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Draft:Openlayer

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Openlayer is a technology company based in San Francisco, California, that provides a platform for AI evaluation, observability, and governance. The company supports both traditional machine learning (ML) and generative AI (GenAI) systems, offering tools to evaluate, monitor, and ensure the quality and safety of models in development and production.

History

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Openlayer was founded in 2021 by Gabriel Bayomi, Vikas Nair, and Rishab Ramanathan, who were formerly engineers at Apple and participated in the Y Combinator Summer 2021 cohort.[1]

The company launched with a focus on improving the reliability of AI systems through better testing, monitoring, and compliance tooling. It has since evolved into a unified platform serving enterprises across experimentation, deployment, and production monitoring.

Funding

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In May 2023, Openlayer announced a US$4.8 million seed round led by Quiet Capital, with participation from Y Combinator, Picus Capital, Hack VC, Liquid2 Ventures, Soma Capital, Mantis VC, and Ground Up Ventures.[2]

In May 2025, the company raised a US$14.5 million Series A round led by Race Capital, with participation from NXTP Ventures, Mindset Ventures, Y Combinator, Quiet Capital, Wayra, KPN Ventures, and Mento VC.[3]

Product

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Openlayer’s platform offers tools for:

- Evaluating and testing machine learning and generative AI models - Monitoring GenAI chains and agents for performance and reliability - Validating data quality through schema checks and anomaly detection - Supporting AI governance and compliance with industry standards

The platform integrates with data and compute systems such as Snowflake, Databricks, and BigQuery.

References

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  1. ^ "Openlayer – Y Combinator". Y Combinator. Retrieved 2025-06-06.
  2. ^ "Openlayer raises USD4.8M seed round to build guardrails for AI". Openlayer Blog. 2023-05-04.
  3. ^ "Openlayer raises $14.5M to help developers assess and improve AI model performance". SiliconANGLE. 2025-05-14.
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- [1](https://www.openlayer.com/) Official website]

See also

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- MLOps - Artificial intelligence - Model validation - Data quality - LLMOps