Uncountable Blog

AI-guided experiment design can cut the number of runs a project needs. The DMTA loop only gets shorter when results reach the next design decision faster, and that depends on structured data and scientists who trust the model...

Unexposed EM plates recorded as sampled. Failing samples discarded before analysis. Two recent FDA warning letters show how QC records can drift from what happened, and what teams should check in their own workflows...

PDRN is in launches from Primark to Dior, but the materials behind the name come from very different sources. Here's what to record about a supplier grade before you compare test results or sign off a claim.

Noel Hollingsworth joins CSols to explore what holds digital labs back, from legacy LIMS architectures to scientist trust in AI, and why modernizing R&D requires more than computing power to accelerate innovation and product development.

GLP-1 medications are changing what some consumers need from food. See how Nestlé, Danone, and Conagra are responding, and what protein, fiber, and portion targets mean for R&D teams balancing nutrition, taste, and texture.

Uncountable Summit Europe 2026 brought 120 leaders to Frankfurt to share digital transformation lessons, explore connected R&D, QC and PLM, and put Bodie AI into practice through hands-on workshops.

FDA’s salt substitutes rule gives food manufacturers more room to reduce sodium. Explore what changes for standardized foods, which formulation trials to prioritize, and how connected R&D data supports testing, labeling, and product launch decisions.

The EU’s PFHxA restriction now applies to cosmetics and food-contact paper and board. Learn which concentration limits apply and what records labs should keep, including supplier evidence, test results, formulation versions and batch traceability.

Explore how freeze-thaw cycles, temperature excursions, and custody records inform biobank sample suitability. Learn what handling history to capture, how to preserve aliquot lineage, and where automation can support better sample use decisions.

A living PLM connects released product records to changing material costs, supplier status, and regulatory requirements. It identifies affected versions and supports reassessment through formal review without automatically changing approved products.

A bill of materials describes components; a bill of substances describes chemical composition. Learn how these views connect with formulas to support product impact assessments, cost calculations, and compliance reviews.

Agent Personas in Uncountable let teams give Bodie, the platform's AI assistant, a defined job and a schedule, from weekly data cleanup to Monday lab reports. Agents run with the user's permissions and propose changes that wait for approval.

AI promises faster product development. But without connected data, trusted context, and clear workflows, it can create more work, not less.

Coating defects are rarely caused by one variable. Connected formula, process, raw-material, application, and quality data helps teams investigate root causes faster, prevent repeat failures, and preserve knowledge across R&D and manufacturing.

Uncountable’s Regulatory Management checks recipe ingredients against market-specific requirements as formulations are built, giving teams pass/fail visibility, cited evidence, and early warnings before noncompliant recipes move downstream.

Unify formulation, raw material, sample, and test-result data with mobile quality control LIMS software. Give quality control laboratories connected workflows, complete traceability, and faster decisions from sample receipt through release.

At WAC 2026, Sika and Uncountable shared how the Nuage digital laboratory program is connecting global R&D data, automation, and AI to reduce experiments and accelerate formulation development.

Mobile data capture can reduce the gap between lab work and lab records. Learn where information is lost, when mobile helps, and why scans, quantities, and photos must connect directly to the system of record.

LIMS manages samples, tests, and laboratory workflows. SDMS captures and preserves raw instrument files. Learn where each system fits, when LIMS alone is sufficient, and when laboratories need both.

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.

AI initiatives depend on more than algorithms. Learn how connected data across R&D, quality, and PLM supports faster, more resilient product development.

CDS and SDMS both support laboratory data integrity, but they do different jobs. Learn where chromatography data is acquired and processed, where scientific records are retained, and when regulated labs need both systems.

AI in formulation and materials R&D depends on connected, trusted data. Learn why fragmented product records hold AI back, and how a unified product-data foundation makes it useful in everyday R&D work.

Beiersdorf is using structured R&D data and machine learning to help reformulate iconic skincare products for a lower environmental footprint, while maintaining the safety, performance, and sensory experience consumers expect.

Suggest with AI helps enterprise R&D teams connect fragmented product information across lab systems, experiments, documents and specifications, so scientists can find relevant knowledge, reuse prior work and decide what to test next.

A practical guide to choosing a LIMS for specialty chemicals QA and QC: the criteria that matter, platform categories to compare, demo scenarios to request, and how to build a shortlist.

Lubricant innovation increasingly depends on accessible, connected formulation and testing data. Learn why strong data foundations are essential for faster R&D and effective AI adoption.

A Certificate of Analysis is more than a template. Treating COAs as governed outputs of approved quality data, not manually assembled documents, reduces errors, speeds turnaround, and keeps every certificate traceable to its source.

Explore Data now supports 3D scatter charts, so R&D and QC teams can plot three properties at once, color by a fourth, and rotate the view to catch groupings, outliers, and spec compliance that a flat chart can hide.

Uncountable is now available as a Plugin inside ChatGPT for US-hosted customers, bringing governed formulation, quality, and project data into the conversations where teams work.

Learn how cosmetics companies can control formulas, PIFs, safety data, claims, labels, batch records, and product changes. Better document management strengthens compliance, traceability, and faster product launches.

EU rules taking effect September 27, 2026 will raise the standard for consumer-facing carbon claims. Learn why traceable formulation, supplier, footprint, and change-control data are essential to keep claims accurate as products evolve.

Abrasive performance depends on more than grain and bond. Learn how porosity, process conditions, test parameters and application data help teams reduce blind iteration and develop more reliable products.

Products often stall near launch because R&D, quality, supplier, regulatory and manufacturing evidence is disconnected. Learn what a launch-ready product record needs before technical progress becomes approval.

An adhesive formula can work in the lab but fail in production when process context is lost. See why mixing, temperature, cure, materials and application conditions must stay connected to performance data.

A changed test method can make historical quality data difficult to compare. Learn how method versions, instruments, specifications and validation evidence protect product decisions, trend analysis and audit readiness.

FDA’s QMSR is now effective. This readiness review helps medical-device quality teams check controlled procedures, training, evidence retrieval, inspection readiness and digital quality workflows under amended 21 CFR Part 820.

An AI assistant can find past experiments, but only connected context shows whether they apply. Learn which material, formula, process, method, and status data R&D AI needs so scientists can inspect and trust its answers.

A beverage ingredient that matches its specification can still change flavor, color, or mouthfeel. Here is how to map the change, match evidence to risk, use sensory testing well, and approve a clearly defined substitution.

A batch can meet every release criterion and still behave differently in a customer application. This article explains when a specification result is enough, and when industrial-biotech teams need connected process, application, and quality evidence.

An eQMS migration changes how quality evidence is created, controlled, retrieved, and explained. Learn how to preserve audit readiness across active workflows, historical records, archives, and cutover.

Learn how a traceable qualification record helps electronics-material teams assess supplier, formulation and process changes without late-stage surprises.

Historian data often captures process drift before QC detects an out-of-spec result. Learn how petrochemical teams connect sensor trends, recipes, batches, and QC results to speed root-cause analysis.

PLM fails formulation teams when product data becomes a collection of disconnected formulas, specifications, revisions, and files. These ten pain points show where the product record breaks down.

A thermal profile is not background information. In glass and ceramics, it can determine the properties a team is trying to measure. Here is why it should be part of the connected material record.

Global spirits teams can protect house style across markets by connecting formula, ingredient, process, sensory and quality evidence.

Quality leaders rarely struggle because they lack a QMS module. They struggle when documents, events, CAPAs, training, suppliers, and product changes are disconnected. Here are ten QMS problems that keep resurfacing.

R&D-to-manufacturing handoffs fail when the specification moves downstream but the development context does not. Learn how connected formulation, process, QC, and lifecycle data help teams trace scale-up issues back to the evidence.

R&D and quality leaders ask the questions that shape strategy: where are we duplicating work, which projects are at risk, are we capturing what our people know? Here are the questions connected data answers without a fire drill.

QC and QMS do different jobs. QC records samples, tests, specifications, and results. QMS governs deviations, CAPAs, and controlled changes. The strongest quality workflows keep those jobs distinct while connecting the evidence.

Manual data entry errors rarely stay small. A mistyped unit, result, ingredient level, or sample ID can distort analysis and send R&D teams toward the wrong next experiment. Structured data reduces the errors that lead to bad decisions.

Formulation-based products need more than a parts list. This guide explains how to evaluate PLM for recipes, sub-recipes, held cost roll-up, version history, and raw-material impact across the product portfolio.

A Part 11-ready ELN needs more than an audit log and an approval button. Use this seven-point checklist to assess audit trails, access controls, e-signatures, validation evidence, retention, exports, and controlled workflows.

Analytics and experimentation are both called AI, but they do different jobs: one explains your past results, the other proposes your next experiment. This piece breaks down the distinction and why both depend on structured R&D data first.

Food additive compliance is no longer a final regulatory check. Changing federal rules, state restrictions, and global divergence now create reformulation work across portfolios.

The best QMS for your team is not simply the one with the most workflows. It is the one that gives every CAPA, deviation, and change-control decision the right evidence, from the QC result and specification to the development history behind it.

Portfolio leaders ask the questions that decide where the money goes: which projects to prioritize, what is the risk and expected return, where to invest limited resources? Here are the questions connected data helps you answer with evidence.

Learn when spreadsheets stop supporting reliable biobank sample management. Explore the costs of missing records, five signs it’s time for a LIMS, and what to fix before migrating your inventory.

ISO 9001:2026 was published in September 2026. Learn why the real transition work is making your quality system's evidence traceable, not rewriting every procedure.

A new PLM does not automatically require a LIMS replacement. This guide explains how the two systems differ, when a one-time product-data load is enough, when integration matters, and when a legacy LIMS may be the real constraint.

Prepare for ISO/IEC 17025 accreditation by testing whether your laboratory can retrieve and explain the full evidence behind a completed result.

Compliance gaps in lab data software usually come from disconnection: audit trails separated from records, manual re-entry, weak links between QC and QMS, and drifting specifications.

Raising bio-based or recycled content in a coating is a grade problem, not a quantity problem, and it comes with real trade-offs in cost and performance. This piece argues sustainable coatings reformulation is fundamentally a data challenge.

Clean beauty claims create a continual reformulation grind across a whole portfolio, and spreadsheets can't keep up. This piece argues the real bottleneck is data: tracing composition, versions, and claims evidence at scale.

In material-intensive manufacturing, one ingredient change ripples through every product, spec, and test. See how a PLM and QA/QC platform built for formulations keeps it all on one connected record, so nothing downstream is missed.

A restricted substance can sit in dozens of formulas, blends, and products. Here is how connected PLM data and where-used analysis help chemical teams scope the impact and run a controlled portfolio change.

Product teams ask the same questions every day: what changed between versions, what depends on this ingredient, where is this in the stage-gate? Here are the questions a connected PLM answers in seconds, so a change never blindsides you.

Many manufacturers run QC inside their ERP because it is already there. This explains what ERP quality modules do well, where they run out of room on specs, instruments, and R&D linkage, and when a purpose‑built quality control LIMS better fits.

Materials informatics tools now range from prediction engines to full R&D platforms. This guide explains the main categories, the data-structure and workflow questions that matter most, and how to test a platform against real work.

Most R&D software still relies on menus and filters, making scientists do the system’s work. This article explains the shift to AI‑first workflows, where natural‑language requests act on structured lab data and “just ask” returns real answers.

ISO 9001 certifies an organization's quality management system. ISO/IEC 17025 accredits a lab's technical competence for a defined scope. Here's the real difference, and what it means for the evidence a quality platform should support.

Automotive PLM helps manufacturers manage increasing vehicle complexity, from electric powertrains and software integration to product variants and supplier collaboration. Learn how PLM supports faster innovation and more efficient development.

A practical implementation guide for formulation manufacturers: how to scope the rollout, define the data model, govern ownership, integrate systems, migrate records, drive adoption, and measure operational outcomes.

In cosmetics, compliance fails when systems don't connect the rules to the data. This guide shows how unified R&D data delivers substance traceability, variant control, claims substantiation, and regional checks so compliance keeps pace with launch.

Food and beverage QC is high-volume, repetitive, and time-critical, with safety on the line. Here is what a quality control LIMS should do from the moment a line sample enters the lab to product release.

Chemistry R&D teams rarely lack data; they lack usable data. This post breaks down the six structural problems that make R&D data hard to query, compare, and learn from, from instrument formats to missing context, with the fix for each.

Fixing R&D data management used to be a someday project, because the cost of waiting was invisible. It is now visible in time to market, customer stickiness, and margin. This briefing explains why the gap between fast and slow innovators is widening.

Stage-gate reviews break down when project status is typed in by hand and out of date. This piece shows how deriving status from real experiment and quality data lets R&D leaders make go, hold, or kill decisions on evidence, not opinions.

Not everyone who sends you data should see inside your platform. The Uncountable portal gives partners and suppliers a simple way to submit requests, samples, and results, and lands each submission in your workspace as a structured, routed record.

Petrochemical R&D generates data faster than most teams can connect it. This guide shows how an enterprise R&D platform centralizes experimental data, strengthens traceability, and speeds the decisions that move products from lab to plant.

QC labs ask the same questions on every shift: is this sample in spec, why did it fail, what did we do last time? Here are the questions a connected quality control LIMS answers in seconds, with the traceability to back up every result.

A customer complaint should trigger a closed-loop investigation, not sit in an inbox. Here is how connected complaint management turns feedback into root cause, corrective action, and a documented resolution.

Uncountable has earned Frost & Sullivan's 2026 Technology Innovation Leadership Recognition for Global Integrated Pharmaceutical R&D Platforms, cited for unifying ELN, LIMS, LIMS for Quality, PLM, and QMS on one structured, AI-ready data model.

Choosing a unified laboratory informatics platform in 2026 hinges on four questions: can its AI learn from your data, is your data structured, how deep is integration, and does context survive scale-up? A guide for materials and chemicals R&D teams.

Connect stored samples with experiments and results so scientists can trace findings to the material used. Explore how lineage, plate mapping, inventory, and AI support sample selection, data retrieval, and investigation across R&D workflows.

Pharma 4.0 is pitched as a connectivity story, but AI-ready, FAIR, and connected are data problems, not machine problems. Structure your R&D and quality data first, thread it across the enterprise, then apply AI. Data first, intelligence follows.

Most companies trash their experimental failures. Treat every experiment as a data point with a long life, and shelved work becomes valuable again.

R&D teams ask the same questions every day, from "have we made this before" to "what should we test next." Here are the questions your lab data can already answer in seconds, and how connected data and AI turn days of digging into an instant answer.

The recast EU Construction Products Regulation adds a Digital Product Passport: composition, origin, embodied carbon, and reuse data. Here's what coatings and materials R&D teams should structure now so it becomes an export, not a rebuild.

Data silos rarely start as a crisis. They spread quietly across engineering workflows until no one trusts the record. This article shows how product data management reconnects fragmented work and keeps development moving on one data model.

The Digital Product Passport is expanding from batteries across the EU's ESPR by 2030. This guide explains what data each passport demands and why the teams that structure their R&D-to-PLM data now will be ready when their category's deadline lands.

Quality managers ask the same questions every day: which SOP version is current, are my technicians trained, where does this CAPA stand? Here are the questions a connected QMS answers in seconds, so you are always audit-ready.

A 2026 shortlist of the best LIMS for specialty chemicals, compared on QC at production scale, compliance, and formulation-data fit. See where six platforms lead and where a formulation-centric LIMS pulls ahead.

AI is transforming polymer R&D, but only for teams whose data is structured and connected. This piece explains why a unified R&D, QC, and PLM platform is the prerequisite for useful AI.

A neutral 2026 buyer's guide to cloud PLM software for R&D data. Compare the leading platforms by origin, discrete-manufacturing PLM versus scientific and formulation informatics, and see where each fits for your lab and formulation data.

Food, beverage, and FMCG products quickly become product families. Formulation PLM connects formulas, variants, ingredients, labels, specifications, and quality evidence so teams can manage change across the whole portfolio.

Battery materials R&D is uniquely data-intensive: fragmented teams, scale-up failures, long test cycles, and a huge design space all slow it down. Here are the eight biggest challenges and how structured, connected data helps solve each one.

Aerospace PLM helps aviation and defense manufacturers manage complex products, long lifecycles, strict compliance requirements, and global supply chains. Explore how PLM supports digital engineering, configuration control, and innovation.

R&D captures the data that defines a product, but that knowledge often stops at the lab. Here is why connecting your ELN, LIMS, and PLM in one data model protects traceability, speeds launches, and turns formulation work into a lasting record.

Recyclable reformulation must hit sustainability targets without losing barrier, seal, or clarity. Teams win by querying their experimental history, not rebuilding it: structured, connected R&D data cuts testing, survives scale-up, and proves it.