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Description

Launch top-notch LLM applications swiftly while maintaining rigorous testing standards. You should never feel constrained by the intricate and often subjective aspects of LLM interactions. Generative AI often yields subjective outcomes, and determining the quality of generated content frequently necessitates the expertise of a subject matter professional. If you're developing an LLM application, you're likely aware of the myriad constraints and edge cases that must be managed before a successful release. Issues such as hallucinations, inaccurate responses, biases, policy deviations, and potentially harmful content must all be identified, investigated, and addressed both prior to and following the launch of your application. Deepchecks offers a solution that automates the assessment process, allowing you to obtain "estimated annotations" that only require your intervention when absolutely necessary. With over 1000 companies utilizing our platform and integration into more than 300 open-source projects, our core LLM product is both extensively validated and reliable. You can efficiently validate machine learning models and datasets with minimal effort during both research and production stages, streamlining your workflow and improving overall efficiency. This ensures that you can focus on innovation without sacrificing quality or safety.

Description

When engineering standards live in wikis, tickets, and CI templates, they are difficult to apply consistently across a growing software organization. Earthly Lunar gives platform engineering teams a central way to enforce those standards across different repositories, pipelines, and developer workflows. It gathers evidence from source code and CI/CD execution, normalizes it into a service-level view, and runs deterministic guardrails against that data. A guardrail might require test coverage, an approved dependency, an SBOM, or a deployment check. Teams can introduce policies in a visibility-only mode, surface findings on pull requests, and move to blocking checks when ready. Developers get actionable feedback on proposed changes while platform leaders see adoption across the organization. Lunar includes a library of 200+ guardrails and supports custom policies for company-specific requirements, including lessons from incidents. Continuous results provide a record of what was checked and when, reducing the work of gathering compliance evidence.