Case Study: Streamlining Material Search for a Manufacturing & Trading Client

The Challenge

One of our clients, a large manufacturer and trader of thousands of industrial materials, faced a huge bottleneck in knowledge access. Their engineers, procurement team, and vendors constantly needed to check specifications like chemical composition, durability, temperature tolerance, compliance standards, and pricing tiers.

But the data was scattered across:

• Technical datasheets in PDFs.
• Internal ERP records.
• Vendor catalogs and certifications.

Employees spent hours searching through multiple systems just to confirm a single specification. This led to:

• Delays in procurement decisions.
• Errors in vendor communication due to outdated or missed details.
• Reduced productivity across engineering and supply chain teams.

The client’s goal was clear: make material search as simple as asking a question, without wasting time digging through documents.

The Solution

We implemented an AI-powered material search assistant using RAG + LAM:

• RAG (Retrieval-Augmented Generation):
  • All technical datasheets, compliance certificates, and vendor catalogs were indexed and made searchable.
  • When a user asked, “What’s the tensile strength of Material X at 200°C?” the AI pulled the exact detail from the most recent datasheet.
  • To ensure reliability, every AI answer included a direct reference link to the source document.
• LAM (Large Action Model):
  • Beyond answering questions, the AI could trigger workflows:
    • Add a selected material to the procurement request list.
    • Share the specs directly with a vendor via email draft.
    • Update ERP records with the chosen material for a project.
  • This turned a once time-consuming process into a seamless, single-step action.
• Best Practices Applied:
  • Chunked indexing: Documents were broken into small, meaningful sections for precise retrieval.
  • Metadata tagging: Materials were tagged by category, vendor, and region for context-aware search.
  • Confidence thresholding: Low-confidence answers were flagged for manual review, avoiding miscommunication.
  • User feedback loop: Engineers could mark answers as “useful” or “needs correction,” which helped refine the system’s performance.

The Results

• Search time cut by 70% – Queries that once took hours across systems were answered in seconds.
• Error rate dropped significantly – Since answers were grounded in the latest documents, wrong specifications in procurement requests were almost eliminated.
• Collaboration improved – Engineers, procurement staff, and vendors worked off the same accurate, AI-retrieved data.
• Adoption skyrocketed – Within 6 weeks, over 80% of material-related queries were handled by the AI assistant.

An operations manager put it simply:

“Before, finding the right spec felt like detective work. Now, I just ask the system and it gives me the exact page from the datasheet. It’s like having a smart assistant who knows every document inside out.”

Key Takeaway

For manufacturers and traders managing thousands of materials and vendors, RAG ensures accurate retrieval of technical specs, while LAM automates procurement workflows. The result isn’t just efficiency—it’s better decisions, faster projects, and stronger vendor relationships.