Codeglide.ai Launches Continuous MCP Server Lifecycle Platform for Real-Time AI Readiness

Codeglide.ai Launches Continuous MCP Server Lifecycle Platform for Real-Time AI Readiness

Codeglide.ai, an Opsera subsidiary, today introduced the first continuous Model Context Protocol (MCP) server lifecycle platform, designed to automatically convert enterprise APIs into AI-ready MCP servers and keep them in sync as systems evolve.

Filling the API-to-AI Integration Gap

Traditional SDK generators and one-off migration tools fall short in today’s fast-paced AI world. They lack the agility and context needed to serve AI agents accurately. Codeglide.ai steps up as a perpetual solution constantly monitoring API changes, generating secure MCP servers, and ensuring AI agents and large language models (LLMs) access enterprise systems reliably and contextually.

Built for the GitHub-Powered Enterprise

Seamlessly embedded within the GitHub ecosystem, Codeglide.ai is delivered via SaaS and GitHub Marketplace, integrating with GitHub Actions, secrets scanning, and more. This makes it accessible, secure, and highly developer-friendly perfect for DevOps and AI engineering teams.

Solving Real Enterprise Challenges at Scale

In many enterprises, thousands of APIs, sometimes 60% legacy, create complex hurdles for AI integration. Static integration methods struggle to keep up with real-time changes, slowing down AI rollout. Codeglide.ai addresses this, automating the lifecycle of MCP servers across all APIs and reducing manual effort dramatically. Enterprises can experience integration time reduced by up to 97% and cost savings of up to 90%.

Leadership Speaks: AI Ready Without the Headache

“AI can’t deliver its full potential if your APIs are stuck in the past,” said Kumar Chivukula, Co-founder and CEO of Opsera. “Codeglide.ai ensures APIs evolve into secure, AI-ready servers enabling organizations to scale AI confidently, without interruption.”

This platform smooths the path for enterprises to manage changes whether it's schema updates, new endpoints, or retiring old ones without disrupting AI workflows.

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