Mphasis launches unified AI platform for enterprise transformation

• 27 Oct 2025
• TM Team
• 0 Comments

Mphasis announced the launch of a new unified Artificial Intelligence (AI) platform designed to enable continuous enterprise transformation, moving away from traditional one-time modernisation programs.

The new platform, called NeoIP, integrates multiple Mphasis.ai solutions into a single ecosystem, combining data, systems, and processes into what the company describes as a “living, breathing layer” of enterprise intelligence. NeoIP aims to help organisations continuously evolve by embedding AI at the core of business and IT operations, automating decision-making, predicting and preventing system issues, and driving sustained innovation.

“Traditional enterprise transformation often fails to deliver long-term value and requires ongoing reinvestment to keep pace with AI’s rapid evolution,” said Nitin Rakesh, Chief Executive Officer and Managing Director, Mphasis. “NeoIP redefines the model by unifying Mphasis-built AI solutions, partner technologies, and client assets into a single, intelligent-by-design platform that is secure by default and scalable by nature.”

The new solution features an intelligent layer - Ontosphere - that collaborates with AI agents to build and sustain dynamic knowledge graphs tailored to enterprise domain contexts. This enables real-time contextual insights, autonomous actions, and intelligent orchestration of enterprise processes.

NeoIP’s capabilities span four key areas—Modernisation, Application Development, ITOps, and BusinessOps—delivered through specialised AI agents and frameworks. The platform also connects seamlessly with third-party AI agents through Model Context Protocol (MCP) and Agent2Agent (A2A) standards, allowing for unified, cross-enterprise orchestration.

Early client engagements have shown strong results, including up to 60% improvement in development and modernisation efficiency, a 50% reduction in mean time to detect and resolve IT incidents and 3–5 hours of predictive early warning for major outages using AI-based anomaly detection.