Hypercubic

Hypercubic, based in San Francisco, California, develops AI agents to autonomously understand, maintain, and modernize legacy mainframe systems running COBOL code. These critical systems, some dating back to the 1950s and 1960s, power essential infrastructure for Fortune 500 companies across sectors like banking, insurance, retail, airlines, and government. The company's AI technology addresses the significant challenge of maintaining and upgrading these outdated but vital technologies that underpin global operations. As a participant in the Y Combinator Fall 2025 batch, with primary partner Garry Tan, Hypercubic is a seed-funded startup. The organization currently operates with 2 employees and targets large enterprises heavily reliant on mainframes for their core operations. Founded in 2025 by Sai Gurrapu, an ex-Apple Machine Learning Engineer, and Aayush Naik, an ex-Apple engineer, Hypercubic aims to transform mainframe modernization.

High-Level Overview

Hypercubic is an AI-driven platform that specializes in maintaining and modernizing legacy mainframe systems, particularly those running COBOL code from the 1960s, which remain critical to Fortune 500 companies across banking, insurance, retail, airlines, and government sectors[1][2][4]. Its mission is to preserve and extend the life of these complex, mission-critical infrastructures by capturing and codifying the human expertise and institutional knowledge embedded in them, using advanced AI agents. Hypercubic’s investment philosophy centers on leveraging AI to solve the pressing problem of knowledge loss and system brittleness in legacy mainframes, a sector that remains indispensable yet under-served by modern software tools. By enabling enterprises to understand, document, and autonomously maintain these systems, Hypercubic significantly impacts the startup ecosystem by pioneering AI applications in legacy modernization, a niche with vast economic and operational importance.

As a portfolio company, Hypercubic builds two core products: HyperDocs, which ingests and documents millions of lines of legacy code into structured, readable formats, and HyperTwin, which creates digital twins of subject-matter experts by capturing their workflows and reasoning to preserve tribal knowledge[2][3]. These tools serve large enterprises with legacy mainframe operations, addressing the critical problem of a retiring workforce and opaque, brittle systems that are costly and risky to maintain. Hypercubic’s growth momentum is fueled by the urgent demand from Fortune 500 companies to safeguard and modernize their legacy infrastructure before expertise disappears, compressing months or years of manual reverse engineering into weeks with AI assistance[2][3].

Origin Story

Hypercubic was founded by ex-Apple engineers, including Sai Gurrapu and Aayush Naik, who recognized the looming crisis in legacy system maintenance as the generation of engineers familiar with COBOL and mainframes began retiring[1][7]. The idea emerged from the observation that despite decades-old technology, these systems underpin critical global infrastructure and are increasingly difficult to maintain due to lost institutional knowledge. Early traction came from demonstrating how AI could rapidly ingest and document sprawling COBOL codebases and replicate expert reasoning, drastically reducing downtime and operational risk for large enterprises[2][4]. This human-centered AI approach evolved from traditional code analysis to capturing the "how" and "why" behind system operations, setting the stage for autonomous AI maintenance agents.

Core Differentiators

• AI-Native Maintenance Platform: Unlike conventional code analysis tools, Hypercubic’s AI captures not only code but also the tacit knowledge and reasoning of expert engineers, creating a living knowledge graph linking code, systems, and human workflows[2][4].
• Dual-Tool Approach: HyperDocs automates documentation and architecture visualization of legacy codebases, while HyperTwin builds digital twins of experts to preserve tribal knowledge and operational workflows[2][3].
• Speed and Efficiency: Compresses months or years of manual reverse engineering into weeks, enabling faster modernization and reducing risk of outages[2][3].
• Focus on Legacy Ecosystem: Specifically designed for COBOL, JCL, PL/I, and mainframe environments, addressing a niche with limited modern tooling but critical business impact[3][4].
• Preservation of Institutional Knowledge: Captures the "how" and "why" behind workflows, not just the code, creating a proprietary asset that mitigates the talent gap caused by retiring experts[3].

Role in the Broader Tech Landscape

Hypercubic rides the trend of applying advanced AI and large language models (LLMs) to legacy software systems, a sector historically neglected by modern development tools. The timing is critical as a large portion of the global economy still depends on fragile, decades-old mainframe systems with a shrinking pool of experts[1][4]. Market forces such as the increasing cost of downtime, regulatory pressures, and digital transformation initiatives favor solutions that can safely modernize these systems without risking outages. Hypercubic influences the broader ecosystem by pioneering AI-driven knowledge preservation and autonomous maintenance, potentially setting a new standard for how enterprises manage legacy infrastructure and bridging the gap between old and new technology paradigms.

Quick Take & Future Outlook

Looking ahead, Hypercubic is poised to expand its autonomous AI capabilities to not only document and preserve but also proactively maintain and modernize mainframe systems end-to-end[4]. Trends shaping its journey include the maturation of AI reasoning beyond pattern matching, the growing urgency of legacy system modernization, and the increasing adoption of AI in enterprise IT operations. As Hypercubic evolves, it may become a critical enabler for digital transformation in industries reliant on legacy infrastructure, potentially extending its platform to other legacy languages and systems. Its influence will likely grow as enterprises seek to future-proof their core operations against talent loss and technological obsolescence, making Hypercubic a key player in the intersection of AI and legacy modernization.