Lab Orchestration: A Practical Guide to the Connected Lab

What we're here to do

The discoveries that change lives start in a lab. Getting them from that lab to the people who need them is one of the most important challenges of our time.

It's also harder than it needs to be.

Dotmatics exists to change that. By connecting experiments, data, and decisions into a continuous scientific record, we help labs move faster, build on their own work more effectively, and bring the innovations that matter most to market sooner.

That's the promise of lab orchestration. And that's what this guide is about.

• Faster innovation from molecule to market

• Reproducible, explainable science you can build on

• AI that works on a foundation worth trusting

• Scale without fragility

• A scientific record where the thread never breaks

Introduction: The work is harder than it needs to be

Scientists don't get into science to manage data pipelines.

They get into it to discover something. To run the experiment, interpret the result, and follow the thread wherever it leads. The work itself is hard enough: the biology is complex, the chemistry is not always predictable, the timelines are unforgiving. Nobody needs the infrastructure to make it harder.

But for most scientists working in labs, the infrastructure does make it harder. Not because the tools are bad. In many cases, the tools are extraordinary. The instruments are more sensitive than ever. The software is more capable. The data being generated would have been unimaginable a generation ago.

The problem is that none of it was designed to seamlessly connect and communicate to the rest of it.

So after the experiment runs, a second job begins. Data gets exported, reformatted, renamed, and moved by hand. Context that was obvious at the bench (which sample, which protocol, which run condition) gets separated from the result it belongs to. Weeks later, when someone asks how a number was generated, the answer requires archaeology.

Connecting the context isn't a niche problem. It's one of the most consistent friction points across scientific organizations of every size, at every stage of discovery. And as labs scale, as instrument fleets grow, as AI initiatives multiply, the friction compounds.

That's what this guide is about.

Not the instruments. Not the software. Not even the data itself. It's about the connective tissue between all of those things: the layer that keeps scientific work coherent as it moves from the first experiment all the way to the decisions that carry it to market.

That layer is called lab orchestration. And building it isn't as far away as it might seem.

Every lab is somewhere on this journey. Some are just beginning to feel the friction of fragmentation. Others have been solving pieces of the problem for years and are ready to connect them. This guide is written for all of them. As you read, you'll find moments to reflect on where your lab is now and what the next step forward looks like from there.

Why this matters beyond the bench

The scientists living with this problem feel it most acutely. But the consequences extend well beyond any individual experiment or researcher. When scientific data loses context between systems, organizations lose the ability to reproduce results reliably. Regulatory submissions become harder to defend. AI initiatives run on foundations too fragmented to produce outputs worth trusting.

And the pace of discovery, the speed at which an idea moves from hypothesis to clinical candidate to market, slows in ways that are difficult to measure but impossible to ignore.

The pressure to move faster has never been greater. Discovery cycles are long and expensive. The competition for scientific talent is fierce. The promise of AI in the lab is real, but it depends entirely on the quality and continuity of the data beneath it.

Organizations that figure out how to connect their science will move faster, waste less, and build on their own work more effectively than those that don't.

This guide is a practical starting point for that journey.

Chapter 1: The physical lab, where science begins and context first gets lost

Walk into a well-equipped research lab and the first thing you notice is the scale of it.

Instruments line the benches, mass spectrometers, liquid handlers, plate readers, sequencers, and chromatography systems. Each one, a specialized tool optimized for a specific kind of measurement or precise step. In larger organizations, automated workcells run experiments in parallel, robotic arms move samples between stations, and autonomous mobile robots ferry materials across the floor. Scheduling software coordinates the choreography. Barcodes track what goes where.

It is, in many ways, a remarkable achievement. Labs can now run experiments at a pace and volume that would have been impossible to contemplate even a decade ago. The physical infrastructure of science has never been more capable.

And yet, nearly every scientist working in this environment will tell you the same thing: the moment an experiment finishes, something gets lost.

The gap between generation and capture

Every instrument in the lab produces data. But producing data and capturing it in a useful form are two very different things.

Most instruments were designed to measure or perform intrinsically, not necessarily to communicate and interact well externally. They output results in vendor-specific formats: proprietary file types, inconsistent naming conventions, data structures that made sense to the instrument manufacturer but were never designed to integrate with anything else. Getting that data out of the instrument and into a place where it can be analyzed requires a step that is, in most labs, still largely manual.

Files get exported. Folders get organized. Spreadsheets get updated. Names get standardized, or don't. And somewhere in that process, the experimental context that was obvious at the bench starts to separate from the result it belongs to.

Which sample was in which well? Which protocol version was used? What were the run conditions? What decision was made after this result came in? That information exists in someone's notebook, in an email thread, in the scientist's memory, but it isn't traveling with the data. It's already starting to drift.

Automation adds power, but also complexity

The rise of lab automation has dramatically increased what's possible in the physical lab. Workcells running in parallel. Overnight runs with no human in the loop. Throughput that scales with the science rather than with the number of hands available.

But it's worth naming something important: many labs around the world are not there yet. In many research environments, scientists are still the connective tissue of the entire process. They move samples by hand. They operate each instrument individually. They enter data at each step. This is not a failure of ambition. It is simply the reality of how science gets done today, and it is a reality that lab orchestration is equally built for.

Whether a scientist is manually carrying a sample from one instrument to the next or watching a robotic arm do it for them, the problem at the end of the experiment is the same: data that was generated across multiple steps, in multiple formats, without a continuous record connecting all of it together.

And for labs that are more automated, scale introduces its own version of the problem. More instruments mean more formats to reconcile. More workcells mean more handoffs where context can be dropped. More throughput means more results that need to be traced back to their origin.

Automation without orchestration is a faster way to generate data you can't fully use. And manual workflows without orchestration are a harder way to generate data you can trust.

What the physical lab needs

The physical lab doesn't need to be rebuilt. The instruments are good. The automation is valuable. The scheduling software is doing its job.

What it needs is a reliable connection to everything around it. A way for data to flow in and out of the physical lab with its context intact, whether that means carrying results forward into analysis or carrying decisions back into the next experiment.

That connection is what the next layer of orchestration is designed to provide.

Chapter 2: The digital lab, powerful tools, incomplete picture

If the physical lab is where science happens, the digital lab is where it gets interpreted. 

Electronic lab notebooks capture experimental records. Laboratory information management systems track samples and workflows. Data analytics platforms surface patterns across large datasets. Visualization tools turn numbers into insight. AI models predict outcomes, suggest next steps, and accelerate the design-make-test-analyze cycle.

The digital toolkit available to scientists today is genuinely impressive. And most of it was built in isolation.

The problem with point solutions

Every digital tool in the lab was designed to solve a specific problem well. An ELN is optimized for capturing experimental records. A LIMS is optimized for sample tracking. An analytics platform is optimized for interrogating large datasets. These are real capabilities and they deliver real value.

But when each tool is optimized for its own domain, the connections between them become the scientist's problem to manage. Data gets exported from one system and imported into another. Formats get translated. Fields get remapped. And at each handoff, the risk of losing context or introducing error grows.

The result is a digital environment that is rich in capability but fragmented in practice. Scientists spend time being translators between systems rather than interpreters of results. And the bigger the organization, the more systems there are to translate between.

Why digital tools alone can't solve the problem

There's a pattern worth naming here. Many organizations have responded to lab fragmentation by adding more digital tools, like a new analytics platform, a better data lake, and a more capable ELN. The instinct is understandable. If the problem is that data isn't usable, better software should help.

But the problem isn't usually the software. It's the gap between the software and the physical reality of the lab. Digital tools, however capable, can only work with the data they receive. If that data arrives stripped of context, formatted inconsistently, and disconnected from the experimental record it belongs to, no amount of analytical sophistication can fully compensate.

This is the fundamental limitation of approaching orchestration from the digital side alone. You can build a sophisticated data platform, but if it isn't reliably connected to the instruments generating the data, and if the data arriving doesn't carry the context needed to make it meaningful, the platform is only as useful as the quality of the inputs feeding it.

What the digital lab needs

The digital tools in most labs aren't the problem. They're underconnected.

What the digital layer needs is a foundation that ensures data arrives already structured, contextualized, and harmonized, regardless of which instrument produced it, which format it came in, or which system it passed through on the way. A foundation where the ELN, the LIMS, the analytics platform, and the AI model are all working from the same coherent scientific record rather than their own isolated slice of it.

That foundation requires something that sits between the physical and digital layers, and extends across both.

If you've ever found yourself manually bridging two systems that should already talk to each other, or wondering why a perfectly good analytics platform still can't give you the full picture, you've felt this gap firsthand. The tools aren't the problem. The missing piece is something most labs haven't been given a name for yet. That's what the next chapter is about.

Chapter 3: The logical layer, the missing piece

The previous two chapters described two things most scientists and lab leaders already recognize: the physical lab is powerful but poorly connected, and the digital lab is capable but fragmented.

Fix the connection between them and the problem is solved. Except it isn't. Not quite.

Because even when instruments are reliably feeding data into digital systems, and even when those systems are sharing information with each other, something is still missing. The data is there. The results are there. But the reasoning behind them, including the intent, the rules, the decisions, often isn't.

That's the logical layer. And it's the part of lab orchestration that is least understood, most often overlooked, and most consequential when it's absent.

What the logical layer actually is

The logical layer is everything that gives scientific work its meaning and continuity beyond the raw data.

• It includes the experimental intent (i.e. why this experiment was designed the way it was, what question it was trying to answer, what hypothesis it was testing.

• It includes the business rules (i.e. the SOPs, the approval workflows, the compliance requirements, the decision criteria that determine what happens next after a result comes in).

• It includes the decisions themselves. Not just what was measured, but what was concluded, what was acted on, and why.

None of this is captured automatically by instruments. Most of it isn't captured reliably by digital tools either. It lives in people's heads, in emails, in meeting notes, in the institutional knowledge of whoever ran the experiment. And when that person moves to a different project, or leaves the organization, it often goes with them.

Why this is where reproducibility breaks down

Reproducibility is one of the most persistent challenges in scientific research. Studies get questioned. Results can't be replicated. Regulatory submissions get challenged. And in most cases, the underlying cause is missing context.

When the logical layer isn't captured, reproducing an experiment means reconstructing not just the physical steps but the reasoning behind them. Which protocol version was current at the time? What exception was made and why? What did the scientist know when they made that decision that someone reading the record six months later wouldn't know?

Without the logical layer, a scientific record is a collection of outputs. With it, it becomes something you can actually build on.

The logical layer and AI

This is also where the promise of AI in scientific discovery either gets realized or falls apart.

AI models are good at finding patterns in data, but patterns without context produce insights without meaning. An AI model trained on experimental results that aren't anchored to their intent, their conditions, and their decision logic will find correlations, but it won't be able to tell you whether those correlations are meaningful, reproducible, or worth acting on.

The logical layer is what transforms a dataset into a scientific record. And a scientific record is what AI actually needs to support reasoning rather than just pattern matching.

What the logical layer needs

The logical layer can't be bolted on after the fact. It has to be captured continuously, as science happens, embedded in the workflows, the data structures, and the systems that scientists are already using.

This matters not just within the lab, but across the entire journey from molecule to market. Every handoff in that journey, from discovery to development, from development to scale-up, from scale-up to manufacturing, is a moment where context can be lost and progress can stall. Organizations that preserve the logical layer aren't just running better experiments. They're building a continuous scientific record that travels with the science all the way to the finish line.

If you've ever struggled to explain a result not because the data was wrong, but because the reasoning behind it lived in someone's head, or an email thread, or nowhere at all, you've felt the absence of the logical layer. Most labs have. It's the part of scientific work that has always been the hardest to capture, and the most costly to lose. The good news is that it's also the part that, once preserved, changes everything about what becomes possible.

Chapter 4: Bringing it all together, what the connected lab makes possible

The previous three chapters described a problem in three parts. The physical lab generates data that loses context on the way out. The digital lab has the tools to analyze that data but can't fully compensate for what's missing when it arrives. And the logical layer, including the intent, the decisions, the reasoning behind the science, often isn't captured at all.

Separately, each of the gaps between the physical, digital, and logical parts of discovery is a friction point. Together, they describe something more significant: a scientific organization that is working harder than it needs to, building on foundations that are less stable than they appear, and moving more slowly than the science itself demands.

Orchestration is what closes those gaps. Not by replacing what's already working, but by connecting it.

What changes when the lab is connected

When physical, digital, and logical layers are orchestrated into a single continuous scientific record, the experience of working in the lab changes in ways that are immediately felt by the people closest to the work.

Scientists stop spending time on the second job. Data capture becomes automatic. Context travels with results rather than being reconstructed after the fact. When a colleague asks how a number was generated, the answer is in the record, and not in someone's memory.

Experiments become reproducible not because scientists are more careful, but because the system is designed to preserve everything needed to reproduce them. Results become explainable not because reports are written more thoroughly, but because the reasoning behind them was captured as the work happened.

And when the time comes to hand work from one team to another, like from discovery to development, from one site to another, from one phase of research to the next, the handoff carries the full scientific record with it. Not just the results. The intent, the decisions, and the context that make those results meaningful.

What changes for the organization

The benefits of a connected lab extend well beyond any individual scientist or experiment.

• Discovery moves faster. When scientists spend less time managing data and more time interpreting it, the design-make-test-analyze cycle accelerates. Experiments build on each other more effectively. Dead ends get identified sooner. Promising leads get followed faster.

• AI becomes trustworthy. AI models operating on a structured, contextualized, continuously updated scientific record produce outputs worth acting on. Not because the models are better, but because the foundation beneath them is. The difference between AI that accelerates discovery and AI that generates outputs you can't trust often comes down to the quality and continuity of the data it's working with.

• Scale stops being fragile. Organizations that grow their instrument fleets, expand their automation, and add new teams and sites without solving the orchestration problem accumulate fragility. Every new system is another potential point of disconnection. Orchestration changes the relationship between scale and complexity: more instruments, more workflows, more teams don't mean more fragility. They mean more data flowing into the same coherent record.

• The molecule to market journey gets shorter. This is the ambition that sits behind all of it. Every phase of the journey from discovery to market is a potential handoff where context gets lost and progress stalls. Organizations that maintain a continuous scientific record across that entire journey move faster, waste less, and build on their own work more effectively than those that don't.

• The overall costs are reduced by shortening timelines, obtaining around-the-clock productivity, increasing utilization of assets, requiring less scientist touchpoints, minimizing lost data, re-work, and re-training of staff

This is not the lab of the future. It's your lab, now.

The industry has spent years talking about the lab of the future: a vision of fully connected, fully automated, AI-driven scientific discovery that always seems to be just around the corner. That framing, however well-intentioned, has made the connected lab feel like something you build toward rather than something you build now.

It isn't. The technology to connect your lab exists today. The instruments generating your data are already there. The digital systems doing your analytical work are already running. The workflows your scientists follow every day already contain the logical layer waiting to be captured. Orchestration doesn't ask you to wait for the future. It asks you to connect what you already have, and start getting more out of it immediately.

The connected lab isn't a vision. It's a decision.

The pressure to move faster, discover more reliably, and bring better science to market sooner is real. And the labs that figure out how to keep their science connected will have a genuine advantage, not just in efficiency, but in the quality of what they're able to build on. The connected lab isn't a vision reserved for the most sophisticated organizations. It's a decision available to any lab willing to start. The next chapter is about what that actually looks like in practice.

Chapter 5: How Dotmatics Luma is different

Most of the solutions available to labs today were built to solve one part of the problem. They do that part well. But because they were designed from a single direction, either from the physical lab upward, or from the digital layer downward, they tend to leave the other half of the picture incomplete.

Understanding where those gaps are is the fastest way to understand why Luma's position is different.

The approach from the bottom up

Lab automation integrators have spent decades solving the hardest part of the physical problem. Connecting instruments. Building workcells. Integrating scheduling software. Getting robots and liquid handlers and barcode readers to work together reliably at scale. That expertise is real and it is hard-won.

But automation integrators typically approach the lab from the physical layer up. Their primary domain is execution: making sure the right thing happens at the right instrument at the right time. The data layer above that execution is often an afterthought, addressed through older technology stacks that were built when the digital demands on labs were far simpler than they are today.

The result is a physical environment that runs smoothly but feeds data into a digital layer that is underserved. Instruments are connected to each other. They are not always connected to the analytical systems, the AI platforms, and the decision-making workflows that give their outputs meaning.

The approach from the top down

Modern scientific data platforms approach the problem from the opposite direction. They are built for the digital domain: data management, analytics, visualization, and AI. They are often sophisticated, well-designed, and capable of doing genuinely powerful things with scientific data.

But they tend to focus on a niche within the digital layer, and their connection to the physical reality of the lab, including the instruments, the workcells, the scheduling software, the barcode readers, is limited. They can analyze data that arrives in their system, but they don't always have reliable, structured, context-rich pipelines connecting them to the instruments generating that data in the first place.

The result is a digital environment that is analytically capable but physically incomplete. The platform is powerful. The data feeding it is not always trustworthy enough to use that power fully.

Luma: orchestrating the entire system

Luma was designed to sit at the center of both layers, and to connect them. Rather than approaching the problem from the physical layer up or the digital layer down, Luma operates across the full physical-digital environment. Luma connects to the physical lab, capturing data continuously from diverse instrument fleets regardless of vendor or format, harmonizing it into structured, FAIR-aligned representations, and delivering it into Luma with experimental context intact. Your existing schedulers and automation systems continue to direct physical execution. Luma ensures that everything those systems produce flows forward coherently into the digital layer where it can be used.

Luma itself sits at the heart of that digital and logical layers, bringing together data management, analytics, visualization, and AI in a governed, enterprise-grade environment.

And critically, Luma does this without replacing what's already working. Existing instruments stay. Existing schedulers stay. Existing ELNs and LIMS stay. Existing AI models stay. Luma connects to all of them, providing the orchestration layer that keeps everything coherent without asking organizations to rebuild their stack from scratch.

Luma's capabilities Luma is the lab orchestration platform at the center of your connected lab. Its capabilities grow with your organization:

• Agentic AI: built-in AI that works directly on your scientific record to support data exploration, pattern recognition, and decision support. Connect your own models, too.

• Digitalized scientific workflows: design, automate, and scale lab workflows that reflect how your science actually happens.

• Material and ontology management: register and track materials, samples, and entities with full traceability across programs and sites.

• Data management and business rules: embed the logic of how your organization works directly into the platform, so decisions and context travel with the data.

• Visualizations and dashboards: surface insights across workflows, programs, and teams in configurable, real-time views built for scientific decision-making.

• Open, extensible ecosystem: connect third-party tools, proprietary AI models, and custom systems through open APIs and a configurable integration framework.

What makes this position credible

Two things make Luma's position in this space credible in a way that is difficult for others to replicate.

The Dotmatics portfolio. Luma isn't a connectivity layer sitting on top of someone else's scientific tools. It's the orchestration platform at the center of a portfolio that includes some of the most widely used scientific software in the world, including GraphPad Prism, Geneious, Virscidian, OMIQ, and more. When Luma connects to those tools, it isn't doing integration work. It's activating a native scientific ecosystem designed to work together.

Siemens. Dotmatics is part of Siemens, one of the few organizations in the world with a presence across the entire molecule to market journey, from discovery through development, tech transfer, and manufacturing. That means the orchestration foundation being built in the research lab today is designed to connect to the broader Siemens ecosystem as science moves downstream. The thread doesn't just run through the lab. It runs all the way to market.

Open by design

One more thing worth stating clearly: Luma is not a closed ecosystem.

The goal of orchestration is connection, not control. Luma is designed to work with the tools organizations already rely on, including competitors, third-party platforms, proprietary AI models, and custom-built systems. Open APIs, FAIR-aligned data structures, and a configurable integration framework mean that connecting to Luma doesn't mean locking into Luma.

Organizations retain the freedom to choose their instruments, their schedulers, their analytics tools, and their AI strategies. Luma provides the layer that keeps all of those choices coherent, without making itself the only option at any point in the stack.

For the scientist, none of this architecture matters unless it means the data is there when they need it, the context hasn't been lost, and the next experiment can build on the last. That's the standard Luma is built to meet.

Chapter 6: How to get started

Every lab is at a different point in its journey toward orchestration. Some are just beginning to feel the friction of fragmentation. Others have been working on pieces of the problem for years and are ready to connect them. A few are further along, running sophisticated automation, investing heavily in AI, and looking for the foundation that makes all of it more reliable.

Wherever you are, the path forward starts with an honest assessment of where you are today.

The lab digital maturity framework

Orchestration is a capability you can build, and organizations tend to move through recognizable stages as they develop it.

• Stage 1: Fragmented. Instruments, spreadsheets, shared drives, email. Data capture is largely manual and context is lost routinely. You might know the friction personally because you're living it every day.

• Stage 2: Partially Connected. Some instruments feed data into digital systems automatically and some workflows have been digitalized. But coverage is inconsistent and the spreadsheets haven't gone away yet. The fragmentation is reduced but not resolved.

• Stage 3: Structured. Data capture is reliable and consistent across most of your instrument fleet. Data arrives in structured, harmonized formats and reproducibility has genuinely improved. This is the turning point. The foundation is ready. Now the question is what to build on it.

• Stage 4: Harmonized. Physical, digital, and logical layers are connected with meaningful relationships drawn across systems. Experimental context travels with results. AI is becoming a credible part of the discovery process rather than an aspiration.

• Stage 5: Orchestrated and AI-ready.  
  The connected lab feeds a closed-loop discovery system that continuously learns. Experiments inform decisions. Decisions inform the next experiments. Scientific knowledge compounds over time rather than fragmenting at every handoff

Most labs today sit somewhere between Stage 1 and Stage 3. Orchestration is the move that takes you from wherever you are toward Stage 4 and beyond. Luma is designed to meet you at your current stage and grow with you from there.

The stages above give you a map. The questions below help you find your place on it. You don't need to answer all of them. Read through and notice which ones make you pause. Those are the ones worth sitting with.

Questions worth asking before you begin

Before bringing a solution into the conversation, it helps to understand where your organization actually is. These questions are designed to surface the conversations worth having internally, and to give you a clearer picture of where the biggest gaps are.

About your data:

• When an experiment finishes, how many different places does that data end up?

• How much of your data capture is still manual: exports, renames, uploads, copy-paste?

• If someone asked you to pull the complete record of an experiment run six months ago, how long would that take?

About your workflows:

• How much of your experimental workflow lives in people's heads or paper SOPs rather than in a system that travels with the science?

• When a scientist leaves or moves to a different project, what happens to the knowledge they accumulated?

• How difficult is it to reproduce a result from a previous program, not just the data, but the full context of how it was generated?

About your digital infrastructure:

• Do your ELN, LIMS, analytics tools, and AI platforms share a common data foundation, or are they each working from their own slice of the picture?

• How much of your team's energy goes into making systems talk to each other, rather than interpreting what those systems produce?

About AI:

• Is your organization being asked to bring AI into the scientific process? If so, how confident are you that the data foundation beneath that initiative is structured and trustworthy enough to support it?

• If AI is already part of your workflows, is it working on the full context of your scientific record, or only on the parts that happen to be accessible?

About scale:

• As your instrument fleet grows and your automation expands, does your data infrastructure get more reliable or more fragile?

• If you needed to replicate your current lab setup at a new site, how long would it take to stand up the same data flows?

A simple path forward

If the questions above surfaced something you recognized, that recognition is your starting point. You don't need a comprehensive transformation plan. You need one honest answer to one question: where is the biggest gap between where your data is and where it needs to be? Start there.

• Step 1: Assess  
  Start with an honest picture of where you are. Use the questions from earlier in this chapter to map your current data flows, identify where context is being lost, and understand which instruments and systems are connected, and which aren't. The goal isn't a comprehensive audit. It's enough clarity to know where the biggest gaps are and where a connection would deliver the most immediate value.

• Step 2: Connect  
  Pick a starting point and connect it. This might be a single instrument type, a specific workflow, or a particular data handoff that is causing the most friction. Orchestration doesn't require connecting everything at once. It requires connecting something reliably, with context intact, and building from there. Early wins create momentum and demonstrate value to the broader organization.

• Step 3: Grow  
  Expand coverage systematically. As more instruments, workflows, and systems connect to the orchestration layer, the scientific record becomes more complete and more useful. AI becomes more trustworthy. Reproducibility improves. The logical layer starts to accumulate. And the organization begins to move from wherever it started on the maturity arc toward the connected, continuously learning lab that makes the molecule to market journey faster and more reliable. Every instrument connected, every workflow digitalized, every handoff that stops dropping context is a move up the maturity journey. You don't climb it all at once. You climb it one reliable connection at a time.

Who to bring into the conversation

Orchestration touches every part of the scientific organization. The most successful implementations start with a small group of people who represent the right mix of scientific, operational, and technical perspectives. Here is who to include.

• The scientist 
  The person closest to the work. They feel the friction of fragmentation most acutely and have the clearest picture of where context is being lost. Their buy-in matters enormously. Orchestration only works if the people running experiments trust it and use it.

• The lab digitalization leader  
  Accountable for the infrastructure that makes the lab run faster. They understand the complexity of the current environment, including its the schedulers, instruments, custom integrations. They are best positioned to evaluate what a new orchestration layer means for the existing stack.

• The R&D director  
  Accountable for scientific output at scale. They feel the organizational consequences of fragmentation when results can't be explained, knowledge walks out the door, or AI investments underdeliver. They are often the person who needs to make the internal case for investment.

• IT and data engineering  
  The team responsible for making sure any new platform meets enterprise standards for security, scalability, and governance. Bring them in early. Their concerns about data lineage, auditability, and integration risk are legitimate. Luma is designed to address those concerns.

• An executive sponsor  
  For larger organizations, an executive sponsor, like a CSO or VP of R&D, gives the orchestration initiative the organizational weight it needs to move across teams and sites. The molecule to market vision resonates at this level in a way that a feature comparison doesn't.

The next step

Whatever the questions above surfaced for you, the most useful next step isn't a purchase decision. It's a conversation about where you are and what becoming more connected would actually mean for your work. Dotmatics works with scientific organizations at every stage of this journey, not to sell a destination, but to help labs find their next step forward from wherever they're standing today.

See Luma in action at dotmatics.com/luma

Talk to our team at dotmatics.com/contact

Conclusion: The connected lab and the journey ahead

There is a version of scientific discovery that most people working in labs can imagine but few have fully experienced.

A version where the experiment finishes and the data is already where it needs to be: structured, contextualized, and ready to build on. Where the next scientist to pick up the work doesn't have to reconstruct what happened; it's all in the record. Where AI accelerates the cycle not because the models are smarter, but because the foundation beneath them is trustworthy enough to act on. Where the knowledge generated in the discovery lab doesn't get lost at the handoff to development, or at the handoff to scale-up, or at any of the other moments where hard-won scientific progress quietly disappears.

A version where the journey from molecule to market is faster, because the system was designed to keep science coherent all the way through.

That version is closer than most organizations think.

What's actually at stake

The pressure to discover faster, innovate more reliably, and bring life-changing science to market sooner has never been greater. The cost of drug discovery remains staggering. The timelines are long. The competition is fierce. And the promise of genuinely transformative AI, not just pattern-matching on incomplete data, depends entirely on building the kind of connected scientific foundation that most labs don't yet have.

The organizations that close that gap first won't just run more efficient labs. They'll compound their scientific knowledge in ways that disconnected organizations can't. They'll build on their own work more effectively. They'll move faster through the phases that have historically been defined by friction and handoff failure. And they'll arrive at the decisions that matter, like which molecule to advance, which target to pursue, which hypothesis to test next, with more confidence and less wasted motion.

The thread that runs through it all

Lab orchestration is a commitment to keeping science coherent from the first experiment to the final filing, across every system, every handoff, and every team involved in moving a discovery toward the world.

The physical lab generates the data. The digital lab interprets it. The logical layer gives it meaning. And when all three are connected continuously, reliably, with context intact, the result is a scientific organization that learns from everything it does, builds on everything it knows, and moves at the pace the science deserves.

Whatever stage your lab is at today, the journey toward a connected scientific record is the same journey. It just starts from a different place.

That is what the connected lab makes possible. And it starts with a single decision: to stop accepting fragmentation as the cost of doing science.