Corelayer Raises Y Combinator–Backed Seed Funding to Build AI “On-Call Engineer” for Enterprise Data Systems 

Corelayer, an AI-native platform building what it calls an “AI on-call engineer” for data-heavy and regulated industries, has raised $500,000 in seed funding to expand its autonomous production support system for enterprise software and data infrastructure teams. The company, founded in 2025 and part of the Y Combinator Winter 2026 batch, is developing agentic AI tools that monitor logs, metrics, and data pipelines to detect incidents, diagnose root causes, and recommend fixes in real time.

The funding is primarily backed by Y Combinator, which provides early-stage capital along with access to its accelerator program, mentorship network, and enterprise customer ecosystem. Corelayer’s inclusion in the YC Winter 2026 cohort positions it among a group of startups focused on applied artificial intelligence for infrastructure automation, developer tooling, and enterprise operations.

Corelayer’s platform is designed to address a persistent challenge in large-scale data and software systems: the high cost and complexity of production support. In industries such as financial services, healthcare, and insurance, engineers on call are responsible not only for infrastructure uptime but also for debugging data integrity issues, pipeline failures, and silent data corruption that can propagate through downstream systems.

The company’s AI system continuously monitors production environments, including logs, metrics, and underlying datasets, to identify anomalies as they occur. Once an issue is detected, the system launches automated investigation workflows using AI agents that analyze system behavior, trace dependencies across services, and identify likely root causes. It then generates suggested fixes, allowing engineering teams to resolve incidents more quickly and reduce manual debugging time.

Corelayer was founded by Mitch Radhuber and Shipra Jha, who previously worked together on data infrastructure systems at Goldman Sachs. Their experience in high-volume, regulated environments informed the company’s focus on industries where systems process massive datasets and where even small errors can have significant financial or operational impact.

A key differentiator for Corelayer is its emphasis on data-aware debugging. Unlike traditional observability tools that focus primarily on infrastructure signals, Corelayer’s system also inspects data outputs to detect anomalies such as missing records, incorrect transformations, or inconsistent aggregations. This dual-layer monitoring approach is designed to surface issues that conventional monitoring systems may not detect.

The platform also includes security and compliance features tailored for regulated industries. Corelayer offers on-premise deployment options, confidential computing environments, and audit trails that document each action taken by its AI agents. These features are intended to ensure that sensitive production data remains secure while still being usable for automated debugging.

According to company information, Corelayer’s system is built to integrate with a wide range of enterprise tools, including cloud platforms, data warehouses, orchestration systems, and observability stacks. This includes integrations with infrastructure providers such as AWS, Google Cloud, Snowflake, dbt, and Datadog, allowing the platform to operate across complex, heterogeneous environments.

The funding from Y Combinator will be used to expand Corelayer’s engineering team, improve the accuracy and reliability of its AI debugging agents, and scale early deployments with enterprise customers in fintech, healthcare, and insurance. The company is also investing in strengthening its context-aware reasoning systems, which allow its agents to build persistent knowledge of a customer’s production environment over time.

As enterprises continue to scale data-intensive systems and adopt more AI-driven infrastructure, demand is growing for tools that can reduce operational overhead and improve incident response times. Corelayer is positioning itself within this emerging category of autonomous operations tooling, where AI systems not only detect problems but actively participate in resolving them.

With its early backing from Y Combinator, Corelayer is now focused on expanding its product capabilities, increasing enterprise adoption, and refining its agent-based approach to production support in highly regulated, data-intensive environments.