From Hardware-Locked to Software-Defined: The New Architecture of Intelligent Labs
There are many reasons you may be thinking about (or have already deployed) lab automation. You may need more walk-away time to focus on other tasks in the lab, more consistency from one run to the next, or you need to scale up a workflow without increasing people or space.
Historically, the focus when building an automation solution has been on what best fits the hardware you are currently working with. There are numerous custom solutions built around a specific imager, liquid handler, qPCR instrument, you name it. But with those custom solutions typically come custom software, proprietary data files, and custom software workarounds. What happens when the needs of the lab change? Most automation platforms are ill equipped to provide the flexibility and connectivity to drive a truly dynamic and intelligent lab.
In this article we’ll discuss the historical “hardware-first” approach to automating the lab, the limitations in that approach, and how LINQ enables a software-defined lab.
The Limitations of Hardware-Driven Workcells
Automated workcells are so often defined by the instrumentation being used on the system, not for the experimental outcome itself. Sometimes this comes about simply because there was a package for your instrument that enabled automated loading and unloading. Or maybe your liquid handler has the ability to integrate some instruments on and around the deck.
These are usually great solutions on first deployment, especially if your workflow is centralized on an instrument or two. But as your workflow begins to scale and if you choose to change instrumentation, some issues can quickly arise:
- Purpose-built tables or accessories may need to be heavily modified to accommodate the new use case
- Workflows need to be re-built in new instrument or scheduling software
- Custom scripts for error recovery, data manipulation, and user prompts will likely need to be recreated on the new platform
- System logs, metadata, run history, and more may be trapped in legacy software, requiring manual export and conversion.
- Labware constraints leads to science adapting to requirements rather than machines adapting to science
- High validation and compliance burdens
All of the above can add time, cost, and downtime to implement. Before you know it, that purpose-built platform is getting in the way of best delivering the results you need.
What are Software-Driven Workcells?
When you instead look at a platform from a software perspective, the options for automation change drastically. Not being tethered to specific instruments, software packages, or even hardware configurations will allow you to be flexible as the scale or complexity of your workflow changes. The lab operates as a cohesive system rather than isolated island. Here are just a few examples of what a software-driven approach can provide:
- Flexibility: Instruments from different vendors have strengths and weaknesses. One vendor may have an emphasis on throughput, while another has improved resolution. Defining your platform at the software level allows you to make the right choice for your expected needs and pivot as needed.
- Consistency: The hardest part of experimental setup on an automated platform can often be around initial run configuration and instructions. If each platform was tailored to the hardware, these setup parameters can vary wildly from one island of automation to the next. A software-first approach means that you can build a unified user experience that remains the same across the lab
- Unity: Data from each instrument has historically been siloed, making downstream analytics and machine learning hard to scale. When building a system from a software perspective, the right tools can be developed to liberate the instrument data and combine with lab data to send to LIMS, ELNs, and other data platforms. These data tools can then be easily ported from one project or lab to the next.
- Resilience: Software-driven workflows allow for simpler recovery and re-routes instead of terminal failures. This leads to fewer lost experiments and less downtime.
LINQ for the Software-Defined Lab
The idea of a software-defined lab is at the core of our philosophy with Automata’s LINQ platform. LINQ is a modern suite designed for flexibility at all levels of lab automation:
- Instrument Hardware: LINQ is device agnostic with a constantly growing library of drivers. If an instrument is automation-friendly, we can integrate it. Changing an instrument to meet adapting needs of the lab is simple.
- Platform Hardware: LINQ Bench has a near-infinite number of different configurations, arm positions, transport options, and accessories that can be configured and re-configured rapidly. This means never having to abandon a system because the layout is no longer fit for purpose. It also means being able to start with a system as small as a single LINQ Bench and grow as your needs do.
- Simple User Interface: Our browser-based, no-code interface allows anyone to build simple, powerful workflows. Physical plate movement and data flows can be visualized in the same space. Runs can be monitored easily from anywhere and on nearly any device.
- Powerful Software Tools: When your needs go beyond the typical workflow, a python SDK and REST API allow for unprecedented customization and integration with other software. Tightly couple your scheduling software with LIMS, ELN, Analytical Platforms, AI, and more. Incorporate data analysis tools from third party vendors, or build your own. Users looking to “close the loop” with AI-driven data analysis and method writing can do so with ease.
Redesigning your lab’s OS
Software-defined labs provide power, potential, and simplicity that haven’t been met with legacy hardware-defined systems. If you’re new to lab automation, starting with a robust software-defined platform will unlock a huge number of experimental possibilities and future-proof your lab for years to come. For those with more hardware-defined lab automation, your next expansion or scale-up may be an ideal opportunity to move to a more agile platform.
Regardless of where you are with your lab, we’re happy to show you how LINQ can simplify your work. Meet us at SLAS Boston 2026 to see LINQ in action and to discuss your application in more detail. We can’t wait to show you what possibilities are unlocked with a software-defined lab.
