A Fully Searchable ELN
Uncountable is deployed by 175+ R&D teams globally, replacing free-text notebooks and buried attachments with a structured, searchable record of every experiment. Results are captured as data at the point of entry, not written up after the fact, so the notebook stays queryable years later instead of becoming another archive nobody opens.
Why Can't Scientists Find Their Own Past Experiments?
Traditional ELNs are fine for recording and useless for searching. Results sit in free text and attachments named by someone's initials and a date, with no way to query across them. Teams end up re-running experiments that already happened, because the record of the first attempt isn't searchable, it's just filed away.
What Makes Uncountable's ELN Different?
A searchable, structured notebook. Every result is captured as queryable data, not free text.
Graphing, reporting, and stats built in. The analysis sits with the write-up rather than in a separate tool.
Past experiments surfaced in seconds. Search by content, months or years later.
Test requests inside the notebook. Send a request and receive analytical results with a single click.
A searchable record of every attempt. Failed experiments show up in search too, so mistakes aren't repeated.
One record, connected to every suite. R&D context carries straight into QC and Product Lifecycle without re-entry.
How Does a Notebook Become a Reusable Asset, Not Just a Write-Up?
The difference shows up on day one, before a team has entered a year of data.
Queryable by design. Not scientific Google Docs. The notebook becomes a reusable asset.
Structure first, AI second. A structured notebook is the data foundation for proprietary AI, not a replacement for the structure itself.
One click to the lab. The LIMS round-trip happens without leaving the write-up.
Every upload becomes searchable too. PDFs, spreadsheets, and SDS sheets are indexed the moment they're uploaded.
Your conventions, applied automatically. Skills capture house conventions once, so every entry follows them.
What Does a Structured Experiment Look Like in Practice?
Illustrative product view, synthetic data. A scientist searches "viscosity" and the notebook surfaces every matching experiment by content, not file name, oldest ones included. Opening a result shows the write-up alongside its data already captured as structured fields (viscosity, gloss, contrast ratio) and the underlying chart, so the analysis and the record are the same object rather than a write-up pointing at a spreadsheet somewhere else.
What Do Customers See After Switching?
Repsol made 10,000+ data points from two decades of formulation work searchable in one platform. Sika ran approximately 75% fewer experiments per project once past results stopped getting re-run by accident. As Richard Haldimann of Clariant put it: "We have already achieved much more than what we had intended in the original scope."
FAQs
Every result is captured as structured, queryable data at the point of entry instead of free text or attachments, so scientists can search past experiments by content months or years later instead of hunting through file names.
Yes. Failed and abandoned experiments are captured and surfaced in search right alongside successful ones, so teams don't repeat past mistakes.
Yes. Test requests can be sent and analytical results returned inside the notebook, and the same experiment record carries its R&D context into QC and Product Lifecycle without re-entry.
No. Structure comes first: every result is captured as clean, structured data, and AI capabilities like Bodie build on top of that foundation rather than replacing it.
