Product Development with Uncountable
Compound and formulation development processes require significant time and numerous iterations to find the optimal formulation.
Uncountable enables R&D labs to cut development time and reduce testing iterations.
The Uncountable Platform increases the lifetime value of each experiment that is run. Scientists can leverage experiments from the complete record of their colleagues’ trials. Managers can monitor projects and intervene quickly to change priorities.
Uncountable’s artificial intelligence algorithms power the platform and mitigate the need for tedious, manual tweaking of individual ingredients. Complicated development processes that used to involve tens or hundreds of experiments can now be conducted in half the time utilizing advanced machine learning models.
HOW IT WORKS
STEP 1: INGEST PREVIOUS TEST DATA
Uncountable's team works to train scientists on utilizing the platform as a knowledge management tool. Scientists can record every piece of the development process, including property objectives, testing requirements, list of available ingredients, past formulations and test history.
Past experimental data can be ingested via several methods, including via database dumps, disparate spreadsheets, or other record systems.
STEP 2: VISUALIZATION AND SEARCHING
The Uncountable Platform has analysis tools built in that can learn trends from across a series of development projects and connect the dots between experiments throughout the organization.
Advanced searching ability within the platform puts every experiment at the scientist’s fingertips. The platform makes it easy to see whether a new set of product specifications is within reach of an organizations current capabilities.
STEP 3: ARTIFICIAL INTELLIGENCE GUIDED EXPERIMENTATION
Uncountable's machine learning algorithms build out a model of the material property performance "space." The use of artificial intelligence models allows the system to understand the effect that altering each subset of ingredients will have on the performance of the formulation.
The model generated can then be queried for the next best set of experiments in order to achieve a set of critical performance properties.
The machine learning engine is continuously improved by feeding further trial information into its base of test history, meaning a research team gets better and better at finding breakthrough materials.