Oqelvia.
Food science intelligence for high-integrity product decisions.
Analyze composition, compare formulations, monitor quality, and review model-supported insights in one scientific workspace.
Prototype 04 shows improved protein density and lower moisture relative to the selected baseline. Review texture and sensory-panel results before finalizing the formulation.
Decision-support output — review before acting
Turn food data into clearer formulations, deeper analysis, and faster product-development decisions.
Oqelvia is designed as an analytical and decision-support layer for food-science teams — not a replacement for them. Models surface structure in complex datasets; qualified scientists decide what it means.
Food Analysis
Analyze nutritional, physicochemical, ingredient, and laboratory datasets against configurable specifications.
/02Formulation Intelligence
Compare formulations, model ingredient changes, and explore optimization scenarios side by side.
/03Quality Intelligence
Analyze batch and QC data to identify trends, anomalies, and potential quality deviations.
/04Predictive Insights
Preview of planned predictive models for historical product and process data. Shown with demo data only; no trained model is live yet.
/05Sensory Intelligence
Import panel results and analyse them with standard methods: hedonic ratings and ISO 4120 triangle tests.
/06R&D Workspace
Keep formulations, experiments, datasets, notes, analysis, and decisions organized in one environment.
Scientifically readable views of your product data.
Composition comparisons, ingredient contribution, sensory profiles and batch trends — rendered for interpretation rather than decoration.
g / 100 g · demo data
% of formulation · demo data
Panel mean, 0–9 scale · demo data
Reducing date syrup by 8% may lower total sugar while maintaining the current formulation structure. Validate texture and sensory acceptance experimentally.
Decision-support output — review before acting
Built for evidence-driven food science.
Analytical outputs are only useful when their inputs, models, and limitations are visible. Every insight in the platform is presented for review by qualified food-science, quality, regulatory, or R&D professionals.
Data transparency
Every analysis references the dataset, record count, and parameters it was derived from.
Model transparency
Model type, version, training date, and evaluation metrics are shown alongside outputs.
Human review
AI-generated insights are decision-support outputs, presented for review rather than as conclusions.
Reproducible analysis
Analyses record their inputs and configuration so results can be re-run and compared.
Configurable specifications
Reference ranges are defined by your team, per product and per parameter.
Secure workspaces
Production plans call for isolated workspaces and server-enforced role-based access; these controls are not active in this MVP preview.
Explore the workspace with demo data.
Create an account to get your own private workspace, with labelled sample modules for the analysis, formulation, QC and model workflows.