Corelayer

Problem/Solution Report

The core problem Corelayer addresses is the immense pain, time, and cost associated with supporting and maintaining modern production systems, especially those that are data‑intensive. In industries like financial services, fintech, healthcare, and insurance, on‑call engineers must frequently debug production issues by manually inspecting vast and sensitive datasets. These data‑related issues are often subtle, complex, and time‑consuming to diagnose, leading to long resolution times, engineer burnout, and potential impact on customers.

Traditional monitoring and observability tools often focus on infrastructure metrics (like CPU and memory usage) and logs but lack a deep understanding of the data itself. When an anomaly occurs— for example, a data pipeline runs successfully but produces incorrect values—these tools may not even register an issue. This forces engineers into a reactive, manual, and inefficient debugging process, trying to piece together clues from logs, source code, and past incidents, all while needing to carefully handle sensitive production data.

Corelayer’s solution is an AI‑powered on‑call engineer named Sevy. It acts as the first line of defense for production systems by providing "data‑aware" monitoring and automated debugging. The platform continuously monitors both infrastructure and the data within it for anomalies. When it detects an issue, such as an unexpected spike in a data value, it triggers an AI agent that mimics the debugging process of a human expert. This agent uses context from logs, observability tools, source code, past issues, and, crucially, the underlying production data to perform root‑cause analysis.

The platform is designed with security and regulated industries in mind. It offers flexible deployment options, including on‑premises, and utilizes confidential compute environments to ensure that sensitive production data can be used for debugging without being exposed. The system has read‑only access and provides features like custom PII detection and masking. By automatically identifying the root cause and suggesting fixes, often including the exact code change needed, Corelayer dramatically reduces the manual workload on engineers, shortens resolution times, and helps prevent issues from impacting customers.

Market and Competitors Report

Corelayer operates in the competitive and evolving AIOps and IT operations management market. The company’s primary competitive advantage lies in its specific focus on using data as the core context for debugging production issues, which differentiates it from broader observability and incident management platforms. Its target market consists of data‑heavy teams in regulated and complex environments like finance, healthcare, and insurance.

The competitive landscape can be broken down into several categories:

1. Broad AIOps & Observability Platforms: This is the most established segment, dominated by large players like Datadog, Dynatrace, New Relic, and Splunk. These platforms offer comprehensive solutions for monitoring infrastructure, applications, logs, and user experience. While they are increasingly incorporating AI/ML for anomaly detection and root cause analysis, their focus is often on metrics and logs rather than deep data content analysis. Corelayer competes by offering a more specialized, data‑centric approach to a problem these larger platforms may not address with the same depth.

2. AIOps & Incident Management Specialists: Companies like BigPanda and Moogsoft (acquired by ServiceNow) specialize in AIOps for incident management. They focus on reducing alert noise by correlating events from various monitoring tools into single, manageable incidents. While they are direct competitors in automating incident response, Corelayer’s differentiator is its ability to go beyond alert correlation and perform debugging using the actual data, not just the signals from other tools.

3. Data Observability Platforms: This is an emerging category with players like Monte Carlo and Bigeye. These companies focus on ensuring data quality and reliability within data pipelines and warehouses. They monitor for data freshness, volume, schema changes, and quality issues. Corelayer is adjacent to these companies but focuses more on the operational/on‑call aspect of data issues within live production applications, rather than just the data pipelines themselves.

4. On‑Call & Incident Response Tools: PagerDuty and Atlassian's Opsgenie are leaders in the on‑call scheduling and alerting space. They are essential for managing the human side of incident response. Corelayer is not a direct competitor but rather a complementary tool. While PagerDuty alerts the right engineer, Corelayer’s AI agent can start the debugging process before the engineer even logs on, potentially resolving the issue or providing a root cause analysis by the time they arrive.

Founders and Background Report

Corelayer was founded in 2025 by CEO Mitch Radhuber and CTO Shipra Jha. The company, based in San Francisco, is a graduate of the Y Combinator Winter 2026 batch. The founding team brings a wealth of experience in building and maintaining large-scale data infrastructure, particularly from their time working together at Goldman Sachs. Their direct experience with the challenges of debugging complex, data‑heavy systems in a highly regulated financial environment was the primary impetus for creating Corelayer.

Mitch Radhuber, Co‑Founder & CEO, has a background in computer science from the University of Michigan and conducted astrophysics research at Princeton. Prior to co‑founding Corelayer, he was instrumental in developing software and data infrastructure at Goldman Sachs. His leadership at Corelayer is focused on building an AI‑native operations layer for production software and data systems, leveraging his deep understanding of the problems faced by on‑call engineers.

Shipra Jha, Co‑Founder & CTO, holds a computer science degree from Carnegie Mellon University. Her professional history includes roles in cloud infrastructure at Oracle and, most significantly, software and data infrastructure at Goldman Sachs alongside Radhuber. At Goldman, they were responsible for systems processing hundreds of billions of rows of data daily, which provided the foundational insights for Corelayer's product. As CTO, Jha leads the technical development of the company's AI‑powered platform.