Compare chemical R&D data effectively
Consolidate data. Evaluate variants. Make confident decisions.
In chemical R&D, process parameters, raw materials and experimental conditions are continuously adjusted. Temperature, concentration, catalysts and reaction times influence quality, yield and material properties.
LabV brings together experimental, analytical and process data within a central structure. This enables systematic evaluation of variations and targeted control of optimisation cycles.
Data exists.
But it isn’t usable.
Which parameters improved yield? How does temperature influence product purity? Has this raw material combination already been tested?
Experimental protocols are stored in Excel files, analytical data in separate systems, and reports as PDFs. Comparing results across experimental series and batches becomes time-consuming.
Without a structured data context, experiments are repeated instead of systematically refined, trends remain hidden, and the economic impact of parameter changes is difficult to assess.
One platform.
All R&D data.
LabV links process parameters, analytical data and batch information within a consistent structure. Changes remain traceable, as does their impact on quality and performance.
The result: shorter optimisation cycles, transparently documented results, and decisions based on complete data sets.
Strukturierte Daten von Anfang an erfassen und verknüpfen
Jede Änderung an Formulierung oder Prozess erzeugt neue Daten. Damit diese projektübergreifend auswertbar sind, müssen Prozessparameter, Messdaten und Analysedaten von Anfang an verknüpft erfasst werden. Es reicht nicht, diese nachträglich zusammenzuführen.
LabV strukturiert diese Daten so, dass der Zusammenhang zwischen Formulierungsentscheidung und Testergebnis über Chargen, Iterationen und Projekte hinweg jederzeit nachvollziehbar bleibt. Dies bildet die Grundlage für eine belastbare Datenverknüpfung und eine iterative Entwicklung.
AI supports. The team decides.
Based on structured data, LabV helps identify patterns within experimental series, assess deviations between batches, and evaluate the impact of parameter changes more precisely.
Relationships between parameters and quality become visible.
The AI provides orientation. The decision remains with the development team.
Experience live
how formulation data becomes systematically usable.
Experience in a short demo session how LabV brings together formulations, test data and variant knowledge in paint and coatings development.