We’ve previously announced that Model Context Protocol (MCP) is now available in Sophia AI. And we also mentioned it’s an open standard that allows AI applications to connect with external systems and information.
But if you work in a laboratory, your first question is probably: “What does this actually mean for my lab?”
What matters is what greater AI connectivity could mean for the way your team works with laboratory information.
In this article, we’ll look at what AI connectivity means in practical, and how Sophia can help your team not only find information, but take action in QLIMS.
What does AI connectivity mean for your lab?
AI tools have become increasingly good at understanding questions, summarising information and generating responses.
But there is an important limitation: an AI model cannot automatically know what is happening inside your laboratory. Your laboratory data still lives in the systems your team uses every day, including your LIMS, instruments, databases and other software.
This is where MCP, or Model Context Protocol, comes in.
For a laboratory, that means AI can move beyond simply talking about information and start working with the information your team already uses.
1. What if you could spend less time searching for information?
Laboratory teams work with large amounts of information every day. Finding one answer can sometimes mean moving through records, filters, screens and different parts of a system before you have what you need.
What if you could simply ask?
“Which samples are still waiting for review?”
“Show me the batches associated with this order.”
“Which methods were used for these samples?”
Instead of changing how your laboratory stores information, the goal is to make that information easier to access.
With Sophia, you can ask questions in natural language and get information from your QLIMS data without having to manually search through every record. But finding information is only part of the story.
2. What if Sophia could understand the context around your question?
Laboratory information rarely exists on its own. A sample can be connected to an order. An order can be connected to a batch. A method can be associated with testing. Materials are used in laboratory processes. Instruments support the work being carried out.
So the useful answer is often not just one piece of information. It is the context around it.
That is where connected AI can become more useful in a laboratory.
Instead of simply asking for a record, you could ask questions such as:
“Which samples are part of this batch?”
“Which method is associated with these samples?”
“What materials are connected to this work?”
“Which instruments are involved in this process?”
The aim is to make it easier to move between the information that already exists in your laboratory system and understand how it relates.
3. What if you could ask Sophia to actually do something?
This is where everything gets interesting. Sophia doesn’t only answer questions, it can also take actions in QLIMS. So what could that look like in a real laboratory?
“Create samples from this information”
“Create a new method for this test”
“Register this new material”
“Create a worklist for these samples”
“Show me the information related to this OOS investigation”
“Read my attached handwritten notes, and first confirm with me, then create those samples”
Imagine being able to ask these questions, get the information you need, and take the next step without navigating through multiple screens or repeating manual tasks. The real value of AI connectivity is the time and effort it can save your team.
And of course, acting on laboratory data comes with responsibility. Sophia’s actions are built within the same security and governance model that protects your lab in QLIMS, with actions and changes recorded through the QLIMS audit trail.
So, how much time could your laboratory save by simply asking?
Book a demo and see what Sophia AI can do for your lab.
