Showcase image

Chemical AI built from the molecule up.

Turn your lab data into AI that predicts which formulations will hit target specs—before you mix a single batch.

From months of trial-and-error to ranked formulations in days
Upload your own data. Make custom AI models for any property you have data for—viscosity/viscosity flow curves/viscoelasticity, stability, foaming, sensory, adhesion, cost, biodegradability, lubrication, etc., whatever matters to your product. Get a ranked list of formulations predicted to meet your targets.
Showcase image
Model building wizard

Train AI models on your own formulation history. Define what success looks like, upload your own data, and let the system learn what works. Start from pretrained models for common properties or build from scratch.

Showcase image
Multi-objective optimization

Pick your ingredients, set your constraints, define success. The AI explores thousands of combinations and ranks them by how likely they are to hit your targets. Walk into the lab knowing which formulations have the highest odds of success.

Showcase image
Universal molecular encoder

The AI doesn't just crunch numbers—it understands molecular structure. Trained on millions of chemical compounds, it predicts how ingredients interact and what properties they'll create. That means smarter recommendations from less data.

Five steps from your data to launch-ready formulations

Your existing data and a simple workflow that gives you ranked formulations in minutes. Built for R&D teams who need results fast and business leaders who need confidence in the plan.

Talk with a formulation strategist
  1. 01

    Upload your own data

    Bring in chemical structures and lab measurements from your existing formulation history to train the AI on what works.

  2. 02

    Define your model

    Choose classification, regression, or time series depending on the property you want to model, then configure target criteria that align with launch requirements.

  3. 03

    Train with our wizard

    The wizard runs deep learning training with the universal molecular encoder to learn patterns from your data.

  4. 04

    Input your optimization criteria

    Select candidate ingredients, set ranges, add property objectives, and launch the optimizer to explore thousands of formulation combinations.

  5. 05

    Review the best candidates

    Inspect best scores, predicted values per property, and ingredient percentages. Export shortlists to drive lab experiments and briefing decks for commercial stakeholders.

What sets FastFormulator apart

The only platform combining a universal molecular encoder, proprietary complex mixture data, foundational and custom models, and industry-agnostic customization.

Universal molecular encoder

Our chemistry-aware encoder adapts to new properties without starting from scratch, reducing data requirements for custom models by leveraging foundational chemical knowledge.

Pre-trained foundational models

Hit the ground running with models already trained on common properties like viscosity and stability. Finetune from a proven baseline instead of building from zero.

Trained on proprietary complex mixture data

Trained on thousands of proprietary complex formulations, our models understand multi-ingredient interactions in real products, not just isolated compounds like traditional chemical AI.

Custom models that leverage foundational knowledge

Build custom models on your data while benefiting from foundational chemical understanding—faster training with better predictions from smaller datasets.

Your data stays yours

Your proprietary formulation data remains completely siloed and secure. We never mix it into our foundational models. It's used exclusively to train your custom models and stays fully under your control.

True industry flexibility

From cosmetics to coatings, pharmaceuticals to agriculture—one platform adapts to any formulation challenge without industry-specific retraining.

Cut formulation cycles from months to weeks

See how teams are using FastFormulator to reduce failed experiments, accelerate time to market, and make product decisions backed by predictive AI instead of guesswork.