Mphasis has unveiled NeoIP, an AI platform that integrates multiple Mphasis AI solutions to enable ongoing enterprise transformation and maintain a competitive edge.
The platform continuously updates core business systems by leveraging enterprise knowledge from legacy data and operations to drive intelligent engineering, according to the company.
At its foundation, NeoIP establishes a dynamic layer connecting data, systems, and processes, helping businesses proactively optimize, modernize, and transform operations.
It is designed to assist enterprises struggling with fragmented legacy systems and siloed data, which often hinder innovation and slow continuous transformation efforts.
“Traditional enterprise transformation frequently fails to deliver lasting value and requires repeated investments to keep up with AI’s rapid evolution,” said Nitin Rakesh, CEO and MD of Mphasis.
He added that NeoIP changes this approach by uniting Mphasis-built AI solutions, partner technologies, and client assets into a single platform capable of supporting multiple data sources, large and small language models, and various computing environments.
“NeoIP represents the future of enterprise technology—intelligent by design, secure by default, and scalable by nature,” Rakesh noted.
A core component of NeoIP, Ontosphere, works with various AI agents to construct and maintain intelligence using dynamic knowledge graphs based on enterprise-domain context.
NeoIP also integrates seamlessly with third-party AI agents through standards like Model Context Protocol (MCP) and Agent2Agent (A2A), broadening the network of AI agents for unified, cross-enterprise orchestration.
The platform enables organizations to evolve continuously rather than relying on one-time transformation programs, making enterprise knowledge more accessible and actionable.
It automates complex decision-making, predicts and prevents potential issues, and drives sustained innovation across operations.
NeoIP empowers CIOs and business leaders to incorporate intelligence early in the software and operations lifecycle, creating self-healing, resource-efficient systems that continuously learn and improve.
Mphasis said the platform combines ongoing business intelligence with AI-assisted implementation, fostering continuous learning and evolution with each new initiative.
Early client use cases have shown measurable impact, including up to 60% improvement in development and modernization efficiency through AI-driven code generation, testing, and optimization.
Also Read:
Google Adds ‘Vibe Coding’ to AI Studio with Gemini
Prosus and Accel partner to fund Indian AI and deep tech startups
