The Clarida Manifesto: Fixing the PCR Workflow

PCR technology itself has reached remarkable maturity. We have reliable instruments, well-validated reagent kits, and comprehensive analysis protocols. The scientific foundations are solid - I have had the privilege of contributing to some of them through the MIQE guidelines and the qbase+ platform.

But here is what I have observed over years of working with PCR researchers: while we have perfected the science, we have not mastered the workflow.

The typical PCR experiment forces researchers to become data janitors. They juggle Excel templates for experiment planning, instrument-specific software for data collection, separate analysis packages for statistics, and manual document writing for reporting. Each tool speaks a different language, requires different data formats, and lives in its own isolated world.

The result? Researchers spend the majority of their PCR-related time on administrative tasks - reformatting data, hunting through folders for the right files, manually transferring sample information, and reconstructing what they actually did weeks earlier when it is time to write the methods section. This is not a technology problem. This is a workflow problem.

Talk to any PCR researcher about their workflow, and Excel appears within the first five minutes. It is not because Excel is perfect for scientific data - it is because Excel does not fight you.

Excel gives researchers what they desperately need: complete control over their data, unlimited flexibility to combine information in unexpected ways, and the ability to modify their approach without asking permission from software developers. When you are exploring biological questions that do not fit neat templates, this flexibility is essential.

But Excel also cannot provide the specialized tools, quality controls, and workflow integration that PCR experiments demand. So researchers end up with the worst of both worlds: the administrative burden of manual processes and data conversions plus the scientific risks of ad-hoc calculations.

This is where I believe most scientific software companies get it wrong. They try to replace Excel with proprietary platforms that lock away your data and force you into their vision of how the science should be executed.

We are taking a different approach. What if, instead of replacing the Excel workflows researchers have made their own, we made them 10x more powerful? What if your experimental data could stay in your files, in formats you control, but with the manual calculations automated, the formatting nightmares eliminated, and the quality controls built in?

This leads to our core philosophy for PCR research workflows: high quality, open science with fewer apps and clicks.

Quality means two things. First, scientific rigor should be foundational, not optional. Every calculation, every recommendation, every automated step should meet publication standards. Second, traceability should not be an afterthought. When it is time to write your methods section or respond to reviewer questions, everything should be documented and accessible.

Openness means your data belongs to you, in formats you can read, export, and work with outside our platform. It also means contributing back to the scientific community - through free tools, educational content, and resources that genuinely advance the field, not just our business.

Fewer clicks means eliminating the administrative overhead that prevents researchers from focusing on science. Not by removing flexibility, but by automating the tedious parts and connecting the tools you already use.

The core insight is simple: instead of forcing researchers to adapt to software limitations, we adapt the software to support how researchers actually work. Our platform connects directly to your Excel files, adding intelligent experiment setup, automated quality controls, and seamless data flow between analysis steps. Your data stays in Excel - open, editable, exportable. But the manual calculations, format conversions, and repetitive tasks disappear.

We are starting with the biggest workflow bottlenecks: experiment setup and data analysis. These are the steps where researchers currently spend the most time on administrative tasks, and where errors have the biggest impact on downstream results. But the longer-term vision is broader: a complete workflow solution that takes you from experiment design to report or publication, with quality controls and documentation built in at every step, and your data always accessible in open formats.

The PCR research community spans academic labs and corporate R&D teams. While both face similar workflow challenges, they each have their own needs. Academic researchers need cost-effective solutions that support open science and community sharing. Corporate teams need professional features, compliance capabilities, and enterprise support. Rather than building separate products, we are creating a unified platform providing free community tools for individual researchers, professional features for small teams that need advanced capabilities, and enterprise options for larger teams requiring more capacity and compliance.

PCR technology has given us incredibly powerful tools for biological discovery. But if researchers spend more time managing data formats than interpreting results, we are wasting their potential.

The fragmented workflow is not just inefficient - it introduces errors, reduces reproducibility, and bores out talented scientists who came to research to make discoveries, not to wrestle with file formats. As someone who has contributed to the scientific foundations of qPCR, I believe we have a responsibility to make those foundations accessible through better tools. The science should be rigorous, the data should be open, and the workflow should get out of the way.

That is what we are building with Clarida.