Find the molecule before you make it
Flavour compounds and fragrance materials are the same problem in different clothing: a chemical space too large to search by hand, a sensory outcome you cannot measure until you synthesise, and a regulatory gate at the end. Ingredient Discovery moves prediction to the front, so candidates arrive at the bench already screened for character, performance, and compliance.
One value stream, both industries. The workflow and the platform are shared; the data, constraints, and sensory targets are specific to your business.
Search, predict, and screen in one workflow
Explore the space
Generate and rank candidate molecules and natural-source materials far beyond what manual search reaches, scored against your target profile.
Predict the sensory outcome
Model taste, aroma, and olfactory character from structure, so you know roughly what a candidate does before it exists.
Performance in the base
Toxicology signals and GRAS, EFSA, IFRA, and regional screening run alongside discovery instead of after it.
What formulation development covers
From brief intake through generation, optimisation, and performance prediction. These apply across both Flavour & Food and Fragrance & Personal Care: the loop is the same, the constraints, the base, and the sensory target change.
Intelligent brief matching
Match an incoming brief against your formula archive, so an existing formula is adapted instead of a new one built from zero.
Brief Prioritisation
Rank incoming briefs by winnability, margin, and technical fit, so formulation capacity goes to the ones worth the bench time.
AI-Enhanced formula generation
Generate candidate formulas from a target brief, ranked against your own historical formulation data.
From research question to validated candidate
Define the target profile
Your flavourists and perfumers set the sensory target, constraints, and no-go materials.
The model is grounded in your compound library, supplier specs, and sensory records.
Generate and rank candidates
Generative chemistry proposes structures and natural-source alternatives against the target.
Every candidate carries a predicted profile and a confidence signal, not a bare score.
Screen, hand over, and learn
Safety, regulatory, and supply screening filter the list before it reaches the bench.
Bench results feed back, so every cycle sharpens the next one.
What changes when ingredient discovery is AI-driven
Wider
search space, explore far more candidate molecules and materials than manual research allows.
Earlier
dead-end detection, safety, regulatory, and performance risks surface before bench work.
Compounding
R&D knowledge, every screening cycle enriches your institutional knowledge base.
Your data
stays yours, discovery runs inside your secure environment, never a shared model.
Each one feeds the next
Four forces shape almost every conversation we have with biopharma teams. Each one is a documentation and data problem before it is a technology problem.