Inside Uncountable Bootcamp
What Four Consultants Learned in Three Days
Team KC’s Mariana Perez Barco, Katie Henning, Felicia Loi, and Founder Talia Karchem recently spent three days at Uncountable Bootcamp in their NYC office, deep-diving into their platform in a hands-on sandbox environment.
At almost every data- or informatics-related event we’ve attended so far in 2026, the message is clear: the foundation comes first. The Uncountable Bootcamp reinforced that lesson from a different angle. Uncountable is a highly configurable platform, with a flexibility that impresses every member of our team, but that flexibility pays off only when the underlying data model is well defined from the start.
Hear from Talia, Felicia, Katie, and Mariana on what stood out, what clicked, and where they see Uncountable fitting in:
Q: What were you hoping to get out of the bootcamp, and how was it organized?
Mariana: I wanted to understand where Uncountable is at its best: the core use cases the platform was built for, and the best practices for configuring it to get the most out of those strengths.
Talia: The training was well structured. There was a lot of material to cover, but the folks who organized and delivered it did a great job of breaking up the topics and getting through everything while still allowing time for questions.
Q: How would you describe Uncountable to a scientist who’s never heard of it?
Felicia: Uncountable is an all-in-one platform that combines an ELN, LIMS, and data analytics tools. It’s built specifically to replace messy spreadsheets, physical lab notebooks, and disconnected instrument files. Uncountable structures all of your formulation, process, and testing data into one searchable database, so you can spend less time hunting for data and more time running your next experiment.
Katie: It’s a cloud-based home for your lab’s data. Notebooks, test results, formulations, and even past experiments all live together in one organized, easy-to-navigate space.
Q: How does Uncountable structure data under the hood, and how does it handle the messy realities of formulation work?
Mariana: Uncountable is shifting toward an entity-first model, where most items are treated as entities. Standard entities like ingredients and equipment are available out of the box, but the platform also supports custom entities that clients can define and interrelate to suit their specific needs.
Felicia: Uncountable recognizes that a formulation is more than a list of ingredients. It models recipes dynamically rather than as static text fields, tying raw materials, ingredient attributes, and process parameters together. Between your inputs and your final test results, the platform can use calculation logic to compute result properties automatically. And because ingredients, conditions, and test results all display on one page, comparing complex formulations becomes much easier.
Q: What was your favorite takeaway or cool fact about the system?
Felicia: How customizable the platform really is. Going into the bootcamp, I thought Uncountable was mainly about structuring data, but I learned there are so many ways to build in flexibility for custom solutions. There’s a concept called a value spec, which lets you define custom backend logic and calculations that are recognized across your entire tenant. It gives you a lot of room to tailor the platform to a client’s specific workflow.
Katie: The Excel-like functionality really impressed me. Cells adjust automatically as your data changes, so it feels as familiar as a spreadsheet while doing much more behind the scenes.
Mariana: The machine learning demo stood out. Users can train an ML model to modify formulations so they meet specific targets, choosing the datasets within the system to train on and setting priorities for certain targets. It’s a very powerful no-code tool for teams that have structured their data appropriately.
Talia: Honestly, the search shortcut (Ctrl+K on Windows) is huge. There’s so much functionality in the system that it can be difficult to remember where everything lives, but as long as I know even part of the name of a specific area, I can find it by searching.
Q: What did you learn about Bodie, Uncountable’s AI assistant?
Katie: Bodie can start an entry, pull in the relevant data, recommend the best way to visualize it, and then build that chart or graph for you, all in just a few prompts.
Mariana: Bodie can walk you through a feature step by step, showing you exactly where to click in the system. For a platform with this much functionality, that kind of built-in guidance is a real help.
Q: Was there a hands-on exercise where something “clicked”?
Felicia: Going into the bootcamp, I had little prior exposure and didn’t realize what Uncountable could do. That changed completely during the forms exercise, when Robin Marcenac, VP of Implementations, walked us through configuring custom workflow fields and forms. Seeing that level of flexibility in real time was a lightbulb moment for me. The exercise shifted my perspective from passively learning to actively brainstorming how I could use what he showed us to solve real pain points users face in the lab.
Katie: For me, it was building my own calculations and configuring limits, then putting everything together with a little help from Bodie. Doing it end to end is what showed me how all the pieces fit.
Mariana: Working through the calculations hands-on is what made it click for me. Seeing how recipes track data by connecting inputs and outputs across an experiment showed me how much value is gained when everything is linked.
Q: How does Uncountable compare to other platforms, and where is it the right fit?
Felicia: Uncountable is both more structured and more flexible than many other platforms we work with. On the structure side, a client really needs to have their data model sorted out upfront. If their entities aren’t clearly defined during implementation, the setup won’t go as smoothly. Once that foundation is in place, though, the system’s flexibility lets clients customize the platform to their needs. Value specs are a good example: they can really help scientists who otherwise rely on messy workarounds because of out-of-the-box limitations elsewhere.
Mariana: Uncountable offers a lot of flexibility for structuring data, though the tradeoff is a steeper initial learning curve. For organizations that need extensive configuration rather than an out-of-the-box standard, it’s a very compelling fit. It really shines in formulations, where teams can configure and customize it for their workflows.
Katie: I agree that formulations are the sweet spot. The Excel-like functionality is the best I’ve seen in a platform like this, and the file parsing really stood out as well. The AI assistant is still maturing compared to some other platforms, but the core data tools are excellent.
Q: After learning more about Uncountable, where can we use this knowledge to help our clients?
Felicia: The bootcamp gave us a solid foundation in Uncountable’s features and capabilities. Where we step in is bridging the gap between those features and real-world execution. We evaluate a client’s workflow before configuration, so we don’t digitize bad habits, and we help design clean integrations so data flows smoothly across their entire system.
Mariana: Much of our value comes from data governance and from bridging the gap between what a platform can do and how our clients’ science actually works. Every lab’s workflows are unique, and a configuration has to reflect that.
Talia: We always add value by deeply understanding what our clients do and translating that into usable workflows within a system. Uncountable has extensive functionality that can be useful to a biotech or pharmaceutical company, but it can be hard for scientists to see how that functionality will work for them. As a trusted advisor, we help clients understand exactly how a system like Uncountable will meet their needs.
Q: After this training, what’s the first thing you’d want to try with a client?
Felicia: Configuring a workflow that requires different forms. I’m eager to test trigger sets in a real-world scenario and see how flexible they are when setting up forms for different variants of the same assay request. It would be a great way to put the Form Admin capabilities to work and simplify a process that often causes headaches in the lab.
Katie: Formulations and forms were new territory for me, so I’d love to dig deeper into both and build them out for a real client use case.
Mariana: I’d configure a custom workflow around a client’s specific requirements to see how flexibly the platform adapts in practice.
Talia: The uploader! Being able to parse instrument data files and pull results directly into the system would save our clients a lot of manual effort.
If there’s a single thread running through our team’s reflections, it’s this: Uncountable’s flexibility is real, but it rewards preparation. Custom entities, value specs, configurable forms, and no-code machine learning all depend on a data model that has been thought through before configuration begins. Organizations that skip that step risk rebuilding the same spreadsheet workarounds inside a more powerful system.
That’s where the platform’s capabilities meet the lab’s reality, and it’s exactly the work Karchem Consulting does every day. From defining entities and data governance to translating scientific workflows into usable configurations, we help teams get the foundation right so their platform can deliver on its promise.
If your team is evaluating Uncountable, or any platform for formulation, process, or testing data, we’d love to talk. Contact us to start the conversation.
