Enterprise Solutions for Hard Science‍

Our data architecture for enterprise science preserves knowledge crucial to understanding, even with nested/conditional/multimedia structure, that won’t fit in ERP/LIMS/PLM. It provides full control to manipulate the data, with advanced tools like tree-compare, without the limitations of traditional applications.

Our environmental simulators and causal model accelerate time-to-market by understanding performance beyond physically tested environments, so we can enrich early successes for those likely to repeat. By focusing on mechanism, we can reduce time spent in multi-phase trials to validate that early results repeat across environments or interacting mechanisms.

Our simulation model and catalog of mechanisms pools the learnings from all of your historical tests to virtually screen new candidates. Simulated experiments flow through metrics calculations just like physical tests, allowing us to navigate the space of potential new candidates to focus on those most promising while dropping others with fewer physical tests.

Understand the sensitivity of product performance to batch-to-batch variation in materials or control systems, by learning and simulating the root-cause mechanisms driving production variability.