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    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.

    Request a PilotExplore Meallions OS

    AI explores and evaluates. Experts approve and decide.

    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

    1. 01Bring research, supplier data, formulations, and test results into one workspace
    2. 02Convert product briefs into measurable constraints
    3. 03Generate and compare more viable formulation candidates
    4. 04Evaluate technical and economic constraints together
    5. 05Identify missing evidence before physical testing
    6. 06Design fewer, more informative experiments
    7. 07Improve laboratory-to-production transfer
    8. 08Preserve decisions, failures, and verified results for future projects

    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.

    Product briefMachine-readable requirementsConnected evidence searchCandidate generationMulti-constraint screeningEquipment-specific simulationExpert selectionControlled physical experimentPredicted-versus-actual comparisonReusable learning

    • 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.

    • 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

    Measurements tracked during a pilot—not promised outcomes.

    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

    The value is not producing thousands of answers. The value is identifying the few answers worth testing.

    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.

    Request a Pilot