1536.
1536.
That’s how many new datapoints we generated last week in our HTE lab.
Not data for data’s sake. Not random screening. But structured, decision-grade reaction knowledge - built to help our client’s process and development teams run fewer experiments and make better decisions earlier.
The problem we see all the time is simple: a new optimization campaign kicks off, and teams spend days or weeks burning material and time just to find initial good hits.
Meanwhile, a huge amount of learning already exists, but it’s fragmented - hidden in past runs, edge cases, “we tried this once” notebook pages, and conditions that never make it into a clean dataset.
So we’re doing something surprisingly rare for a chemistry software: we’re mapping key transformations systematically, generating consistent, comparable datasets across solvents, bases, ligands, catalysts, temperature and time windows, and - most importantly - the interactions that only show up when you look at the data holistically.
The goal is straightforward. When a client starts an optimization campaign, they shouldn’t have to begin from scratch.
They should be able to warm-start with strong priors, avoid dead ends early, and converge faster to robust, scalable conditions.
In other words: fewer experiments, clearer insights, and a path that’s easier to communicate across the team.
Because the best optimization isn’t just “what should I try next?” It’s “what do we already know - and how do we use it immediately?”
Let’s make chemistry smarter - together.
