Corelayer Launches : AI On-Call Engineer for Data Pipelines

Corelayer recently launched!

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^{"Reduce your on-call support load with AI agents"}

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^{TL;DR: When you’re on call in data-heavy industries like fintech, healthcare, or insurance, you need to inspect data to debug production issues. Corelayer is a platform that monitors both data and infrastructure for anomalies and uses AI agents to debug and suggest fixes in minutes.}

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Corelayer Launch Video

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Founded by Mitch Radhuber & Shipra Jha‍

Before Corelayer, they built data infrastructure at Goldman Sachs where they spent many late nights and weekends debugging systems that processed 100s of billions of rows a day.

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On-call support is painful and expensive

All engineers hate being on call. Production issues kill velocity, erode user trust, and become more and more costly as companies scale.

Fortune 100s spend $100M+/year on first-line-of-defense production support.

Smaller companies can’t afford to burn scarce engineering resources on time-consuming support and maintenance.

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The (sensitive) data problem

If you’re a backend engineer at a fintech, you probably own a service that queries data, applies some business logic, and stores the result somewhere (i.e., a data pipeline).

When this service is running in production, you’ll see two failure modes:

1. Exceptions in logs and/or failing jobs
2. Bad data produced by your service

“Bad data” can mean anything from incorrect values to entirely missing or duplicated rows.

This is really common in certain products and industries, but you’re totally blind to these errors without monitoring data for anomalies and querying data while debugging.

In regulated environments where this problem is prevalent, production data is sensitive, which is another constraint they have to solve for.

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Corelayer solves this

They continuously monitor logs, metrics, and data for anomalies and use AI agents to debug, root-cause, and suggest fixes for issues.

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They also filter out false positives and group related issues to reduce alert noise.

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Every team is different. They take feedback from human engineers so their agents learn your systems and improve over time.

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Data is especially sensitive in regulated industries. They are SOC 2 compliant, offer on-prem deployments and confidential compute (hardware-backed secure environments), and expose an audit trail of each step performed by the agent with citations.

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Learn More

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^{🌐 Visit www.corelayer.com to learn more.}

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^{⭐ Does your team deal with data or integrations and spend more time on production support than you’d like? Chat with the team here.}

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^{🤝 Can you connect them with an engineering leader at a finserv, fintech, healthcare, or insurance company? Reach out here.}  

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^{👣 Follow Corelayer on LinkedIn & X.}

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