Empower AI teams to be confident in their AI products
Our mission is to empower AI teams to understand AI behavior so they can build more reliable, refined, and capable AI products.
Testimonials
Distributional provides a unique testing workflow for outlier and drift detection to help with Al reliability. l've already found the platform to be useful for text-based applications with structured output and expect that its utility will continue to grow to other Al applications.
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GenAl has created unique challenges that aren't well handled by existing MLOps platforms and workflows. Most notably, enterprises seek ways to ensure that GenAl systems behave as expected and don't introduce unpredictable behavior that could result in reputational harm, poor user experiences, or costly business consequences. New approaches to system profiling and testing are required to meet this need, and Distributional's adaptive testing solution is purpose-built to solve this for enterprise teams.
Lack of reliability in AI systems in one of the biggest barriers to widespread enterprise adoption. We are excited for distributional to address this problem by building a platform for robust and repeatable AI testing.
This is a huge opportunity
As the capacity of AI expands, so does its potential impact across a growing range of applications.
But as teams scale AI in production, they often find it increasingly becomes a black box. And without confidence in how their products are being used–or how this usage shifts over time–AI teams find it hard to evolve their products over time.
The first step is understanding AI behavior – the interplay and correlations between users, context, tools, models, and metrics.
Rely on Distributional to understand AI product behavior and scale these AI products with confidence.
Team: Built to deliver, support, and evolve this roadmap
Scott Clark
CEO
Multi-time founder, founded and sold SigOpt to Intel, ran 200 person AI & HPC engineering team at Intel, applied math PhD from Cornell with focus on Bayesian optimization, dozens of AI publications, papers, and talks.
Renaud Bourassa
Chief Architect
More than 12 years experience as senior staff engineer building, scaling, and optimizing AI/ML systems, infrastructure, and pipelines at Stripe, Slack, and Google.
Erin LeDell
Chief Scientist
Former Chief ML Scientist at H2O.ai with dozens of publications and open source projects, including the H2O open source AutoML library. Holds a PhD in biostatistics, computational science, and engineering from Berkeley.
Nick Payton
COO
More than 15 years in go-to-market and operations leadership for fast-growing enterprise AI, data, and vertical software products with 3 exits and 1 IPO, including SigOpt and Opower (now Oracle Utilities).
Harvey Cheng
Research Engineer
AI researcher at Intel and SigOpt with dozens of publications focused on multiobjective optimization and multiple open source projects, PhD in Operations Research from Princeton.
Katrina Crisostomo
Software Engineer
Front-end focused engineer with experience at publicly traded companies and fast-growing startups, including Watsi, Uplift, Lilia and Here.fm.
Ian Dewancker
Product
Multi-time founder, founded industrial synthetic data company SBX Robotics, former researcher at Uber ATG, former researcher at SigOpt where he focused on optimization research.
Dan Anderson
Software Engineer
Technical team lead at SigOpt and Intel with focus on back-end, deployment, security and privacy, with engineering experience in the US Department of Defense and the financial services industry.
Taylor Jackle Spriggs
Software Engineer
Full stack software engineer at SigOpt and Intel with deep experience scaling systems supporting AI/ML, statistical, or HPC workflows.
We have a passionate team
We are a dedicated team of AI researchers, software engineers and customer-driven leaders who are serious about solving customer problems, but don’t take ourselves too seriously. In combination with our customers, investors, partners and technology, our team has a unique opportunity to make AI reliable for the full range of enterprise AI and ML use cases.