Molecular feature intelligence
Turn compounds from names into a computable feature space the optimizer learns from — structure, not spreadsheet labels.
AI-native formulation intelligence
Reach the right formulation in dozens of experiments, not millions.
Formulith turns your experimental data, molecular features, and expert constraints into the next experiment worth running.
The bottleneck
The bottleneck isn't effort. It's search-space complexity.
Take five main components with fixed concentration ranges, then add zero to two more from a library — in 0.5% steps, no more than 5% of the total. Add one variable — a component, a concentration step, a process parameter — and the space doesn't grow linearly. It explodes.
As formulation complexity grows, how do you stay ahead?
Even under a simplified model, one formulation space can hold
32.7B
combinations
Test ten a day, and finishing them all would take
8.96M
years
The problem was never effort. It's a space too large for trial-and-error.
The platform
See the whole search space — not just a table of results.
Formulith builds CatalystFlow™ — a decision platform for constrained, multi-objective formulation R&D. Instead of testing one cell at a time, you see the whole landscape: what's been explored, what's still unknown, and where the next experiment is most likely to pay off.
tested unexplored★ optimum
More experiments. Fewer insights.
explored unexplored next experiment★ optimum
Fewer experiments. Faster arrival.
Stop reading the table. Start seeing the space.
Capabilities
What the platform does.
Three streams of input — experimental data, molecular features, and expert knowledge — flow through an adaptive engine that returns ranked experiments, new candidate molecules, and explanations you can trace. Every output feeds back into the next round.
Each flow above is backed by a focused capability:
Expert constraints
Encode what chemists would actually run: required and excluded compounds, cardinality limits, objective weights, concentration ranges, process windows.
Adaptive strategy selection
There's no single best optimizer — only the one that fits the problem. The platform selects the strategy per campaign, so you don't need a data-science team.
Explainable recommendations
Every suggestion comes with the features and trade-offs behind it: an answer you can question and trace, not a black box.
Closed-loop flywheel
Each completed experiment feeds back and sharpens the next recommendation.
Plateau detection
The platform flags when a campaign stops improving, so you adjust the search instead of repeating low-value experiments.
How it compounds
Every experiment compounds.
One experiment's value is limited. But when each one makes the model more accurate, value accumulates: better recommendations → fewer dead-ends → faster to a working formulation.
One experiment teaches the model. A thousand build an advantage.
Why Formulith
From scattered trial-and-error to guided, multi-objective search.
The usual way
With Formulith
Grid / DOE / spreadsheets
Guided search of the space that matters
One metric at a time
Multi-objective trade-offs, made explicit
Knowledge trapped in people
Knowledge captured as a reusable asset
Data in someone else's cloud
Data stays in your environment
Formulation IP is often too sensitive for multi-tenant AI. Formulith is built for self-hosted, customer-controlled deployment — data, models, and feature pipelines stay inside your infrastructure.
Customer-controlled infrastructure
Deploy inside your own environment — self-hosted, under your operational control.
Sensitive data stays private
Experimental data and formulation IP never have to leave your infrastructure to power the optimizer.
Built for enterprise & regulated R&D
Compatible with the access controls and operational expectations of regulated R&D environments.
Your data is your edge. It should stay yours.
Use cases
Built for campaigns where every experiment is expensive.
Semiconductor wet-process chemistries
Etchants, CMP slurries, and cleans — optimized across etch/removal rate, selectivity, and defect targets at once.
UV-curable adhesives & resins
Balance cure speed, adhesion, viscosity, and yellowing instead of chasing one property at a time.
Functional & conductive inks
Trade off conductivity, viscosity, adhesion, and sintering temperature across a constrained formulation space.
Specialty dyes & colorants
Hit color-match targets under fastness and stability constraints.
and more — catalysts, battery electrolytes, coatings… any expensive, multi-objective formulation campaign.
Contact
Work with Formulith.
If your team faces constrained formulation search, multi-objective trade-offs, or lab decision-support bottlenecks, reach out for a working session.
To make each experiment count.