Vals Grading Layer for Enterprises

This makes Vals easier to adopt inside large companies because it separates grading from generation. A team can keep its own model, prompts, retrieval stack, and sensitive data inside its own cloud, then send only the finished answers into a Vals run for scoring against checks. That turns Vals into the quality control layer, not the place where the model actually runs, which lowers security and procurement friction.

• In practice, the upload flow is simple. Teams can upload a CSV with Question and Answer fields, plus optional context columns, and Vals grades those outputs with the selected evaluation model. That means a company can batch run its own app internally overnight, then use Vals only for pass fail analysis, summaries, and review workflows.
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• This matters most for regulated and privacy sensitive use cases. Vals is already exposed to enterprise tasks like legal and document heavy workflows, and the company profile highlights data custody as a key buying barrier. The grading only setup is a direct way to reduce that barrier without giving up the structured test suite and human review system.
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• The broader product is built around fitting into existing engineering workflows rather than replacing them. Vals supports a CLI, SDK, GitHub Actions, and custom functions that call a team’s own app. The upload path is the lowest commitment version of that idea. It lets a team keep its serving stack unchanged and add evaluation after the fact.
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Over time, this pushes Vals toward becoming the neutral measurement layer for enterprise AI systems. If more teams keep inference inside their own stack and standardize on Vals for grading, review, and CI checks, the company can sit across many models and apps at once, which is a stronger position than competing to host the model itself.