Distributional

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Distributional's next chapter: Talaria Scientific

Timeline of four eras on one through-line labeled 'make expensive computational work optimal, scalable, and trustworthy': MOE/SigOpt (Optimize it.), Intel (Scale it.), Distributional (Trust it.), Talaria Scientific (Accelerate it.). A box around Distributional and Talaria marks them as the same Delaware corporation with the same investors. An arrow from Talaria labeled 'one level deeper' points from the tooling around the science to the science itself.
One mission, four eras: optimize expensive computational work, run it at scale, trust it in production, and now point it at science itself. The box around the last two is the same-entity part: Distributional and Talaria Scientific are one company with the same investors. The mission is what moved one level deeper.In the postDownload SVG© 2026 Distributional, Inc. · CC BY-ND 4.0

Beyond averages: how DBNL's distribution comparison reveals what summary metrics hide

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Enrichments versus evals

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The agent observability hierarchy

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Demo: Financial analyst

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Demo: Improving agents with production data analysis

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Demo: Agent hyperparameter optimization with behavioral analytics

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Demo: Fixing agent issues in production with tab complete analytics

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Analytics-driven agent A/B testing

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DBNL fits your agent data

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Integrating DBNL with NVIDIA NeMo Agent Toolkit

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Adapting product analytics to the AI era

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The Distributional Workflow: Define, Detect, Understand, and Improve

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Scale, Metrics, Tests, and Results in Distributional

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How Distributional tests for consistent app behavior in production

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How Distributional’s Similarity Index works

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How Distributional balances security with insights

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Designing for speed and scale: Integrating Distributional within an existing environment

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Understanding Distributional’s Platform Architecture

Distributional's platform architecture
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Adaptive testing for AI confidence

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Distributional Simplifies Adaptive Testing with Similarity Index and Key Insights

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Driving confidence in AI: Understanding deployment testing through model version updates

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Bridging the AI Confidence Gap with adaptive behavioral testing

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What is Distributional? Explaining AI behavior through the language of statistical distributions

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Driving confidence in AI: Foundational distributional testing explained through a RAG example

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The AI Software Development Lifecycle: A practical framework for modern AI systems

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