In process development, the best optimization method is useless if the team can’t actually use it
In process development, the best optimization method is useless if the team can’t actually use it
Process optimization teams are rarely homogeneous. In the same project, you might have:
- Bench chemists focused on running experiments
- Process scientists thinking about scale-up and robustness
- Data scientists developing models and workflows
For tools to genuinely support process development, they need to work across this spectrum - not just for those who are already comfortable with them.
We’ve spent a lot of time talking to development teams, working in our own lab, and iterating to address a simple question:
How do you make advanced optimization methods easily accessible without diluting their customizability?
The goal is for someone to be productive on the platform within a short onboarding session - whether they’ve never written code before, or actively publish in the space.
In practice, this shows up in a few ways:
- An interface designed around how chemists actually think about experiments
- A range of visualizations, from response plots to detailed molecular insights
- API integrations with automated hardware and PAT
Just as importantly, the platform remains flexible enough for teams who do want deeper customization and control.
Power users can still go deeper - automatically import/export data via APIs, plug in their own pipelines, and customize how models are configured and used.
For us, success isn’t just about building powerful models - it’s about building software that teams actually adopt, keep using, and trust throughout development.
We want ReactWise to provide value to every process development team, regardless of where they are today in terms of data, automation, or computational expertise.
