R&D teams rely on product information to make decisions at every stage of development. Yet that information is often fragmented across laboratory systems, product records, experimental data, technical documents, specifications, project files, and the knowledge held by individual experts.
Suggest with AI helps enterprise R&D teams bring these disconnected sources into context. It makes it easier for scientists, formulators, and lab managers to find relevant information, uncover related work, and reuse existing knowledge without manually searching across multiple systems.
Instead of relying only on keywords, document titles, or exact product identifiers, teams can use AI-assisted suggestions to explore the information connected to a question, product, material, formulation, or experiment. This helps researchers build a more complete picture before deciding what to test, change, investigate, or develop next.
Move beyond disconnected search
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Product information rarely exists as a single, complete record. A formulation may be linked to raw materials, specifications, test results, stability data, analytical reports, previous versions, manufacturing notes, quality documentation, technical publications, and historical project decisions.
When those records are distributed across different applications and repositories, finding the right information can take longer than the scientific evaluation itself. Researchers may need to search several systems, use different terminology in each one, ask colleagues for context, or recreate work because they cannot find evidence that already exists.
Suggest with AI helps reduce this effort by identifying potentially relevant connections across the R&D information landscape. It can surface related products, ingredients, materials, documents, experiments, and data points that a researcher may not have known to search for directly.
This shifts the experience from “Where should I look?” to “What information could help answer this question?”
Help researchers discover relevant knowledge
Scientists do not always begin with a fully defined search. They may be trying to understand why a product is underperforming, find a substitute for an ingredient, improve a material property, investigate a formulation issue, or determine what has already been learned about a product concept.
In these situations, relevant knowledge may be spread across projects, business units, sites, and systems. Valuable context can be missed when it is stored under a different project name, material code, product identifier, or naming convention.
Suggest with AI helps researchers explore information that may be relevant to their work, including:
- Related products, materials, ingredients, and formulations
- Comparable experiments and historical test results
- Technical documents, reports, specifications, and project records
- Analytical, quality, stability, and performance data
- Prior decisions, observations, and documented lessons learned
- Information held in connected internal and approved external sources
For example, a formulation scientist assessing a potential ingredient replacement may need to understand where that ingredient, or a similar material, has previously been used. Suggest with AI can help surface related formulations, performance data, specifications, technical reports, and previous investigation records so the scientist can start with available knowledge rather than beginning from zero.
Make institutional knowledge easier to reuse
R&D organizations create valuable knowledge through every experiment, development program, product update, and quality investigation. However, knowledge only creates continuing value when future teams can find and apply it.
Suggest with AI supports knowledge reuse by making connections between records more visible. It can help a researcher identify that a comparable material was assessed by another team, a similar issue was investigated in an earlier project, or supporting test data exists in a different system.
This can help organizations reduce unnecessary duplication, shorten the time spent searching for context, and preserve knowledge when teams change or experienced employees leave. It can also give new scientists and technical team members a faster path to understanding previous work.
For lab managers and R&D leaders, the benefit is greater continuity across projects. Teams can build on proven learning, identify relevant precedent sooner, and spend more time on high-value scientific and technical work.
Keep experts in control
AI-assisted suggestions should support scientific and technical judgment, not replace it. Researchers still need to assess the quality, applicability, and limitations of any information they find.
Suggest with AI helps users get to relevant data and supporting context faster, while allowing them to review the underlying records, documents, results, and sources. This is particularly important when decisions affect product performance, quality, safety, compliance, manufacturability, intellectual property, or commercial viability.
A strong AI-assisted R&D workflow gives users the ability to understand why a piece of information may be relevant, inspect the evidence behind a suggestion, and apply their own expertise before taking action.
Build a stronger foundation for R&D AI
AI becomes more useful when R&D information is accessible, connected, and governed. If critical product knowledge remains isolated across systems, files, and teams, researchers will continue to spend significant time locating and interpreting information manually.
Suggest with AI helps create a more connected R&D information experience. It supports faster discovery, stronger knowledge reuse, and more informed decision-making by helping teams find relationships between the product information they already hold.
For enterprise R&D leaders, this can support a more scalable approach to innovation: one in which data, documents, experimental findings, and technical expertise are easier to discover and apply across the organization.
Request a demo to find out how to turn fragmented R&D information into usable knowledge with Uncountable.
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