01
Bring research, supplier data, formulations, and test results into one workspace
For food scientists and product-development teams
Evaluate more product candidates. Take only the strongest to the line.
Meallions turns fragmented research, specifications, formulation knowledge, and prior results into ranked, explainable product candidates—then evaluates them on the equipment where they must ultimately run.
AI explores and evaluates. Experts approve and decide.
Candidate ranking · evidence confidence
Illustrative example
C-014Simulated
Fit score 0.91
Evidence coverage 86%
Required next action: Schedule trial
C-007Simulated
Fit score 0.86
Evidence coverage 74%
Required next action: Confirm die geometry
C-022Generated
Fit score 0.74
Evidence coverage 41%
Required next action: Missing sensory data
C-031Generated
Fit score 0.62
Evidence coverage 55%
Required next action: Cost above target
Generated is not verified. Simulated is not production-proven. Experts select which candidates proceed to a physical experiment.
Who might benefit from the platform
- Corporate food R&D teams
- Product-development laboratories
- University and applied-research laboratories
- Innovation centers
- Contract R&D organizations
- Ingredient application laboratories
Customer tasks
What these teams need to solve.
02
Convert product briefs into measurable constraints
03
Generate and compare more viable formulation candidates
04
Evaluate technical and economic constraints together
05
Identify missing evidence before physical testing
06
Design fewer, more informative experiments
07
Improve laboratory-to-production transfer
08
Preserve decisions, failures, and verified results for future projects
Value proposition
Replace repeated trial-and-error with evidence-driven candidate selection.
Meallions does not replace food scientists or release products automatically. It handles repeatable research, calculation, comparison, and documentation work so experts can focus on scientific judgment, experiment design, exceptions, and approval.
Connected evidence
Research, ingredients, supplier specifications, equipment knowledge, previous projects, and production data.
Multi-constraint formulation
Evaluate nutrition, sensory targets, cost, ingredient availability, regulatory requirements, and processability together.
Explainable candidate ranking
Show why a candidate is recommended, its evidence level, confidence, missing data, and required next action.
Stronger scale-up readiness
Evaluate candidates against the target production line before scheduling a physical run.
Relevant modules
The parts of Meallions OS this segment uses.
Research + Learning
Structured research, ingredient and supplier specifications, prior projects, equipment knowledge, and reviewed production results in one workspace.
Recipe Builder
Generates and ranks candidates against nutrition, sensory, regulatory, cost, ingredient availability, yield, and processability constraints.
Digital Twin
Simulates a selected candidate on the target line and predicts a feasible operating window.
Verification
Compares predicted and measured results, preserves approvals, and blocks candidates outside agreed limits.
Supporting feedback loop: Production returns reviewed real-world results to the R&D knowledge base.
How the workflow operates
One governed sequence, end to end.
Product briefMachine-readable requirementsConnected evidence searchCandidate generationMulti-constraint screeningEquipment-specific simulationExpert selectionControlled physical experimentPredicted-versus-actual comparisonReusable learning
“The value is not producing thousands of answers. The value is identifying the few answers worth testing.”
Practical use cases
Where teams start.
New formulation development
Create and rank candidates against technical, nutritional, sensory, economic, and equipment constraints.
Reformulation
Change protein, sugar, fiber, fortification, cost, or ingredient composition while protecting processability.
Ingredient substitution
Evaluate new suppliers, alternative ingredients, or locally available crops before physical trials.
Experiment planning
Identify high-value experiments and missing evidence instead of using the production line as the primary search method.
Laboratory-to-production scale-up
Evaluate whether a promising laboratory formulation can run on a specific commercial line.
Organizational knowledge retention
Convert research decisions, test results, exceptions, and verified outcomes into reusable institutional knowledge.
Pilot scope and indicators
What a pilot measures.
Each indicator is tracked against your own baseline during the pilot. Generated is not verified. Simulated is not production-proven.
Research and evidence-collection time
Number of candidates evaluated
Number of candidates physically tested
Number of physical confirmation runs
Candidate confidence and evidence coverage
Recipe readiness for production
Prediction-versus-actual difference
Knowledge reused from previous projects
Time from brief to approved test plan
Meallions pilot target
Measurements tracked during a pilot—not promised outcomes.
AI and human responsibilities
AI executes the repeatable work. Experts decide.
What AI does
- Search and structure research, specifications, and prior project results
- Generate, screen, and rank candidates against stated constraints
- Show confidence, evidence level, and missing data per candidate
- Draft documentation and experiment records
What experts control
- Define the brief, constraints, and acceptance criteria
- Apply scientific judgment to candidates and evidence gaps
- Design and approve physical experiments
- Own safety, compliance, exceptions, and product release
Experts control objectives, scientific judgment, exceptions, safety, approval, and product release. Meallions does not release products automatically.
Platform and showcase
Go deeper.
R&D Teams
Map your current workflow, then decide what to automate.
We map how your team collects evidence, builds candidates, and runs trials today — then show which steps Meallions can execute and which stay with your scientists.