Turning complex toxicology AI into a clear, usable product experience

For a toxicology AI product moving towards commercialisation, the interface cannot simply expose raw model output. A prediction may be technically meaningful yet still be difficult to interpret, explain or incorporate into a professional workflow if the front end does not provide the right structure.

DioScor needed to communicate cross-species and organ-level predictions, model confidence, structural attribution, biomarker and mechanism context, known evidence, and the boundary between curated data and model output. It also had to remain scientifically responsible: a prediction does not replace regulatory testing or guarantee safety.

Working from Consone AI's scientific requirements, defined product direction and subject-matter expertise, Veda AI designed and developed the front-end experience. The work focused on information architecture, interaction flow, visual hierarchy and the implementation needed to present complex outputs clearly.

Veda AI did not define Consone AI's commercial strategy, scientific model or product claims. The contribution was translating those established inputs into a practical interface journey: molecular input, exposure route, prediction summary, cross-species comparison, structural and mechanism interpretation, weight of evidence and exportable reporting.

• Interface architecture — Organising a complex scientific workflow into a clear sequence of screens and decisions.
• Explainability presentation — Layering summary, confidence and evidence without making claims beyond the supplied science.
• Front-end delivery — Building the responsive product interface that makes the defined DioScor proposition tangible.

Users do not receive an unexplained answer. They can move from a summary prediction into confidence, organ-level risk, species comparisons, contributing molecular regions, biomarker mechanisms and similar known compounds.

The weight-of-evidence component lets model output sit alongside related known compounds, similarity values, species, endpoints and applicability context rather than presenting a prediction in isolation.

Consone AI supplied the science, predictive capability and product proposition. Veda AI turned those inputs into a structured, visual and reportable front-end that could be demonstrated, reviewed and prepared for launch.

The delivered interface makes the intended product journey tangible without claiming ownership of the underlying commercial strategy or model. Real-world adoption, commercial performance and scientific outcome metrics will only be claimed once the platform is live and evidence exists.