Mindsprint, a provider of purpose-built industry-first digital solutions and services headquartered in Singapore, is set to launch its new generative AI platform called MindVerse.
It is a purpose-built platform that includes over 10 generative AI solutions such as data intelligence, document interactor, intelligent chatbots, customer feedback analytics, recommendation engines, language translation, and content generation.
According to Sagar Porayil Vadakkinakathu, chief technology officer at Mindsprint, the idea of MindVerse was conceived about 18 months ago, right after ChatGPT took the whole world by storm.
“As we experimented and built various proofs of concept, we discovered that many elements were reusable across different solutions. This realisation led us to the idea of creating a platform that leverages these reusable components to accelerate the delivery process and streamline development,” Vadakkinakathu told AIM.
The company, which spun out of Olam Group, a $36 billion food and agri giant, developed MindVerse as an external-facing platform to allow customers to explore demos and try out different generative AI solutions independently.
Solving Business Problems with MindVerse
Under the MindVerse platform, sits yet another platform called Mercury, where, according to Vadakkinakathu, all the magic happens. It leverages different Large Language Models (LLMs), allows customers to cluster processes, to vectorize documents, and extract analytics or insights from them.
“Because of our parent company, we have a significant presence in the agri sector. MindVerse is leveraged by F&B companies to derive procurement strategies.
“If you’re part of the procurement team and need to create a purchase order with multiple suppliers, you often have to negotiate without full visibility, relying on trial and error. Our solution, however, provides valuable insights based on historical trend analysis and market studies, enabling procurement professionals to make well-informed, strategic decisions,” Vadakkinakathu said.
While the platform uses Machine Learning models to make sense of historical data, the LLMs help the procurement officer with the negotiation pitch.
“The negotiation pitch should be backed by quantifiable data. If I have access to this data in a conversational format, I can effectively use it as a powerful tool during negotiations,” he pointed out.
The platform also chooses among multiple LLMs depending on the use case. For instance, “For some use case if Google T5 works best, we leverage that and for some other use case we might be leveraging the LLama Models.”
Besides agriculture, Mindsprint also operates in the life science, manufacturing and retail industries. MindVerse has solutions for sales, marketing, HR functions for large enterprises It provides inventory forecasting solutions for customers in the manufacturing space.
Building Chatbots
As part of the MindVerse platform, customers also have the ability to build highly intelligent chatbots. These chatbots support structured data querying where business users can get results from Data Lake/Data marts without them needing to write SQL or report to visualise the data.
“We developed a chatbot for spend analytics that streamlines data access from data lakes like Snowflake. Traditionally, obtaining data from Snowflake could take weeks due to the request and BI dashboard creation process. Our chatbot enables users to directly query and extract data from data lakes/marts instantly,” Vadakkinakathu said.
With MindVerse, customers will be able to develop similar chatbots which provide instant, user-friendly access to data and streamline querying processes, thereby enhancing efficiency and decision-making.
Building Local LLMs
While LLMs like OpenAI’s GPT-4, or LLama 3.1 405 billion are significantly large models trained on big datasets. However, enterprises find smaller language models which can be trained on their enterprise data more useful.
Given that is the direction the industry is heading towards, Vadakkinakathu also revealed that the company is in the process of training a small model on one of its customer’s enterprise data.
“We’re in the process of training a local LLM using one of our customer’s datasets. This approach promises to be a game changer, surpassing the limitations of Retrieval-Augmented Generation (RAG) and other methods,” he continued, “We have the necessary infrastructure, including GPUs, and customer agreements on specific datasets. Currently, we’re focused on building the data pipeline, a challenging process that involves running multiple models to neutralise and balance the datasets.”
About Mindsprint
MindSprint, previously known as Olam Technology and Business Services (OTBS), was Olam’s IT division. However, now it operates as an independent company and caters many large enterprises among the sectors it operates in. It has presence in Singapore, US, India and the UK.
It counts Olam– which operates in 60 countries and supplies food and industrial raw materials to over 20,900 customers worldwide, placing them among the world’s largest suppliers of cocoa beans, coffee, cotton and rice–as one of its customers.
Its customers list includes other heavyweights such as Nestle and Mondelez International. In India, Mindsprint has offices in Bangalore and Chennai. According to Vadakkinakathu, much of the Research and Development (R&D) work done by the company also happens in India.
