Sunthetics accelerates sustainable innovation in the chemical industry.
The production of chemicals is indispensable in the products we use on a daily basis, from electronics to textiles to food. The chemical industry, however, has become the third largest contributor of greenhouse gas emissions, with more than half of its resources ending up in waste streams.
Sunthetics' mission is make the chemical industry more sustainable, one reaction at a time. The only way to create lasting sustainable impact in the industry is to fast-track current innovation around processes that have lower energy consumption, better resource usage, safer or bio-based starting materials, and integrate with renewable energy sources.
This innovation traditionally takes years to be developed because of the thousands of experiments needed to reach a viable, commercializable product. Traditional predictive modeling tools, whether they be physical, statistical or machine-learning, also require a deep understanding of the reaction or many data points to generate valuable insights.
At Sunthetics, we're the first to leverage very few data points to generate insights equivalent to thousands of experiments, accelerating a multi-year process into just a few months.
We are part of a new generation of chemical engineers that have a responsibility to the environment and to our society. We incorporate sustainability in our solution as an asset, and not a disadvantage. As chemical engineers we understand that the importance of cost and efficiency in chemical manufacturing, but through our tools, we can reduce costs and improve efficiency while significantly reducing carbon emission, energy, and raw material usage.
Sunthetics makes a sustainable chemical industry not just a possibility, but a reality.
We offer a machine-learning tool that is capable of leveraging small datasets to generate big insights. Sunthetics' platform enables process chemists to innovate and pinpoint viable processes using only a few experiments, accelerating the path to market for innovation by 75%.
The optimization process for chemical reactions is often empirically driven. This means that chemists will test operation points that, based on reaction understanding, will yield optimal results. This process can be quite time and resource intensive as small tweaks in many different factors can have a large effect on production. Machine-learning enables the creation of complex patterns between the different process variables and quickly pinpoints the optimal operation point while guiding a smart and efficient experimental campaign.
Our platform is reaction agnostic, meaning we can enter a variety of verticals in the chemical industry and to have a large impact in a short amount of time. We're specifically focused on pharmaceuticals and specialty chemicals initially, as these industries constantly require the development of novel reactions.