to craft a natural language conversation interface and conversational nudges enhancing the user experience, making the forecasting process more approachable and user-friendly.
“The combined utilisation of LLMs, and other models in both Genome and Forcastra AI reflects Nagarro’s commitment to delivering sophisticated and comprehensive solutions in the realm of AI and data science,” Sahay added.
Ginger now provides more sophisticated and context-aware responses
Nagarro developed Ginger as the de facto interface for its employees to interact with the organisation and its systems. Today, Ginger AI is an integral part of Nagarro’s Fluidic Enterprise AI suite, which helps redefine workplace dynamics by enhancing responsiveness, efficiency, intimacy, creativity, and sustainability.
For instance, platform users can directly retrieve services and data, expediting information access. Additionally, personalised data nudges guide employees toward data-centric decisions, surpassing conventional decision-making approaches.
Now, generative AI integration has resulted in enhanced conversational abilities of chatbots and other AI interfaces. “As the friendly face of the company for every employee, Ginger serves as an all-encompassing workplace assistant, streamlining information access and daily task management. These AI systems can now provide more sophisticated and context-aware responses, resulting in improved customer interactions and support.”
Challenges with generative AI adoption
The integration of generative AI also comes with its own set of challenges from data availability, hardware cost and compute power. Sahay believes for companies, particularly in the field of AI services, integrating responsible AI becomes a key factor in setting themselves apart.
“At Nagarro, this is particularly crucial as we aim to distinguish ourselves. The primary challenge encountered by Nagarro in generative AI is protecting sensitive enterprise data when using generative AI models. Proper security measures must be in place to safeguard confidential information,” he said.
Compliance with copyright, privacy laws, and licensing agreements related to the use of LLMs is also essential for Nagarro. “We must ensure that it operates within legal boundaries. Moreover, ensuring responsible and ethical AI use is a challenge. This includes addressing issues like hallucinations, misinformation, and bias in AI-generated content.
“Nagarro is actively addressing these challenges by implementing appropriate security measures, complying with legal requirements, and adopting responsible AI practices to mitigate the risks associated with generative AI adoption.
“While we strive for cutting-edge AI technology, incorporating responsible AI design principles is not just a trend but a long-term strategy for building robust and resilient AI solutions. It’s a nuanced approach that goes beyond technical excellence and considers the ethical, interpretative, and human aspects of AI, providing a comprehensive framework for responsible AI development,” he concluded.
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