How Dotmatics ships new workflows in days with Sigma

Dotmatics made its name on hyper-specialized point solutions, but the harder problem was unifying the disparate data those tools produce inside Luma, its lab orchestration platform. A homegrown visualization layer couldn't scale, so Dotmatics embedded Sigma and replaced code with configuration: demos and go-lives ship in days to weeks, and scientists explore vendor-neutral data and commit decisions back into the platform.

The Challenge

Dotmatics is a company built by scientists for scientists, producing top point solutions from flow cytometry to chromatography. As research organizations scaled, a strategic gap appeared: none of it could be connected. Dotmatics answered with Luma, its Databricks-based orchestration platform, which collates data across sources and lays the foundation for future AI work.

Dotmatics' expertise was in data management and analysis, but a platform doesn't feel real to a scientist until they see their own data in it. The team's bespoke visualization worked as a demo, but every change lived in the code base, meaning long release cycles and maintenance that pulled engineering attention from the science Dotmatics knew best.

The Solution

Dotmatics needed a configuration-based layer that plugged into its existing architecture and owned the final mile of visualization. Sigma became that layer inside Luma: Dotmatics shapes the data, and Sigma is where customers see, explore, and act on it. Sigma's plugin architecture let the team build domain-specific extensions, and Actions write those views back into Luma.

Instrument data is the clearest example: labs receive the same research in different vendor formats, and Dotmatics' parsing engine extracts each one before Databricks and Luma harmonize the results into one model. Sigma presents that vendor-neutral result on screen, so a scientist can examine peaks from an instrument like an X-ray diffractometer, select what matters, and commit the decision back to the database, kicking off downstream processes without leaving the screen.

Rolling this out changes the question customers bring to the table. Science, like healthcare, is used to static reports, so Dotmatics opens each engagement by asking what an ideal workflow looks like, rather than rebuilding whatever the current system does. Teams start simple and grow toward automated, AI-assisted decision-making as workflows mature.

The Results

Because delivery is configuration rather than code, Dotmatics pulls together demos and go-lives in days to weeks, where changes once meant longer release cycles and heavier maintenance. That matches Luma's own config-based ethos. For customers, it means going live faster than competing vendors and at higher quality than an internally built tool, with the automated pipeline making the decisions themselves hold up better.

The biggest thing we're seeing is higher quality decisions that are being made faster, with a lot lower overhead to implement and maintain.

— Michael Fritz, Principal Product Manager, Lab Orchestration, Dotmatics

Dotmatics would miss the governance and configurability most if the layer went away. Drag-and-drop workbook building lets people who are computational but not software engineers, which describes much of the life sciences workforce, ship real analysis without giving up the quality controls clients expect. The stakes justify the rigor: a bad decision here can end with a suboptimal drug reaching a patient. Rebuilding that in-house would be possible, but it would pull focus from Dotmatics' core competencies and slow its path to market.

Now part of Siemens, Dotmatics is moving downstream into development, manufacturing, and scale-up, and beyond life sciences into other domains. The team is watching Sigma's AI work closely: AI-assisted workbook building to speed up search and filtering workflows and extend go-lives past its services team, and natural-language exploration through Sigma Assistant to surface insights the scale of scientific data would otherwise hide. Workbooks as Code is the most anticipated engineering change, a lower-risk way to move app configurations across clients and tenants.