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How does AI extract data from supplier SDS and specification documents?

| Product Lifecycle Management | AI in PLM
Posted By: Federico Fontanella, Head of Strategic Innovation and Product Partners, Trace One

AI reads supplier documents such as safety data sheets (SDS) and specifications, pulls out the key data and places it in the right fields of your system. Instead of retyping hazard classifications and ingredient lists by hand, regulatory, EHS and R&D teams review what the AI extracted and approve it. The result is faster onboarding, fewer typing errors and more consistent data.

Summary

  • Teams manage large numbers of supplier documents, each one updated several times over its life, and the data in them is critical for safety and compliance. Retyping it by hand is slow and error-prone.
  • In Trace One SDS, AI extracts hazard classification (Section 2) and composition data (Section 3) directly from supplier SDS PDF files.
  • In Trace One PLM, AI extracts data from supplier specification documents during raw material onboarding.
  • Experts still review every extraction before it is saved. AI removes the typing, not the judgment.

Why are supplier documents such a bottleneck?

Every raw material comes with paperwork: safety data sheets, specifications, certificates of analysis and technical data sheets. Most arrive as PDFs, often in different languages. Teams manage large numbers of them, and each document can be updated several times over the life of a material. The data they hold is critical: it drives hazard classification, labels and safety data sheets.

Getting that information into a system usually means a specialist reads each document and retypes the data. This creates three problems:

  • It is slow. Expert time goes to data entry instead of regulatory and product work.
  • It is error-prone. One wrong concentration or hazard code can carry through to classification, labels and safety data sheets downstream.
  • It is inconsistent. Two people can enter the same document in two different ways.

How does AI extract data from a supplier SDS?

A supplier SDS follows a standard structure. Section 2 covers hazard identification, including the GHS classification. Section 3 covers composition and ingredients. These two sections hold the data regulatory teams need most, and they take the longest to retype.

In Trace One SDS, the process works like this:

  1. Upload the PDF. The regulatory user uploads the supplier SDS PDF to the supplier SDS specification record.
  2. Extract the hazard classification. The AI reads Section 2 and fills in the GHS hazard classification.
  3. Extract the composition. The AI reads Section 3 and fills in the supplier formulation: substance names, CAS and EC numbers, concentration ranges and GHS classifications.
  4. Review and confirm. When the AI detects hazardous substances, it lists them so the user can review them, link them to existing materials and confirm concentrations.
  5. Compare. Built-in comparison tools show what has changed between SDS versions, or between supplier data and internal material records.
  6. Save. The user edits any field if needed and saves. Nothing is final until a person approves it.

Key Takeaway

In Trace One SDS, AI extracts hazard classification and composition data from Sections 2 and 3 of supplier SDS PDFs. The regulatory expert reviews, edits and approves the result.

How does AI extract data from supplier specifications?

The same approach applies to product development. During raw material onboarding in Trace One PLM, AI extracts the relevant information from supplier specification documents and maps it to the right fields. R&D and quality teams get usable raw material data in the system faster, without retyping it from the PDF. For cosmetics and chemical manufacturers, the Supply Chain Assistant add-on in Trace One Devex PLM also extracts Section 2 (hazard identification) and Section 3 (composition) from supplier SDS files into fields that stay editable until someone validates them.

What should experts still check after AI extraction?

AI extraction is fast, but it is not the final word. Supplier documents can be incomplete, outdated or unclear, and AI output can be wrong. Experts stay in charge of three things:

  • Accuracy. Every extracted value is reviewed before it is saved.
  • Matching. Extracted substances are linked to the right materials in your own system.
  • Compliance decisions. The final classification, label and SDS remain the expert's decision.

AI that authors a complete SDS or updates it on its own, without review, is not what regulated teams need. The value is in removing the retyping so experts spend their time checking and deciding.

Key Takeaway

AI removes the typing, not the judgment. Every extraction is reviewed by an expert before it drives a classification, label or safety data sheet.

Why does better supplier data matter beyond saving time?

Supplier data feeds everything that comes after it: hazard classification of your own mixtures, labels, your own safety data sheets and regulatory reports. When supplier data is entered the same way every time, those downstream results are more consistent and easier to trust.

In Trace One SDS, extracted supplier formulation data connects to the material record, so it flows into classification and labeling work without being entered twice.

How do you start using AI for supplier documents?

  1. Start with your highest-volume document type. Supplier SDS onboarding is a strong candidate: high volume, standard structure and data that matters downstream.
  2. Define the review step. Decide who reviews AI extractions and what they check before saving.
  3. Measure the time saved. Time a set of documents before and after AI, so the gain is real and not assumed.
  4. Check your vendor's data commitments. Ask how supplier documents are processed and whether your data is used to train AI models. See the questions to ask an AI vendor.

Frequently asked questions about AI and supplier documents

Which SDS sections can AI extract in Trace One SDS?

AI in Trace One SDS extracts Section 2 (hazard identification, including GHS classification) and Section 3 (composition and information on ingredients) from supplier SDS PDF files.

Does AI replace regulatory review of supplier SDS documents?

No. AI fills in the data, and a regulatory expert reviews, edits and approves it before it is saved. Final classification and labeling decisions stay with the expert.

What data does AI extract from the composition section?

Substance names, CAS and EC numbers, index numbers, concentration ranges and GHS classifications, where the supplier document provides them.

Is supplier document data used to train AI models?

At Trace One, customer data, including uploaded supplier documents, is not used to train public AI models.

Can AI extract data from supplier specifications too?

Yes. In Trace One PLM, AI extracts data from supplier specification documents during raw material onboarding and maps it to the right fields. With the Supply Chain Assistant add-on, Trace One Devex PLM also extracts Sections 2 and 3 of supplier SDS files.

Learn more

How AI speeds up product development beyond documents: How does AI speed up time to market in product development?

What AI can do in Trace One PLM today and how it protects your data: AI in PLM with Trace One

About Trace One

Trace One is a global provider of AI-native Product Lifecycle Management (PLM) and regulatory compliance software for the food & beverage, cosmetics & personal care, and specialty chemical industries. With more than 30 years of industry expertise, the company helps over 9,000 brands worldwide develop, launch, and manage remarkable products faster. Purpose-built for formula-based product development, its platform connects the entire product lifecycle from ideation to launch, with integrated regulatory intelligence spanning 170+ countries. Learn more at traceone.com.