Mindtree exclusively launched the Canvas AI platform, a Artificial intelligencegenerative AI designed to help companies responsibly build, manage, and harness the power of generative AI.
Among the promises of the platform is the significant reduction in the time required for cloud migration and application modernization, reaching up to 50% optimizations.
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Available through the company's generative AI studios, the platform addresses critical AI issues such as compliance regulatory data and the reduction of bias, while providing transparency and observability for all solutions generated.
Meanwhile, Mindtree is already in talks with 75 customers for Canvas AI implementation, of which around 12-13 are already in use.
The platform allows customers to have access to various partnerships and Artificial Intelligence tools belonging to Mindtree.
It already has integrations with 12 LLMs (Large Language Models, an advanced language model based on a neural network with a large amount of parameters), including services like Azure OpenAI and Hugging Face.
Nachiket Despande, COO of Mindtree, emphasized the platform's capabilities, mentioning: “We also integrate Open source LLMs for specific use cases in different domains, and that number only tends to increase. For example, we are working on an integration for AutoCAD (design software) to simplify engineering use cases.”
Despende also highlighted the efficiency gains that organizations can achieve by adopting Canvas AI, stating that in modernizing technology, platform restructuring and cloud adoption, we can see a 50% to 60% reduction in efforts – both both in terms of time and costs, since delivery times for products and services are also reduced.
When used in marketing campaign and other projects, we can also see a productivity increase of up to 70% due to reduced efforts.
In addition to this big news, Mindtree recently announced that it plans to invest between $40 million and $50 million to enhance its Artificial Intelligence capabilities in the next fiscal year.
These investments are aimed at expanding productivity-driven use cases for generative AI, as well as other enterprise uses such as forecasting and lead generation.