Dimenso

The Challenge

Scientific research still depends on manual, fragmented, and post-facto documentation. Even in laboratories using modern ELN and LIMS systems, researchers often capture critical experimental context after the work has already happened, relying on memory, handwritten notes, screenshots, spreadsheets, or disconnected files. This creates a structural bottleneck for biotechnology, pharma, and academic research. Scientists lose valuable time on administrative documentation, experimental context is often incomplete, and critical details that determine reproducibility can be lost. As AI becomes increasingly important in drug discovery and scientific R&D, this problem becomes even more urgent: models and scientific agents can only be as good as the data they receive.

The Solution

Dimenso captures scientific work in real time using voice, computer vision, and lab-specific AI models. Researchers can document experiments hands-free while working at the bench, with Dimenso capturing observations, actions, deviations, timestamps, sample context, and workflow metadata as the experiment happens. The platform then transforms raw voice and visual signals into structured scientific records with provenance and auditability. Instead of replacing existing systems, Dimenso sits on top of laboratory infrastructure and syncs with ELN and LIMS tools, making platforms such as Benchling or LabArchives more useful by feeding them cleaner, richer, real-time data. Dimenso’s long-term vision is to become the invisible interface between scientists and AI: a capture layer that turns every experiment into high-quality, structured data for collaboration, reproducibility, compliance, and AI-driven scientific discovery.

Why We Invested