Category Archives: 2026

2026, Sophia AI

Can You Trust AI with Your Laboratory Data? How Sophia AI Is Governed in QLIMS

Posted on by Ezgi Ozer
Can You Trust AI with Your Laboratory Data
12
Oct

Security is one of the first topics that comes up in almost every demo, evaluation, and conversation we have with labs considering agentic AI. The moment an AI can act, not just answer questions, a different question takes over: “Is it safe?”

It’s the right question to ask. Laboratory systems hold sensitive operational data and support workflows where permissions, traceability and accountability matter. Introducing AI into those workflows requires more than useful capabilities. It requires clear boundaries around what AI can access, what it can do and how its actions are recorded.

Sophia AI is built to operate within the existing security and governance model of QLIMS. Its interactions with the system are governed by authentication, user permissions and audit trails, helping laboratories explore AI-assisted workflows without treating security as an afterthought.

Here’s what that means in practice. 

Authentication & Access Control

  • Zero credential exposure. You never provide a username or password to Sophia. Authentication is handled automatically and securely in the background through tokenized sessions.
  • Role-based access control (RBAC). Every action Sophia performs is governed by your existing QLIMS permissions. She can only do what your account is authorised to do, nothing more.
  • Session-scoped. All API interactions are tied to your authenticated session, preventing any unauthorised access.

Data Security

  • No data storage. Sophia does not store, log, or retain your laboratory data, sample information, or credentials between sessions.
  • Encrypted communication. All interactions with the QLIMS Action API occur over encrypted channels (HTTPS/TLS).
  • Audit trails. Every action performed in QLIMS, whether by a person or by Sophia, is fully logged and traceable in the system’s audit trail, ensuring complete accountability.

Compliance & Governance

  • 21 CFR Part 11 / GxP ready. QLIMS is built for regulated laboratory environments, so every action Sophia takes is auditable and traceable.
  • No autonomous overreach. Sophia follows strict directives. She never creates, modifies, or deletes records without your explicit instruction, and she always asks permission before creating new identifiers or entities.
  • Transparent actions. Before executing any significant operation, Sophia walks you through exactly what will be done.

Security is part of making AI useful

AI has the potential to reduce administrative work and help laboratory teams interact with their systems more efficiently. But capability alone isn’t enough. Laboratories also need confidence in how AI accesses information, performs tasks and leaves a traceable record of its activity.

Sophia AI brings AI-assisted workflows into QLIMS’s existing security and permission framework, helping teams explore what AI can do while keeping access, control and accountability central to the process.

Curious how Sophia AI could support your laboratory workflows? Contact us for a personalised walkthrough and explore how its capabilities could fit your lab’s needs.

2026, laboratory management

The Audit Survival Guide for Lab Managers

Posted on by Ezgi Ozer
the audit guide for lab managers
08
Oct

Audits are part of laboratory life. Whether it’s ISO 17025, GLP, GMP, CAP, or an internal quality audit, the pressure tends to arrive in the same way: Where’s that record? Who approved this? Can we prove when this happened?

An audit doesn’t have to mean a last-minute scramble through spreadsheets, emails and paper files. The best way to survive an audit is to make audit readiness part of the everyday workflow, rather than something you prepare for once a year.

 

1. Don’t wait for the audit to find the gaps

A common mistake is treating audit preparation as a project with a start and end date.

Instead, regularly check whether your lab can answer basic questions such as:

  • Can we trace a sample from receipt to result?
  • Can we show who performed and approved each step?
  • Are equipment calibrations and maintenance up to date?
  • Can we quickly retrieve the relevant SOP, method or record?
  • Can we demonstrate that deviations and corrective actions were followed through?

If answering these questions takes many hours, that’s your first warning sign.

 

2. Make traceability easy

Auditors want evidence. Your team shouldn’t have to build that evidence from scratch.

Sample history, test results, approvals, equipment records, methods and changes should be connected and easy to trace. A centralised laboratory system can help create that audit trail automatically, rather than relying on someone remembering where every document lives.

The goal isn’t more documentation. It’s better-connected documentation.

 

3. Keep equipment records under control

Calibration and maintenance records are classic audit checkpoints and easy to overlook when they’re spread across spreadsheets, folders or paper logs.

Set up reminders for upcoming calibration and maintenance dates, and make the relevant records accessible from the equipment’s history. That way, your team can see what’s due before it becomes an audit finding.

 

4. Treat deviations and corrective actions as a workflow

Finding a problem isn’t the end of the process. Auditors also want to know what happened afterwards.

A strong process should make it clear:

What happened → why it happened → what was done → who approved it → was it effective?

When these steps are tracked in one place, it’s much easier to demonstrate that your lab doesn’t just identify problems, it manages them.

 

5. Use your LIMS as your audit trail 

Instead of relying on separate spreadsheets, shared folders and paper records, a LIMS brings key laboratory information into one controlled environment.

Depending on how your system is configured, you can use it to:

  • Track samples and their complete history
  • Record who performed, reviewed and approved activities
  • Manage methods, workflows and results
  • Keep equipment calibration and maintenance records
  • Track deviations, non-conformances and corrective actions
  • Maintain electronic audit trails
  • Control access and permissions
  • Retrieve records quickly when evidence is requested

The important point isn’t simply having a LIMS. It’s having your LIMS reflect how your lab actually works.

A well-configured system should make compliance part of the workflow, rather than creating another layer of administration for your team.

 

Audit-ready shouldn’t mean audit panic

The strongest labs don’t become organised because an auditor is coming. They build processes that keep them organised all year.

With the right systems in place, audit preparation becomes less about hunting for evidence and more about showing the evidence that’s already there. And that’s a much better place to be when the auditor walks through the door.

Contact us to see how we can help you with your audit. 

2026, Sophia AI

What Does AI Connectivity Mean for Your Lab?

Posted on by Ezgi Ozer
OnQ Software - What does AI connectivity mean for your lab
22
Sep

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.

2026, Sophia AI

Sophia AI Unlocked a New Level in the Laboratory of the Future: MCP Brings True Automation to the Lab

Posted on by Ezgi Ozer
Sophia AI
16
Sep

Until recently, the conversation about “AI in the lab” was mainly focused on natural language search. However, new trends in AI are taking everything to the next level, so the QLIMS team thought: why not laboratories? 

We’re excited to announce a major addition to Sophia AI, the agentic AI built into QLIMS: MCP (Model Context Protocol) is now available.

MCP is an open standard, created by Anthropic, for connecting AI to the tools and systems it works with. It acts like a universal “USB-C port” for connecting artificial intelligence models to external data sources, tools, and workflows. In practice, it’s what turns Sophia from an assistant that tells you things into an agent that does things, acting directly and securely inside your lab environment.

The integration of the Model Context Protocol (MCP) into QLIMS marks a major shift for laboratory informatics. By connecting QLIMS directly to AI agent architectures, OnQ Software enables true Lab-in-the-Loop workflows and sets the foundation for fully autonomous scientific labs.

From Search to Action

For most of the labs, “AI” still refers to asking a question and getting an answer, however, labs need more than getting answers. Imagine that you could ask your AI assistant to create the samples for you, and Sophia AI goes even beyond it with reading your attached handwritten notes. 

With MCP integration, Sophia now can perform real laboratory actions on your behalf, right from a conversation. 

What Sophia Can Now Do

Through MCP, Sophia can act across the entire lab workflow:

  • Sample & Order Management: Create and manage orders (batches) and samples from scratch, or directly from documents/OCR (Optical Character Recognition) input.
  • Analytical Management: Create, approve, and duplicate methods and sample types.
  • Inventory Control: Register new materials, track physical containers, and record reagent consumption.
  • Instrumentation: Monitor hardware health and decommission faulty instruments.
  • Execution & Worklists: Build batch runs (worklists), assign samples to positions, and enter test results in bulk.
  • Compliance & Audit: Manage OOS (Out-of-Specification) investigations and retrieve full audit trails for any record.

Security is one of the biggest topics we’re asked about in demos, and in evaluations. Sophia’s ability to act is built on the same security and governance model that already protects your lab in QLIMS. 


Curious what Sophia could do in your lab? Step into the laboratory of the future today and contact us for a personalised walkthrough. Every lab is different, and we’d love to show you how Sophia adapts to yours. 

2026, LIMS

The Hidden Cost of Spreadsheet-Based Lab Data

Posted on by Ezgi Ozer
The Hidden Cost of Spreadsheet-Based Lab Data
01
Sep

Spreadsheets are everywhere in laboratories. They’re familiar, flexible, and easy to start with. But as a laboratory grows, spreadsheets can quietly become one of its biggest operational costs. And the real cost isn’t the spreadsheet itself.

It’s everything that happens around it. In this blog article, we’ll dive into the hidden cost of spreadsheet-based lab data management. 

1. Manual data entry creates more opportunities for error

Copying information from one spreadsheet to another may seem harmless. But when sample IDs, test results, QC data, dates, or processing steps are entered manually, every additional touchpoint creates another opportunity for human error.

The errors like misplaced digit, or copied value in a wrong row can be difficult to identify after the fact, particularly when data moves between multiple people and systems.

The more samples a laboratory processes, the harder this becomes to manage manually.

2. Time spent searching for data adds up

How long does it take your team to find a particular sample record or confirm who processed a sample?

A few minutes here and there may not seem significant.

But multiply those minutes across a laboratory team, every day, and the hidden cost becomes much more substantial.

3. Version control becomes a problem

“Which spreadsheet is the latest one?” It’s a simple question, but it can create a surprisingly complicated problem.

When multiple people download, edit, email, and save copies of the same file, maintaining a single source of truth becomes difficult.

And when the wrong version is used, correcting the problem can take even more time.

A centralised system removes much of this uncertainty by keeping data in one controlled environment.

4. Traceability becomes harder as workflows grow

Modern laboratory workflows rarely involve a single step. A sample may move through accessioning, preparation, QC, testing, sequencing, analysis, review and reporting.

When each stage is tracked in separate spreadsheets, connecting the complete history of a sample becomes increasingly difficult.

You may have the information. But can you see the complete story?

5. Integration becomes increasingly difficult

Laboratories often rely on multiple instruments and software platforms.

When data has to be manually transferred between them, your team effectively becomes the integration layer.

Instrument → spreadsheet → another spreadsheet → analysis platform → report.

Every manual handoff adds time and another potential point of failure.

With appropriate integrations, data can move between systems with less manual intervention, reducing repetitive work and improving consistency.

6. The biggest cost: what your team could be doing instead

This is the hidden cost that is easiest to overlook.

Every hour spent:

  • searching for sample information
  • checking spreadsheets
  • copying data
  • reconciling records
  • fixing manual errors
  • chasing missing information
  • preparing reports manually

is an hour your laboratory could have spent on higher-value work.

The question isn’t simply:

“How much does our spreadsheet process cost?”

It’s:

“What could our team achieve if they didn’t have to manage it manually?”

From spreadsheets to a connected laboratory workflow

A Laboratory Information Management System (LIMS) is not simply a digital replacement for a spreadsheet. A modern LIMS can connect samples, workflows, instruments, users, QC, results and reporting in one environment.

Instead of asking your team to keep the data connected manually, the system can help connect the workflow for them.

That means fewer manual handoffs, better traceability, less duplicated work and a clearer view of laboratory operations.

What is spreadsheet-based data really costing your laboratory? 

Instead of guessing what a move to a LIMS could mean for your laboratory, calculate the potential ROI for yourself.

Use our LIMS ROI Calculator to estimate how much time, money and effort your laboratory could save by moving away from manual processes and towards a connected laboratory information management system.

If your team is spending more time managing laboratory data than working with it, it may be time to look at what a modern LIMS could change.

Contact us today for less admin, more science.

2026, LIMS

How to Choose a LIMS for a Genomics Laboratory: 12 Questions to Ask Vendors – Part 2

Posted on by Ezgi Ozer
How to Choose a LIMS for a Genomics Laboratory Part 2
25
Aug

The right LIMS needs to support more than your current workflows. It also needs to provide the flexibility, scalability and infrastructure to support your laboratory as it evolves.

In Part 1, we looked at six questions focused on the core capabilities of a genomics LIMS. But selecting a LIMS is a long-term decision. Before committing to a platform, you also need to consider how it will perform as your laboratory grows and how much control your team will have after implementation.

In Part 2, we explore six more questions to ask before choosing a LIMS, covering traceability and audit history, scalability, cloud infrastructure, reporting, vendor dependency and total cost of ownership. Here are six more questions to add to your LIMS evaluation checklist.

7. Can the system provide complete traceability and audit history?

Genomics laboratories need to know not only what happened to a sample, but also who performed an action, when it occurred and what changed.

This becomes increasingly important as laboratories scale and workflows become more complex.

Ask whether the LIMS provides:

  • Audit trails
  • User activity history
  • Role-based access
  • Controlled data changes
  • Traceability across sample and workflow

For regulated laboratories, also consider the specific compliance requirements relevant to your environment.

8. Can it scale with our laboratory?

A LIMS that works for a small laboratory may not necessarily be suitable when sample volumes, users, instruments and workflows increase. Think beyond today’s requirements.

Ask the vendor:

  • What happens when sample volume doubles?
  • Can we add new workflows?
  • Can we support multiple laboratory sites?
  • Can we add new instruments?
  • Can the system handle increasing data volumes?
  • How does the infrastructure scale?

Scalability should not mean buying a completely new system when the laboratory reaches its next stage of growth. The right platform should provide a foundation that can evolve with the laboratory.

9. Is the LIMS suitable for our IT strategy?

Cloud-based and SaaS LIMS platforms can offer significant advantages for laboratories that want to reduce infrastructure management and make their systems easier to scale.

Ask:

  • Where is laboratory data hosted?
  • How is data protected?
  • How are backups managed?
  • What is the disaster recovery approach?
  • How are software updates handled?
  • What access controls are available?
  • Can the deployment meet our data residency requirements?
  • What responsibility remains with our IT team?

For organisations already using cloud infrastructure, it can also be useful to understand how the LIMS fits into the broader cloud environment.

For example, QLIMS is delivered as a SaaS platform and hosted on AWS, providing a cloud-based foundation for laboratories looking to modernise their laboratory information infrastructure.

10. Can our laboratory access meaningful reporting and operational insights?

A LIMS should not simply collect information. It should help your team understand what is happening in the laboratory.

Consider whether the system can provide visibility into sample status, workflow progress, turnaround times, workload, outstanding samples and operational KPIs. 

Ask vendors to demonstrate dashboards using scenarios relevant to your laboratory.

For example: “Show us which samples are currently waiting for QC.”, or “Show us which workflow stages are creating the longest delays.”

These are much more useful demonstrations than a generic dashboard walkthrough.

11. How much will we depend on the vendor after implementation?

This is one of the questions that is often overlooked during LIMS selection. Implementation is only the beginning of the relationship. Your laboratory will continue to change after go-live. Requirements might change after the implementation. 

So ask:

What can our laboratory team manage itself?

Compare vendors based on configuration capabilities, training, support model and upgrade process. 

A system that gives your laboratory more control can reduce dependence on lengthy vendor development cycles.

12. What is the total cost of ownership?

The initial software price is only one part of the cost of a LIMS.

When comparing solutions, consider implementation, configuration, support, ongoing maintenance and upgrades.  A lower initial price does not necessarily mean a lower total cost.

Likewise, a more expensive system may not deliver better value if your laboratory needs significant custom development for every workflow change.

Look at the full lifecycle of the system and ask:

What will this LIMS cost us to operate and adapt over the next three to five years?

QLIMS For Genomics Labs

Choosing a LIMS for a genomics laboratory is ultimately about more than finding the longest feature list. They need a platform that can connect complex workflows, samples, instruments, quality control, data and reporting while remaining flexible as the laboratory evolves.

QLIMS is a configurable, cloud-based LIMS designed to support complex laboratory environments, including genomics and sequencing workflows. It brings together the capabilities laboratories need to manage their workflows from sample intake through processing, QC, sequencing and reporting.

If you’re currently evaluating LIMS solutions, our guide to the QLIMS for genomics laboratories provides a closer look at the capabilities to consider and how QLIMS can support complex genomics workflows.

Book a demo with us today to see firsthand how we could support you. 

2026, LIMS

How to Choose a LIMS for a Genomics Laboratory: 12 Questions to Ask Vendors – Part 1

Posted on by Ezgi Ozer
How to Choose a LIMS for a Genomics Laboratory Part 1
19
Aug

Choosing a Laboratory Information Management System (LIMS) for a genomics laboratory is not simply a matter of comparing feature lists.

Genomics workflows can involve large numbers of samples, multiple preparation and quality control stages, sequencing instruments, bioinformatics systems, complex sample relationships and strict requirements for traceability and data integrity. As laboratories grow, the LIMS also needs to evolve with changing workflows, technologies and operational requirements.

The right LIMS should do more than store sample information. It should connect the laboratory’s workflows, data, instruments and people while reducing unnecessary manual work.

So, how do you choose the right LIMS for a genomics laboratory?

To make the evaluation process easier, we’ve broken this guide into two parts, covering 12 key questions to ask LIMS vendors before making a decision.

1. Can the LIMS support our entire genomics workflow?

Genomics workflows rarely consist of a single test or process. Depending on the laboratory, a workflow may include:

  • Sample accessioning and registration
  • Sample preparation
  • DNA or RNA extraction
  • Quantification and quality control
  • Library preparation
  • Library QC
  • Sequencing
  • Bioinformatics analysis
  • Results review
  • Reporting

A LIMS should provide visibility across these stages rather than managing each process as a disconnected activity.

When evaluating a system, map your current workflow and ask the vendor to demonstrate how the LIMS would manage it from sample receipt through to the final result.

2. How easily can our laboratory configure the system?

Genomics laboratories change. New assays are introduced, protocols are updated, QC requirements evolve or workflows are refined. If every change requires custom development from the vendor, the LIMS can quickly become a bottleneck.

A configurable LIMS allows laboratory teams to adapt workflows, fields, business rules, and other system components as requirements change.

When evaluating a LIMS, ask:

  • Can our laboratory configure workflows?
  • Can we add or modify fields?
  • Can we create new sample types?
  • Who makes these changes?
  • Do changes require vendor development?
  • What happens when our workflow changes six months after implementation?

For growing genomics laboratories, configurability can be just as important as the initial feature set.

3. Can it provide end-to-end sample tracking?

Sample management is at the heart of a genomics laboratory. A single original sample may generate multiple derived samples, aliquots, libraries or downstream outputs throughout the workflow.

Look for capabilities such as:

  • Unique sample identification
  • Barcode support
  • Sample relationships
  • Aliquot tracking
  • Location tracking
  • Chain of custody
  • Sample status
  • Full audit history

The goal is not simply knowing where a sample is, it is being able to reconstruct its history.

4. Can the LIMS integrate with our laboratory instruments?

Genomics laboratories often operate with a diverse technology ecosystem. This can include sequencing platforms, PCR systems, Qubit, NanoDrop, TapeStation, liquid handling systems or other analytical instruments. 

If laboratory staff need to manually transfer information between instruments and the LIMS, the benefits of digitisation are limited.

Ask vendors:

  • Which instruments can you integrate with?
  • How does the integration work?
  • Can data be transferred automatically?
  • Can the LIMS validate incoming data?
  • Can it connect to new instruments in the future?
  • Does the vendor support APIs or middleware?
  • Who is responsible for maintaining integrations?

A modern LIMS should fit into your laboratory’s technology ecosystem rather than forcing your laboratory to work around the limitations of the system.

5. Can it connect with bioinformatics and other laboratory systems?

Sequencing is only one part of a genomics workflow. The data generated by sequencing may need to move into bioinformatics pipelines, analysis platforms, reporting systems or other applications.

That means LIMS integration should not stop at laboratory instruments.

Consider whether the system can connect with:

  • Bioinformatics platforms
  • Analysis pipelines
  • ELN systems
  • ERP systems
  • External applications

When evaluating a vendor, ask about its API capabilities and integration architecture.

A LIMS with an open and well-supported integration approach can provide more flexibility as your laboratory technology environment changes.

6. How does the LIMS manage quality control?

Quality control is fundamental to genomics workflows. Depending on the workflow, laboratories may need to monitor measurements such as DNA/RNA concentration, purity, fragment size or other assay-specific QC parameters. 

A suitable LIMS should allow laboratories to define and manage QC requirements as part of the workflow. Instead of treating QC as a separate spreadsheet or manual checkpoint, the LIMS can connect QC results with the relevant sample and workflow stage.

Ask vendors:

  • Can QC requirements be configured?
  • Can results be automatically captured from instruments?
  • Can failed QC trigger workflow actions?
  • Can QC information be included in reports and dashboards?

The objective is to make quality control part of the workflow rather than another disconnected administrative task.

If you’re currently evaluating LIMS solutions, our guide to the QLIMS for genomics laboratories provides a closer look at the capabilities to consider and how QLIMS can support complex genomics workflows.

Book a demo with us today to see firsthand how we could support you. 

2026, LIMS

How a LIMS Improves Quality Control in Manufacturing Laboratories

Posted on by Ezgi Ozer
How a LIMS Improves Quality Control in Manufacturing Laboratories
05
Aug

Imagine that a batch is ready to leave the warehouse, waiting on one thing which is lab sign-off on a Friday. Somewhere between a spreadsheet, an inbox and a paper form is the result confirming the product can ship. This isn’t a rare event for manufacturing labs, it’s an everyday consequence of manual quality control.

According to industry research, laboratory staff can spend a significant portion of their day on admin tasks such as manually entering data, searching for records and preparing documentation instead of performing analytical work. Sometimes a compliance officer digs through binders before an audit, hoping nothing’s missing. Sometimes a supervisor manually re-keys the numbers into a report for the plant manager. None of this is because the people running these labs aren’t good at their jobs. It’s because the tools they’re working with were never built for the volume, complexity, or speed manufacturing demands today.  This is where a question comes to mind: Why does LIMS matter for manufacturing LIMS? 

Why LIMS matters and why now

A Laboratory Information Management System (LIMS), exists to take the manual grind out of running a lab: sample tracking, testing workflows, results, compliance records, all in one connected system instead of scattered across paper, inboxes, and spreadsheets.

While production lines have embraced automation, laboratory data is often still managed using paper, spreadsheets and disconnected systems. That gap is becoming harder to ignore.

The labs still relying on paper and spreadsheets aren’t just slower — they’re carrying risk every single shift.

That’s the moment LIMS steps onto the stage: not as a nice-to-have digitisation project, but as the system that lets a manufacturing lab keep pace with the plant around it. (If you’d like to read what a LIMS is, click here.)

 

How a LIMS actually improves quality control

A modern LIMS changes QC on the floor in a few concrete ways:

  • Faster sample turnaround. Digital sample booking and automated routing mean a sample gets logged, assigned, and tracked the moment it hits the lab.
  • Fewer manual errors. Manual data entry creates unnecessary risk. By connecting directly with instruments and capturing results electronically, a LIMS reduces transcription errors, eliminates duplicate entry, and ensures data is linked to the correct sample every time. 
  • Risk-based testing. Not every product or batch requires the same level of testing. A configurable LIMS can apply predefined rules based on product type, risk level, or testing requirements, helping laboratories focus resources where they matter most while maintaining quality standards. 
  • Audit-ready by default. Every sample, test, and result is logged automatically with a full chain of custody, so an audit becomes a report you pull, whenever it is needed. 
  • Real-time visibility. Production leaders and quality leads can see batch status without pinging the lab, so “is it ready to ship?” gets an instant answer instead of a phone call.

Put together, these aren’t just efficiency gains. They’re the difference between QC being a bottleneck and QC being a competitive advantage.

This is where QLIMS comes in

QLIMS is built specifically for labs like these, manufacturing QC labs that need to move fast without losing rigor. It replaces paper forms and Excel trackers with digital sample booking, automated workflows, and no-code configuration, so labs can adapt the system to their processes instead of bending their processes to fit rigid software.

No two manufacturing labs test the same way, and QLIMS is designed around that reality: configurable testing protocols, automated risk-based rules, and reporting that’s ready for an auditor the moment it’s requested. The goal isn’t to add another system on top of the chaos, it’s to remove the chaos underneath the work labs are already doing.

Real labs, real results

Not all LIMS vendors claiming to support precision medicine actually do. Here is what separates platforms that deliver from those that overpromise.

The proof isn’t in the pitch. It’s what happens when manufacturers actually put a LIMS to work.

  • Baiada replaced manual, paper-based sample submissions with QLIMS and freed up 1.5 full-time-equivalent roles that had been consumed by chasing forms and re-entering data. That’s not a marginal saving; that’s over a full headcount redirected toward work that actually needs a human.
  • Comvita moved its technicians off manual Excel spreadsheets and onto QLIMS, and saw a 20% jump in technician efficiency. Same team, same skills, but  just far less time lost to spreadsheet admin.
  • A confectionery manufacturer used QLIMS to automate risk-based testing protocols, cutting down the number of tests run on lower-risk products without compromising on the ones that matter. Less redundant testing, same confidence in the result.

Three different industries. Three different problems. The same underlying story: when the manual work disappears, the lab gets faster, the results get more reliable, and the people running it get their time back.

Every manufacturing laboratory reaches a point where paper forms, spreadsheets and disconnected systems begin to limit performance rather than support it. A modern LIMS helps laboratories spend less time managing information and more time delivering accurate, timely quality results.

If your lab is still running on paper and spreadsheets, the question isn’t whether a LIMS would help. It’s time to evaluate whether your current processes are still keeping pace with your operation. Book a demo with us today to see firsthand how we could support you. 

2026, LIMS

What Is a LIMS for Precision Medicine — And Why Most Labs Get It Wrong

Posted on by Ezgi Ozer
OnQ Software - What does AI connectivity mean for your lab
29
Jun

Precision medicine is rewriting the rules of diagnostics. Genomic sequencing, targeted oncology, and molecular pathology are no longer niche capabilities. They are the new baseline for forward-thinking labs. But the infrastructure holding these labs together? That is often still stuck in the past.

Most laboratory information management systems were designed for routine sample tracking, not the complex, multi-stage workflows that precision medicine demands. The result is labs running world-class science on outdated software, patching the gaps with spreadsheets, manual workarounds, and disconnected tools.

A LIMS for precision medicine is not just a standard LIMS, it is a fundamentally different type of platform, which is the one built for the data complexity, regulatory rigour, and clinical accountability that precision medicine requires.

What sets a precision medicine LIMS apart

A standard LIMS handles sample accessioning, result entry, and basic reporting. That is table stakes. Precision medicine workflows go further, a single patient specimen can generate genomic, transcriptomic, and proteomic data across multiple instruments and analytical stages. The system managing that journey needs to do more.

A LIMS for precision medicine labs should handle:

  • Automated import of molecular data, including NGS instrument integration and automated sample sheet generation
  • End-to-end traceability, from sample receipt through nucleic acid extraction, library prep, sequencing, and clinical reporting
  • Stage gate workflows that enforce quality control at every step, preventing errors from progressing downstream
  • Built-in compliance with ISO 17025 and ISO 15189 standards, including complete audit trails for sample handling
  • Clinical modules for patient and physician tracking, with secure report distribution and real-time delivery notifications
  • HL7 support for direct integration with hospital information systems and electronic health records

Without these capabilities, labs are forced to bridge the gaps manually which introduces exactly the kind of human error that precision diagnostics cannot afford.

The compliance challenge most labs underestimate

Regulatory compliance in precision medicine is not just about passing an audit. It is about building a system where trust in data is built into every process by default.

In high-stakes environments — oncology diagnostics, genetic testing, molecular pathology — the consequences of a documentation failure are not just operational. They are clinical. A sample without an unbroken chain of custody, a result without a time-stamped record, a report delivered to the wrong physician: these are not edge cases. They are risks that need to be designed out.

Paper-based systems and general-purpose LIMS platforms cannot reliably support this level of accountability. Electronic records need to be time-stamped, version-controlled, and immutable. Workflows need to enforce process integrity, not just record that a step was completed, but prevent the next step from proceeding until compliance criteria are met.

LIMS for precision medicine in the real world: a Bangladesh case study

One of the most compelling demonstrations of what a precision medicine LIMS can achieve comes not from a well-resourced first-world hospital, but from a molecular pathology lab being built in Bangladesh.

An oncology and nanotechnology research organisation engaged QLIMS to support the delivery of rapid point-of-care cancer diagnostics and genetic testing in one of the world’s Least Developed Countries (LDCs). The challenge was significant: limited regulatory infrastructure, language and cultural barriers across the lab team, and the need to meet international quality standards from day one.

The organisation evaluated four LIMS platforms. QLIMS was selected for its no-code configurability, multilingual visual interface, and high-security architecture, capabilities that mattered as much in Bangladesh as they would in London or Sydney.

The deployment included:

  • Automated Illumina sequencing integration with sample sheet generation and mutation identification
  • Centralised data management across results, instrument logs, and reagent records
  • ISO 17025 and ISO 15189-compliant audit trails from day one
  • Secure clinical report distribution with real-time SMS tracking for physicians

Data lake infrastructure to capture population-level genomic data unique to South Asian genetic profiles, not typically represented in Western datasets

The outcome: a lab now delivering world-class oncology diagnostics in a region that previously had no such capability. External auditors described QLIMS as one of the most sophisticated and easy-to-use systems they had ever assessed.

The broader impact is significant. Bangladesh spends an estimated USD 5 billion annually on citizens seeking medical treatment abroad. By raising the standard of diagnostic capability domestically, the country can begin to recapture those patients and provide equitable access to precision medicine for populations historically excluded from it.

What to look for when evaluating LIMS for a precision medicine lab

Not all LIMS vendors claiming to support precision medicine actually do. Here is what separates platforms that deliver from those that overpromise.

No-code configuration

Precision medicine workflows evolve quickly. If adapting your LIMS to a new assay or regulatory change requires a software developer, you are already behind. Look for a platform that allows lab managers and informatics teams to configure workflows, fields, and reports without code.

Instrument integration

Manual data entry between instruments and your LIMS is a reliability risk. LIMS should integrate directly with your sequencing and analytical platforms, automating data import and eliminating transcription errors.

Scalability and AI readiness

The labs winning in precision medicine are the ones building data infrastructure now that will support AI-powered analysis later. Look for a LIMS that supports data lake architecture, structured data capture, and a clear roadmap for AI integration. 

Compliance by design

Regulatory compliance should be built into the platform, not bolted on. ISO-compliant audit trails, stage gate workflow controls, and document version management should come standard, not as premium tiers.

QLIMS: precision medicine LIMS built for modern labs

QLIMS is a SaaS LIMS designed specifically for the complexity of precision medicine, genomics, and molecular diagnostics. It brings automation, instrument integration, and clinical reporting into a single secure platform without requiring IT infrastructure or software developers to configure and maintain it.

Used by genomics and oncology labs across globally, QLIMS is built for labs that operate at the frontier of medicine where the stakes are high, the data is complex, and the infrastructure needs to be as advanced as the science.

If you are evaluating LIMS platforms for a precision medicine or genomics lab, we would be happy to show you what QLIMS can do.

Book a Demo Here. Read the Bangladesh Case Study Here. 

2026, laboratory management, LIMS, Uncategorized

When Does a Laboratory Need a LIMS? 7 Signs You’ve Outgrown Spreadsheets

Posted on by Ezgi Ozer
when does a laboratory needs LIMS
22
Jun

When does a laboratory need a LIMS? It’s one of the most common questions laboratory managers ask as their operations grow. Spreadsheets are where everything begins. A few samples become a few hundred. A simple tracking sheet becomes several workbooks. Then more tabs are added, formulas become more complex, and suddenly the entire laboratory is relying on a system that was never designed to manage laboratory operations.

The spreadsheet that solved yesterday’s problem could be creating tomorrow’s bottleneck.

At first, spreadsheets feel flexible and familiar. But as workloads grow, compliance requirements increase, and teams expand, they can quietly become one of the biggest barriers to efficiency.

So how do you know when your laboratory has outgrown spreadsheets?

Here are seven common signs:

1. Your Team Is Spending More Time Managing Data Than Performing Science

Scientists, technicians, and quality teams should be focused on testing, analysis, and decision-making.

Instead, many laboratories find themselves manually entering results, updating multiple files, copying data between systems, and checking formulas.

The result? Valuable time is spent maintaining records rather than generating insights.

For example, entering 100 sample results may only take a few minutes. But repeat that process multiple times per day, across multiple analysts, and those minutes quickly become hours every week.

A LIMS automates much of this administration, allowing teams to focus on higher-value work.

2. Sample Tracking Is Becoming Difficult

One of the earliest warning signs is losing visibility of sample status.

Questions such as:

  • Has this sample been tested?
  • Who approved the result?
  • Which version of the report is correct?
  • Where is the sample now?

often become more frequent as volumes increase.

A laboratory processing 20 samples per day may manage comfortably using spreadsheets. A laboratory processing 200 samples per day often struggles to maintain visibility without a centralised system.

A LIMS provides real-time tracking from sample receipt through to reporting.

3. Audit Preparation Feels Stressful

Audits should not require days of searching through folders, emails, and spreadsheets. If your team spends significant time gathering evidence, proving traceability, or reconstructing records, your current process may be creating unnecessary risk.

Modern laboratories increasingly need:

  • Complete audit trails
  • User accountability
  • Electronic approvals
  • Controlled data access

These requirements are difficult to manage consistently using spreadsheets alone.

4. Errors Are Becoming More Common

Spreadsheets are powerful tools, but they are also highly dependent on human input.

A single accidental formula change, duplicate entry, or copy-paste mistake can impact reporting, investigations, and compliance.

As laboratory complexity grows, the likelihood and cost of these errors grows with it.

Consider a laboratory generating hundreds of reports per month. Even a small error rate can lead to significant rework, delayed reporting, or customer dissatisfaction.

As laboratories grow, reducing human error becomes increasingly important.

5. Different Teams Are Working in Different Systems

Many laboratories find themselves storing information in several locations:

  • Sample tracking in spreadsheets
  • Results in shared folders
  • Quality records in separate systems
  • Instrument data stored independently

For example, generating a single client report may require information from three or four different sources. This creates data silos.The more disconnected the data, the harder it becomes to maintain efficiency and confidence in decision-making.

A LIMS creates a single source of truth, bringing data, workflows, and reporting together in one platform.

6. Growth Is Creating Operational Pressure

Growth is often the point where spreadsheets begin to struggle.

Perhaps your laboratory has:

  • Added new testing methods
  • Expanded into multiple locations
  • Increased sample volumes
  • Hired additional analysts
  • Won larger customer contracts

What worked for a small laboratory rarely scales efficiently.

If every increase in workload requires additional manual administration, your systems may be limiting future growth.

A modern LIMS helps laboratories scale without increasing complexity at the same rate. Scalable laboratories need scalable systems.

7. Reporting Takes Longer Than It Should

Laboratory data is valuable only when it can be accessed, reviewed, and shared efficiently.

If teams are spending hours compiling reports, validating data, and manually creating customer deliverables, reporting may have become a bottleneck.

A modern laboratory informatics platform can help streamline reporting by keeping data connected from sample receipt through to final approval. You can simply click on the “Generate a CoA” in a modern LIMS and automate your report process. 

A Simple Rule of Thumb 

There is no perfect time to implement a LIMS. However, laboratories should begin evaluating their options if they:

  • Conduct more than 500 tests per month
  • Have multiple staff entering or reviewing results
  • Need stronger compliance and traceability
  • Struggle to track samples efficiently
  • Spend significant time preparing reports or audits
  • Plan to grow over the next few years

The best time to implement a LIMS is often before operational challenges become business problems.

Ready to See What a Modern Laboratory Platform Looks Like?

QLIMS helps laboratories centralise sample management, automate workflows, strengthen compliance, and reduce manual administration within a configurable cloud-based platform.

Whether you’re managing hundreds of samples or hundreds of thousands, QLIMS is designed to grow with your laboratory.

Book a personalised QLIMS demonstration and discover how modern laboratories are replacing spreadsheets with connected, scalable laboratory informatics.