Get to the formula that holds up
A formula has to satisfy a sensory target, a cost ceiling, a regulatory boundary, a supply reality, and a base it will sit in for two years. Formulation Development compresses that from a sequence of bench cycles into a single optimisation loop, with performance and stability predicted alongside the formula that produces them.
One value stream, both industries. The workflow and the platform are shared; the data, constraints, and sensory targets are specific to your business.
Generate, optimise, and predict in one loop
Start from the brief
Match an incoming brief against your formula archive, prioritise what is worth the bench time, and generate candidates from a target profile.
Optimise inside real constraints
Cost, regulation, allergens, customer restrictions, and sustainability targets are enforced during optimisation, not checked afterwards
Predict how it will behave
Stability, longevity, intensity, and physical properties are predicted before the formula reaches the lab bench.
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.
Sensory profile reformulation
Rebuild an existing formula to a new constraint while holding the sensory profile the market already knows.
Formula simplification
Cut the ingredient count without losing the profile, reducing cost, complexity, and supply exposure.
Ingredient substitution engine
Find and rank replacements when a material is restricted, delisted, priced out, or unavailable.
Formula optimisation with constraint compliance
Optimise inside your regulatory, cost, allergen, and customer-restriction boundaries, with every constraint enforced.
Optimal dosing & concentration guidance
Recommend dose levels that hit the intended intensity in the finished product, not just in the concentrate.
View more use cases for formulation development
Sustainable & green formulation
Optimise formulas against biodegradability, carbon, and renewable-content targets alongside performance.
Production & supply chain optimisation
Factor supplier availability, lead time, batch cost, and risk into which formula you actually run.
Formula reconstruction from analytical data
Turn GC/MS and other analytical traces into a plausible, ranked reconstruction of the formula behind a sample.
Sensory profile prediction
Predict how a molecule will be perceived and place it in the right sensory family before it is ever made or smelled.
Digital twin for product performance
Simulate how a formula behaves in the finished product, so fewer physical prototypes are needed to answer the question.
Intensity & diffusion mapping
Predict perceived strength and how it develops and spreads over time and use conditions.
Longevity & release prediction
Model how long the profile holds and how it evolves from first impression to end of use.
Flashpoint & physical property prediction
Predict the physical and safety properties that gate transport, storage, and regulatory classification.
Application-specific performance tuning
Tune one formula to the several applications and processes it has to survive.
Stability prediction across bases
Predict interaction, discoloration, and degradation in the base before committing to stability testing.
From brief to a formula that survives the base
Triage the brief, encode the constraints
Incoming briefs are matched against prior work and ranked before anyone starts.
Sensory target, cost ceiling, regulatory boundary, and banned materials become machine-readable constraints.
The model learns from your formula archive, including what failed and why.
Optimise and predict together
Candidate formulas are generated and scored on profile, cost, and compliance at once.
Stability, longevity, and physical properties are predicted for each candidate before the bench.
Validate, deploy, and feed back
Your formulators validate the shortlist; every decision is logged and attributable.
Bench and stability results return to the model, so predictions improve with each project.
What changes when formulation development is AI-driven
Fewer
physical iterations, prediction removes the cycles that only confirm what a model already knew.
Faster
reformulation, restriction and delisting responses move from months to weeks.
Built-in
compliance, regulatory and customer constraints are enforced during optimisation.
Retained
know-how, formulation logic is captured rather than leaving with the formulator.
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.