Every experiment earns its keep
The R&D Suite puts your lab notebook, R&D lab data, formulation records, design of experiments, and inventory on one structured record, so a result from three years ago is as searchable as the one from this morning.
Deployed by 175+ enterprise product development teams
Deployed by Enterprise R&D and Innovation Teams
Every experiment, on one record
Notebook entries, samples, formulations and test results are captured in one connected record. Scientists can trace any result back to its recipe, and every experiment stays searchable.
Electronic Lab Notebook (ELN)
LIMS
R&D
R&D, from first result to next design
An experiment record that holds up the day someone needs to search it.
- Structured, not a blank page: fields, tags, and results are captured in a structured format, so an experiment is data from the start, not prose in a document.
- Graphing and stats built in: plot and analyze right where the data was captured, with no export to a separate tool.
- Years of history stay queryable: every past experiment remains searchable on its own fields, never buried in a shared drive of file names.
You're here if experiment records still live in notebooks or personal spreadsheets, and finding past work means asking around instead of querying it.
The formulation you build is the record Quality and PLM inherit.
- No re-entry at the handoff: the formulation revision built in R&D is the same record QC tests against and PLM ships, not a spec re-typed into an email.
- Raw materials link both ways: every experiment traces to the lots and suppliers behind it, so a material change surfaces everywhere it's used.
- One approval workflow, not three: the record moves through review once, on the same data every downstream team reads.
You're here if a formulation gets re-typed into a spec email before Quality or PLM can use it, and a raw-material change doesn't reliably reach every recipe it touches.
Ask what's already been tried, in plain language.
- Institutional knowledge, not tribal knowledge: historical experiments, including the ones that failed, stay searchable by material, property, and outcome.
- Bodie searches for you: describe what you're looking for and Bodie finds it across years of project history, not a keyword match on a file name.
- Nothing restarts from zero: a shelved project picks up from what the last team already learned.
You're here if a shelved project restarts from zero because nobody can find what the last team already tried, including what failed.
The next trial, chosen by the data you already have.
- Statistical, not one variable at a time: multi-variable design plans for the most learning from the fewest runs.
- Model confidence shown, not asserted: effect sizes and fit sit next to every recommendation, so a formulator can judge it.
- No export to a stats package: design, run, and model without leaving the record.
You're here if DOE runs in a separate stats package that goes stale the moment a new trial lands, and a model's recommendation gets re-checked by hand before anyone trusts it.
What connected R&D is worth
When R&D, quality, product lifecycle, and project management share one data model, the payoff shows up as experiments never repeated and time given back to scientists.
CTO, Cooper Standard
CTO, Sun Chemical
46%
faster formula reviews, saving 34 hours each
15%
less matierals waste = more sustainable R&D
1,000+
users across 35 global sites
>50%
faster transition from testing to results
The lab that never starts from scratch
Four things R&D teams tell us are different once they move onto one record.
One system for LIMS, ELN, and R&D
Configured, not customized
Your data, ready for AI
It carries into quality, PLM and PPM
See the platform in action
Clariant is using Uncountable to connect global R&D workflows, make scientific data easier to reuse, and build a foundation for analytics and AI across more than 1,000 users and 35+ sites.
Shared by every suite
Bodie and the Portal work across R&D, Quality, Product Lifecycle and Project Portfolio Management at once, on the same structured data model.
Bodie, the AI Layer
The Uncountable Portal
Where connected R&D shows up
Past experiments, including the ones that failed, are searchable by material, property, and outcome, so the project starts from what's already known, not a blank notebook and a guess.
Inventory is tracked against the formulas and experiments that consume it, so a lot swap surfaces against every formulation it touches, not just the one in front of you.
DOE tools recommend the next set of experiments from the accumulated data, so each round narrows in instead of repeating a full factorial because nobody's sure what's already been ruled out.
Effect sizes and model quality show alongside every prediction, so a formulator can see what's actually driving it before acting on it, not a black-box number taken on faith.
The formulation, its test results, and its revision history move with it to Quality and the BOM, so the next team picks up the same record, not a spec re-typed into an email.
Supplier documents are parsed automatically into structured data, so the properties inside them are searchable and usable the same day, not a PDF filed away until someone remembers to read it.
Connected to the systems you already run
Uncountable sits at the center as the system of record for R&D and quality data, with a comprehensive API and integration layer connecting the systems around it.
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A failed experiment should teach the next one something.
Most systems store the answer. Uncountable stores the evidence, including what didn't work, so the next experiment starts from what's already been learned, not from scratch.
FAQs
It's a statistical, multi-variable design tool: it plans a set of experiments to learn the most from the fewest runs, rather than testing one variable at a time. It generates constraints from project history and explains in plain language what the resulting model actually shows.
Yes. A formulation revision built in R&D is the same record Quality tests against and the Bill of Materials references, not a copy passed along by email or spreadsheet. Changes and results stay connected in either direction, with no re-entry.
Yes. Institutional knowledge search covers historical experiments including failures, so past learning stays available in plain language instead of buried in files no one remembers to check.
Yes. Ingestion is instrument-agnostic rather than a fixed list, connecting by drop folder, SFTP, API, or a lightweight edge agent light enough to run on a Raspberry Pi for unnetworked instruments. Your specific instruments get scoped and mapped during implementation.
See it on your own R&D data.
