Launch HN: Openlayer (YC S21) – Testing and Evaluation for AI
Hey HN, Rish, Vikas and Gabe here. We're building Openlayer (https://www.openlayer.com/), an observability platform for AI. We've developed comprehensive testing tools to check both the quality of your input data and the performance of your model outputs.

The complexity and black-box nature of AI/ML have made rigorous testing a lot harder than it is in most software development. Consequently, AI development involves a lot of head-scratching and often feels like walking in the dark. Developers need reliable insights into how and why their models fail. We're here to simplify this for both common and long-tail failure scenarios.

Consider a scenario in which your model is working smoothly. What happens when there's a sudden shift in user behavior? This unexpected change can disrupt the model's performance, leading to unreliable outputs. Our platform offers a solution: by continuously monitoring for sudden data variations, we can detect these shifts promptly. That's not all though – we’ve created a broad set of rigorous tests that your model, or agent, must pass. These tests are designed to challenge and verify the model's resilience against such unforeseen changes, ensuring its reliability under diverse conditions.

We support seamlessly switching between (1) development mode, which lets you test, version, and compare your models before you deploy them to production, and (2) monitoring mode, which lets you run tests live in production and receive alerts when things go sideways.

Say you're using an LLM for RAG and want to make sure the output is always relevant to the question. You can set up hallucination tests, and we'll buzz you when the average score dips below your comfort zone.

Or imagine you're managing a fraud prediction model and are losing sleep over false negatives. Openlayer offers a two-step solution. First, it helps pinpoint why the model misses certain fraudulent data points using debugging tools such as explainability. Second, it enables converting these identified cases into targeted tests. This allows you to deep dive into tackling specific incidents, like fraud within a segment of US merchants. By following this process, you can understand your model's behavior and refine it to capture future fraudulent cases more effectively.

The MLOps landscape is currently fragmented. We’ve seen countless data and ML teams glue together a ton of bespoke and third-party tools to meet basic needs: one for experiment tracking, another for monitoring, and another for CI automation and version control. With LLMOps now thrown into the mix, it can feel like you need yet another set of entirely new tools.

We don’t think you should, so we're building Openlayer to condense and simplify AI evaluation. It’s a collaborative platform that solves long-standing ML problems like the ones above, while tackling the new crop of challenges presented by Generative AI and foundation models (e.g. prompt versioning, quality control). We address these problems in a single, consistent way that doesn't require you to learn a new approach. We’ve spent a lot of time ensuring our evaluation methodology remains robust even as the boundaries of AI continue to be redrawn.

We're stoked to bring Openlayer to the HN community and are keen to hear your thoughts, experiences, and insights on building trust into AI systems.