Uncountable Briefing Note
Axendia was briefed by Joseph Mann, Vice President, Solutions Engineering; Zeeshan Mahmood, Director of Partnerships; and Jason Hirshman, Co-Founder and Chief Technology Officer at Uncountable on the company’s AI-native platform for end-to-end product development.
Founded in 2016, Uncountable provides scientific data management and product development software designed to connect scientific data, workflows, and product information to help organizations improve R&D productivity, collaboration, and the transition from research to manufacturing. The company serves more than 175 customers across industries including chemicals, materials, consumer products, food and beverage, and life sciences.
From Machine Learning Consulting to an AI-Native Product Development Platform
Uncountable originated in machine learning consulting, applying techniques such as Bayesian optimization and Gaussian processes to scientific and product development challenges. Early applications included formulation optimization, identifying trends in experimental data, investigating out-of-specification results, and supporting design of experiments.
Through this work, the company identified a recurring obstacle: organizations often lack accessible, structured, and connected data on which to apply advanced analytics effectively. Uncountable subsequently expanded its focus toward scientific data management and an integrated product development platform, positioning data organization and workflow connectivity as foundations for applying AI across the product lifecycle.
Mahmood explained, “After two years, we realized that AI and machine learning models weren’t really the problem. The problem was the data. It’s garbage in, garbage out. We would receive data, run our model a few weeks later, and then the customer would tell us that a colleague had forgotten to upload part of the data. All that work was for nothing. So, we asked ourselves, ‘Why don’t we focus on being a system of record?’”
Today, the company describes its platform as AI-native and configurable, supporting multiple product development and laboratory workflows within a common data environment. Built on a unified relational data model, the platform can be configured to support capabilities associated with laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs), product lifecycle management (PLM), quality control, and quality management systems (QMS).
Rather than requiring organizations to replace all their existing applications, Uncountable can serve as a data and workflow layer or support selected applications, with integrations to systems such as ELNs, LIMS, ERP and others.
Addressing Fragmented Scientific Data and Disconnected Workflows
A central theme of the briefing was the challenge of connecting data across R&D, scale-up, technology transfer, quality, and manufacturing. Relevant information may be distributed across laboratory applications, spreadsheets, enterprise systems, documents, and individual workflows. This fragmentation can make it difficult to maintain context, reuse experimental results, trace decisions, and transfer knowledge between teams.
“Our approach is to provide a configurable environment in which scientific data, product information, and workflows can be connected. The platform can support research activities while also providing more structured processes for product development, quality, and lifecycle management,” said Mann.
The company’s PLM capabilities connect information such as multilevel bills of materials (BOMs), specifications, formulas and formulations, recipes, inputs and outputs, supplier documentation, and product or marketing information. The outcome is a more connected product record and digital thread linking activities from early development through scale-up and manufacturing.
As Mann described it, the platform provides a way to capture relevant data and connect information across workflows, while accommodating external documents where needed. The practical value of this approach lies in how well those connections reflect an organization’s actual processes. Achieving end-to-end traceability will depend on implementation scope, integrations, data governance, and how the platform is configured and used.
Beyond PLM, Uncountable also demonstrated capabilities associated with quality management systems (QMS) and quality control laboratory information management systems (QC LIMS). These include configurable workflows, specifications, required fields, approvals, and audit trails. Together, these capabilities broaden the platform’s potential role from managing scientific and product data to supporting the quality processes that accompany product development and downstream operations.
For regulated environments, Uncountable provides vendor-supplied validation documentation and supporting evidence, including assessments against 21 CFR Part 11 and EU Annex 11, to help customers develop and execute their own risk-based validation packages, supported by a quarterly GxP release cadence.
Applying AI to Structured Scientific Data
Uncountable’s AI capabilities build on its early work in machine learning and its focus on making scientific data available in a structured form. The company demonstrated Bodie, its large language model (LLM)-based assistant, which is designed to help users interact with scientific data and experimental results. Bodie can answer questions about prior work, retrieve relevant information, generate graphs, and support formulation analysis, prediction, and recommendations for next experiments.
As Mahmood explained, structured data enables more targeted interactions with the assistant. “Because your data is structured, you can actually now ask it very pointed questions. It can create graphs for you, it can do formulation prediction, it can suggest synthesis routes.”
The broader value proposition is that AI becomes more useful when it is grounded in an organization’s scientific data, experimental results, and product development workflows. By bringing data management, application workflows, and AI-enabled analysis into a common environment, Uncountable aims to help R&D teams make better use of existing knowledge, reduce the effort required to find and interpret prior work, and accelerate decisions about the next experiment or formulation.
The extent to which these benefits are realized will depend on data quality, the completeness of connected information, and the suitability of each AI method for the task. Axendia’s newly published market research: AI in Life Sciences, What the Industry is Really Saying, offers two relevant recommendations: strengthen data foundations for AI by improving data accessibility, structure, and governance across R&D, manufacturing, quality, and regulatory systems; and align AI strategies with operational priorities by targeting measurable business outcomes, including faster time to market, improved manufacturing efficiency and enhanced quality oversight.
Connecting R&D to Product Lifecycle Management
Uncountable’s PLM offering maintains a product record that links development information to downstream activities. This includes relationships among product specifications, formulations, ingredients or inputs, supplier documentation, and other product attributes. Mahmood described the scope as “one system of record from design to plant,” with multilevel bills of materials connecting the relevant product information.
In the demonstration, Uncountable showed how configurable product records and workflows could support collaboration and change management. The goal is to reduce disconnects between early research, product definition, scale-up, technology transfer, and manufacturing by preserving relationships between product information and the processes used to develop it. As Mahmood explained, the company is seeking to address the handoff between R&D and downstream teams, where development knowledge can become disconnected from the product as it moves toward production.
The approach may be particularly relevant to organizations developing formulated products, where product characteristics depend on the relationships among ingredients, recipes, experimental results, and process conditions.
Uncountable is also extending its PLM offering toward broader engineering use cases. Rather than replacing established computer-aided design (CAD) tools, Uncountable integrates with them: the demonstration showed that CAD files can be uploaded and visualized, with integration to external CAD systems enabling users to open and edit files in their dedicated design environment. This approach provides a path to connect CAD-based engineering information with Uncountable’s product records and process-driven workflows. The company’s roots in process and formulation-based development provide a foundation for expanding its role in organizations seeking to connect product design, development, and manufacturing more effectively.
In Brief
Uncountable has evolved from applying machine learning to customers’ R&D data into a platform connecting scientific data management, R&D workflows, PLM and AI. Its core insight is that AI becomes more useful when grounded in accessible, structured scientific data. By combining data management, scientific analysis, and AI capabilities, the platform aims to help organizations leverage experimental results, preserve institutional knowledge, and make better informed product development decisions.
Its expanding capabilities in PLM, QMS and QC LIMS extend this approach beyond R&D. Product records, bills of materials, specifications, and configurable workflows offer a way to connect product development with downstream quality and manufacturing activities. This is especially relevant for formulated products, where ingredients, experimental results, supplier information, and process conditions all contribute to the final product.
The broader lesson is that connecting data across the product lifecycle can turn scientific knowledge into an enterprise asset. When R&D findings, product specifications, and quality processes are linked, organizations have greater opportunity to reuse what they learn, improve handoffs between teams, and carry development knowledge into manufacturing. Uncountable’s evolution illustrates how a strong scientific data foundation can serve as a starting point for a broader digital thread, with AI helping teams access and apply that knowledge throughout product development.
We will continue to provide updates on Uncountable as they become available.
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.