KPMG in India and Vianai Systems Announce Alliance to Bring AI-based Conversational Finance to CFOs with Vianai’s hila™ Enterprise
KPMG in India and Vianai Systems announced an alliance to put reliable, conversational AI directly into the hands of finance users with Vianai’s hila Enterprise.
The innovative joint offering by KPMG in India and Vianai Systems will leverage Vianai’s hila Enterprise fine-tuning and optimisation capabilities, with enterprise systems of record. Targeted specifically at finance professionals, conversational finance will allow finance users to ask any question against their systems of record, internal documents, public data, and other data in natural language, with a view to get immediate responses by way of text, dashboards, charts and more.
Today the adoption of Artificial Intelligence (AI), and the use of Large Language Models (LLMs) in enterprises has largely been a non-starter for business users that require an extremely high degree of accuracy and reliability, in particular those in finance roles. Issues of LLM hallucinations and inaccuracies are simply too risky for business-critical functions such as finance to adopt AI.
As a result, finance teams have not been able to take advantage of the latest AI advancements and instead many still rely heavily on Information Technology (IT), analysts, visualisation tools, experts, and others to find answers to their questions.
The alliance would see both KPMG in India and Vianai Systems leveraging their respective expertise and resources together to provide this innovative Conversational Finance offering to enterprises looking to transform their finance function.
“Finance functions in enterprises today have an unparalleled need for accuracy, privacy, and security as well as relevance to the context of business. Leveraging the power of LLMs with internal data sources, transaction systems, documents, and other data, as well as public information, would allow enterprises to paint a transparent and accurate picture of their business, and industry. These LLMs can also help finance functions play a critical role in ensuring resilience towards a challenging risk and regulatory environment.
Moreover, while independent SaaS (ERP, CRM, HRMS) companies are geared to launch their own co-pilots, they will fall short in desired value creation, specifically when cross functional insights are required. Large generic models are not able to deliver this today, and we are excited for Vianai Systems and KPMG in India to finally unlock this value for the finance functions of enterprises today,” said Sachin Arora, Partner and Head, KPMG Lighthouse (Analytics, AI and Data), KPMG in India.
“Delivering the power of AI into the hands of finance users is a foundational step toward making AI available to all business-critical functions. Finance departments and teams must have transparency in their work and deliver transparency to the outside world – with speed. We are thrilled to be working closely with KPMG to make this a reality for every organisation in every industry,” said Dr. Sanjay Rajagopalan, Chief Design & Strategy Officer, Vianai Systems.
How hila Enterprise works: business-relevant generative AI, in context, for enterprise CFOs:
- hila Enterprise leverages a full suite of AI tools, techniques and technologies including Vianai’s ero Hallucination™ technology, veryLLM verification capabilities, fine-tuning and optimisation techniques, and more, making LLMs safer, more reliable and context-relevant for business users.
- The system leverages large public models, open-source language models, Vianai-built models as well as the customer’s own models to enable querying of large amounts of data in the context of the business.
- Customers can query any transactional system within their enterprise landscape to get maximum insights via natural language interaction with systems in real-time.
- Business users get a response generated from public and private business data, in real-time, in the form of the user’s choosing, whether as a dashboard, chart, text or other response format.
