Scientific organizations today rely on a range of software solutions—Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), and Scientific Data Management Systems (SDMS)—to manage data, document experiments, and enable collaboration. These tools have played an essential role in digitizing scientific workflows, and for many teams, they continue to serve core needs. However, they were never designed to provide the real-time data connectivity, cross-functional intelligence, or AI-powered insights that are increasingly central to modern R&D.
Rather than replacing these systems, there is an opportunity to build on their strengths by integrating them into a unified, intelligent framework that supports multimodal scientific workflows and future-ready discovery.
ELNs: Flexible But Fragmented
Originally designed to support domain-specific needs—particularly in chemistry—ELNs have proven invaluable for documenting experiments and enabling collaboration. However, they were not built to manage structured, real-time data or integrate seamlessly with AI and automation. For many research teams ELNs remain foundational, but their value can be significantly extended through platforms that offer advanced analytics, experimental traceability, and intelligent data flow across teams.
LIMS: Structured Data, Disconnected Science
LIMS provide strong structure and excel at tracking samples and results, particularly in automated lab environments. However, they lack an inherent concept of an experiment, making it impossible to map scientific workflows in a way that reflects real-world lab processes. LIMS can associate large datasets with sample IDs, but provide no context for what those samples represent, nor can they trace the upstream steps that may have influenced results.
SDMS: Data Repositories Without Context
Similarly, SDMS systems, designed for managing and archiving scientific data, play a crucial role in centralizing instrument outputs and raw data files. However, they primarily serve as repositories rather than active workflow management tools. They lack the ability to contextualize data within the broader scientific process, making it difficult to correlate raw instrument data with experimental decisions and outcomes. Despite SDMS ensuring data retention and compliance, they do not inherently support real-time data connectivity or workflow-driven insights.
To enable agile innovation and seamless scientific collaboration, digital workflows must allow tasks and data to move fluidly between traditionally siloed systems—adapting to evolving research needs and enabling AI-driven discovery.
Emerging Market Alert: A Multimodal Scientific Intelligence Platform
According to Gartner, by 2027, nearly 75% of life science organizations will adopt cloud-based composable architectures to power AI-driven research. These organizations are preparing for a world where the drug discovery cycle—from preclinical through commercialization—takes less than seven years. Other companies report that using AI-driven approaches they have already compressed the discovery phase down to from 9 to 18 months.
The message is clear: science needs to move faster, and data needs to move with it. But today’s software forces scientists to make a difficult choice: move quickly and risk accuracy, or slow down to ensure precision. Rarely can they do both.
In this new era of data-driven science, teams need a platform that unifies all aspects of the research lifecycle, allowing for seamless data flow, adaptive workflows, and cross-functional collaboration. The Multimodal Scientific Intelligence Platform is a new category of software designed specifically to address these challenges. By linking every step of the research cycle—Design, Make, Test, Decide—and ensuring that the inputs and outputs at each step are connected and accessible in real-time, the Multimodal Scientific Intelligence Platform unlocks the full potential of research data.
This is where Dotmatics Luma comes in—not as a replacement, but as a bridge to the future. With Luma, data doesn’t just support research, it actively drives it.
Luma is the first of its kind in this emerging market. It represents the next logical step in scientific R&D, designed to future-proof organizations by integrating the best capabilities of ELNs, LIMS, and SDMS into a unified, intelligent platform. This isn’t just better software; it’s a new way of working. One that makes AI possible, cross-functional collaboration natural, and innovation scalable.
Luma is built specifically to bridge the gaps that traditional systems leave behind. This flexibility empowers scientists to explore multiple paths simultaneously, pivot based on emerging data, and standardize best practices across programs and teams. It’s also purpose-built to interoperate seamlessly with any existing system, from ELN to LIMS to SDMS, enhancing their capabilities through real-time data connectivity, intelligent workflow orchestration, and cross-functional insights.
Double-Click: What Makes Luma So Different?
Luma isn’t just a platform—it’s a new architecture for scientific discovery.
Luma enables researchers to model all of their processes—including both dry lab and wet lab operations—as a cohesive digital environment. Every research step is precisely tracked and can be adapted in real time, without relying on complex service-based configurations. Scientists create and update workflows on their own, building a “digital twin” of their end-to-end workflows with greater precision than a typical ELN and more flexibility than LIMS tools.
Luma maintains relationships between material inputs and outputs across steps and automatically links raw and processed data (including simulation inputs and outputs). This creates a detailed, connected record of end-to-end scientific processes, supporting faster, data-driven decisions across therapeutic areas. Plus, these capabilities are delivered without compromising record-keeping and compliance requirements. Data with high levels of detail can also be exported to ELNs in a compatible format reducing the burden of duplicate entry and supporting downstream requirements for IP documentation and compliance.
Each therapeutic mode comes with its own set of unique complexities, from living cells and multiple protein formats to RNA, small molecules, and their combinations. The workflows required to make, validate, and purify these materials are complex, with some processes involving over 100 steps. Luma takes a different approach, delivering a platform that adapts gracefully across scientific modes and domains. Its combination of adaptive process modeling and data automation allows it to scale with the demands of multimodal science—bridging gaps with a precision that ELNs and LIMS were never designed to address. By unifying fragmented research processes into a connected, data-driven environment, Luma is purpose-built for the complexity of modern R&D. It adapts to your workflows, scales with your needs, and evolves as your research advances.
With Luma, scientific organizations can protect their existing investments while unlocking new levels of efficiency, collaboration, and discovery—at their own pace and on their own terms. That means no longer choosing between speed and accuracy. Decisions become faster because they’re smarter, and collaboration isn’t a workaround, it’s built in by design.
The result? Luma transforms research from fragmented to fluid. It’s not just a tool—it’s how modern science moves forward.
Interested in learning more? Read the full “Enabling the Lab-in-a-Loop” ebook.