Corelayer: AI On-Call Engineers for Data-Driven Systems

Modern software systems run on data. In industries like financial services, fintech, healthcare, and insurance, production systems ingest, transform, and store enormous volumes of information—often hundreds of billions of rows per day. When something goes wrong, the impact is immediate: incorrect balances, missing records, delayed reports, broken downstream workflows, and regulatory risk.

Corelayer was created to address a painful and deeply familiar problem in these environments: on-call engineering for data-heavy systems. Traditional on-call processes rely on human engineers waking up at inconvenient hours, digging through logs, dashboards, and datasets, and manually stitching together clues to understand what broke and why. This work is slow, stressful, expensive, and increasingly unsustainable as data complexity grows.

Corelayer’s mission is to replace this reactive, human-only model with AI-powered on-call agents that can inspect infrastructure and data, debug production issues, and suggest fixes in minutes rather than hours. The startup positions itself as an AI-native operations layer for production software and data systems—one that understands not just machines, but the data flowing through them.

Why Is On-Call Support Especially Painful in Data-Heavy Industries?

On-call work is universally disliked, but in regulated, data-intensive industries, it becomes uniquely difficult. Engineers are not just responsible for uptime; they are responsible for correctness. A system may be running without throwing errors while quietly producing bad data—incorrect values, duplicated rows, or missing records that only surface later.

In sectors like fintech and healthcare, these silent failures can lead to financial loss, compliance violations, or patient harm. Yet many teams remain effectively blind to data quality issues until users complain or downstream systems fail.

The cost is enormous. Large enterprises can spend over $100 million per year on first-line production support, while smaller companies burn scarce engineering time on firefighting rather than innovation. Every alert interrupts deep work, slows velocity, and chips away at morale. Corelayer was built around the belief that this model is fundamentally broken.

What Makes Debugging Data Pipelines So Much Harder Than Debugging Code?

Traditional observability tools focus on infrastructure: CPU usage, memory, error rates, and latency. While these signals are essential, they tell only part of the story. In data pipelines, the real source of truth is the data itself.

A backend service might successfully execute every job while producing subtly incorrect outputs. Without monitoring the data for anomalies—unexpected distributions, missing values, or schema drift—engineers have no visibility into these failures. Debugging then requires querying production datasets, comparing historical patterns, and correlating data anomalies with recent changes in code or infrastructure.

This process is time-consuming even in permissive environments. In regulated industries, it becomes even harder because production data is sensitive and tightly controlled. Engineers must navigate access restrictions, audit requirements, and security constraints while under pressure to resolve incidents quickly. Corelayer was designed specifically to operate within these constraints rather than work around them.

How Does Corelayer Use AI Agents to Transform On-Call Engineering?

At the core of Corelayer’s platform are AI agents designed to act like experienced on-call engineers. These agents continuously monitor logs, metrics, and—crucially—data itself for anomalies. When an issue is detected, the agent doesn’t just raise an alert; it begins debugging.

The AI inspects relevant data, traces anomalies back through pipelines, correlates them with infrastructure events or recent deployments, and identifies likely root causes. It then generates suggested fixes, providing engineers with actionable insights rather than raw signals.

By filtering false positives and grouping related issues, Corelayer reduces alert noise—a major contributor to on-call fatigue. Over time, the system learns from human feedback, adapting to each team’s unique architecture, business logic, and operational preferences. The result is an AI-assisted on-call workflow that becomes more accurate and more helpful the longer it runs.

Why Is Data Sensitivity a Central Design Constraint for Corelayer?

Unlike many AI tools that assume cloud-native, open-data environments, Corelayer was built with regulated industries in mind. In fintech, healthcare, and insurance, production data often contains personally identifiable information, financial records, or protected health data. Sending this data to external systems is not an option.

To address this, Corelayer offers on-prem deployments and confidential compute environments—hardware-backed secure enclaves that allow AI agents to safely access production data without exposing it. The platform is SOC 2 compliant and provides a detailed audit trail of every action taken by the agent, complete with citations and explanations.

This approach allows teams to leverage AI for deep, data-driven debugging while maintaining strict compliance and security standards. Rather than forcing organizations to choose between innovation and regulation, Corelayer aims to make advanced AI operationally safe by design.

Who Are the Founders Behind Corelayer, and Why Are They Credible?

Corelayer was founded by Shipra Jha and Mitch Radhuber, engineers who have lived the on-call pain firsthand. Before founding Corelayer, both worked on large-scale data infrastructure at Goldman Sachs, where they spent countless late nights and weekends debugging systems processing hundreds of billions of rows per day.

Shipra Jha, Co-Founder and CTO, brings deep experience in software and data infrastructure from Goldman Sachs, cloud infrastructure from Oracle, and a strong academic background in computer science from Carnegie Mellon University. Mitch Radhuber, Co-Founder and CEO, also comes from Goldman Sachs, with additional experience in astrophysics research at Princeton and computer science at the University of Michigan.

Their shared background gives them a rare combination of hands-on operational experience and technical depth. Corelayer is not a theoretical solution built from the outside; it is a product shaped by years of real-world frustration inside some of the most demanding data environments in the world.

How Does Corelayer Reduce Costs While Improving Reliability?

By automating large portions of on-call debugging, Corelayer aims to dramatically reduce the cost of production support. Issues that once required hours of human investigation can now be diagnosed in minutes, freeing engineers to focus on building new features rather than maintaining old ones.

For large enterprises, this means reducing reliance on expensive, always-on support teams. For smaller companies, it means avoiding the trade-off between scaling infrastructure and burning out engineers. Faster resolution times also translate into higher system reliability, better user trust, and lower downstream costs caused by bad data propagating through the organization.

Corelayer positions its platform not as a replacement for engineers, but as an always-available first responder that handles the most tedious and time-sensitive aspects of on-call work.

How Does Human Feedback Improve Corelayer’s AI Over Time?

One of Corelayer’s key design principles is adaptability. Every team has different data models, business rules, and operational priorities. A one-size-fits-all AI agent would quickly fall short.

To solve this, Corelayer incorporates continuous feedback from human engineers. When an agent suggests a fix or identifies a root cause, engineers can validate, correct, or refine its conclusions. This feedback is used to train the system on the specifics of each environment, allowing it to improve over time.

The result is a collaborative loop where humans and AI work together. Engineers retain control and accountability, while the AI handles the repetitive analysis and pattern recognition that slows teams down.

What Does Corelayer’s Launch Say About the Future of DevOps?

Corelayer’s launch reflects a broader shift in how companies think about operations. As systems grow more complex and data volumes explode, purely human-driven on-call models are no longer sustainable. AI-native operations are emerging as a necessity rather than a luxury.

By focusing on data-aware debugging, secure deployments, and regulated industries, Corelayer is carving out a niche that many existing observability tools have ignored. Its approach suggests a future where AI agents become trusted members of engineering teams—always on call, always learning, and always ready to dive into the data when something breaks.

Can Corelayer Redefine What “On Call” Means?

At its core, Corelayer is asking a provocative question: what if being on call didn’t mean constant interruptions, sleepless nights, and reactive firefighting? What if AI could shoulder the burden of first response, leaving humans to handle only the most complex and strategic decisions?

By combining deep data inspection, secure AI execution, and continuous learning, Corelayer offers a compelling vision of on-call engineering that is faster, calmer, and more humane. If successful, it could redefine not just how incidents are resolved, but how engineers experience their work in data-driven organizations.

In a world where data is both the most valuable asset and the most common source of failure, Corelayer is betting that the future of reliability lies in AI agents that understand data as well as engineers do—if not better.