Use case
Use case
Use case
How to Supplier Parts Catalog Automation
How to Supplier Parts Catalog Automation
How to Supplier Parts Catalog Automation
Supplier updates arrive in inconsistent formats that require hours of manual cleanup. Trame automates ingestion, normalization, matching, and exception handling to generate clean catalogs ready for ERP or PIM import.
Supplier updates arrive in inconsistent formats that require hours of manual cleanup. Trame automates ingestion, normalization, matching, and exception handling to generate clean catalogs ready for ERP or PIM import.
Supplier updates arrive in inconsistent formats that require hours of manual cleanup. Trame automates ingestion, normalization, matching, and exception handling to generate clean catalogs ready for ERP or PIM import.
Challenge
Challenge
Challenge
Updating a parts catalog across multiple suppliers is slow, expensive, and risky.
Distributors routinely receive supplier data in:
Excel or CSV with inconsistent field names
PDFs or email attachments with unstructured descriptions
Portals with non-standard export formats
Missing or ambiguous attribute data
Each source demands manual parsing, normalization, matching, and exception handling before it can be used in production systems. This creates:
High operational cost and friction
Data quality issues (duplicate SKUs, inconsistent dimensions, missing prices)
Delays in SKU onboarding and sourcing decisions
Fragmented workflows across teams and tools
Manual approaches simply do not scale as supplier counts grow and product complexity increases.
Updating a parts catalog across multiple suppliers is slow, expensive, and risky.
Distributors routinely receive supplier data in:
Excel or CSV with inconsistent field names
PDFs or email attachments with unstructured descriptions
Portals with non-standard export formats
Missing or ambiguous attribute data
Each source demands manual parsing, normalization, matching, and exception handling before it can be used in production systems. This creates:
High operational cost and friction
Data quality issues (duplicate SKUs, inconsistent dimensions, missing prices)
Delays in SKU onboarding and sourcing decisions
Fragmented workflows across teams and tools
Manual approaches simply do not scale as supplier counts grow and product complexity increases.
Updating a parts catalog across multiple suppliers is slow, expensive, and risky.
Distributors routinely receive supplier data in:
Excel or CSV with inconsistent field names
PDFs or email attachments with unstructured descriptions
Portals with non-standard export formats
Missing or ambiguous attribute data
Each source demands manual parsing, normalization, matching, and exception handling before it can be used in production systems. This creates:
High operational cost and friction
Data quality issues (duplicate SKUs, inconsistent dimensions, missing prices)
Delays in SKU onboarding and sourcing decisions
Fragmented workflows across teams and tools
Manual approaches simply do not scale as supplier counts grow and product complexity increases.
Solution
Solution
Solution
Trame’s supplier catalog automation workflow uses agentic logic and micro-actions to model each step of the process in isolation, ensuring accuracy, repeatability, and traceability.
How it works
Universal ingestion — Accept files in any supplier format (Excel, CSV, PDF, HTML exports).
Structured extraction — Break noisy inputs into normalized rows and attributes using AI extraction logic.
Attribute standardization — Align fields like SKU, UPC, price, dimensions, availability across suppliers.
Master catalog matching — Determine whether items exist in your catalog, linking by identifiers and similarity logic.
Exception detection — Identify business and system exceptions (missing data, conflicting prices, unknown SKUs).
AI-assisted resolution — Provide human validators with AI-suggested fixes and explanations.
Clean output generation — Produce an import-ready file for ERP, PIM, or e-commerce systems.
Audit reporting — Deliver clear reports on changes, exceptions, and confidence scores.
Each component is modular, letting operations teams plug in business rules, enrichment logic, and exception workflows that match their distribution needs.
Trame’s supplier catalog automation workflow uses agentic logic and micro-actions to model each step of the process in isolation, ensuring accuracy, repeatability, and traceability.
How it works
Universal ingestion — Accept files in any supplier format (Excel, CSV, PDF, HTML exports).
Structured extraction — Break noisy inputs into normalized rows and attributes using AI extraction logic.
Attribute standardization — Align fields like SKU, UPC, price, dimensions, availability across suppliers.
Master catalog matching — Determine whether items exist in your catalog, linking by identifiers and similarity logic.
Exception detection — Identify business and system exceptions (missing data, conflicting prices, unknown SKUs).
AI-assisted resolution — Provide human validators with AI-suggested fixes and explanations.
Clean output generation — Produce an import-ready file for ERP, PIM, or e-commerce systems.
Audit reporting — Deliver clear reports on changes, exceptions, and confidence scores.
Each component is modular, letting operations teams plug in business rules, enrichment logic, and exception workflows that match their distribution needs.
Trame’s supplier catalog automation workflow uses agentic logic and micro-actions to model each step of the process in isolation, ensuring accuracy, repeatability, and traceability.
How it works
Universal ingestion — Accept files in any supplier format (Excel, CSV, PDF, HTML exports).
Structured extraction — Break noisy inputs into normalized rows and attributes using AI extraction logic.
Attribute standardization — Align fields like SKU, UPC, price, dimensions, availability across suppliers.
Master catalog matching — Determine whether items exist in your catalog, linking by identifiers and similarity logic.
Exception detection — Identify business and system exceptions (missing data, conflicting prices, unknown SKUs).
AI-assisted resolution — Provide human validators with AI-suggested fixes and explanations.
Clean output generation — Produce an import-ready file for ERP, PIM, or e-commerce systems.
Audit reporting — Deliver clear reports on changes, exceptions, and confidence scores.
Each component is modular, letting operations teams plug in business rules, enrichment logic, and exception workflows that match their distribution needs.
Results
Results
Results
Dramatically reduced manual work — Transform a multi-hour weekly task into a repeatable automation.
High-quality catalog data — Standardized fields and consistent attribute schemas reduce operational risk.
Scalable workflows — Grow from tens of suppliers to hundreds without linear increases in headcount.
Human-in-the-loop validation — Exceptions are surfaced with context, reducing review time while maintaining control.
Dramatically reduced manual work — Transform a multi-hour weekly task into a repeatable automation.
High-quality catalog data — Standardized fields and consistent attribute schemas reduce operational risk.
Scalable workflows — Grow from tens of suppliers to hundreds without linear increases in headcount.
Human-in-the-loop validation — Exceptions are surfaced with context, reducing review time while maintaining control.
Dramatically reduced manual work — Transform a multi-hour weekly task into a repeatable automation.
High-quality catalog data — Standardized fields and consistent attribute schemas reduce operational risk.
Scalable workflows — Grow from tens of suppliers to hundreds without linear increases in headcount.
Human-in-the-loop validation — Exceptions are surfaced with context, reducing review time while maintaining control.
Ready to get started?
Create an account and start accepting payments – no contracts or banking details required.
Read more use cases
Read more use cases
Read more use cases
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 26 26" xmlns="http://www.w3.org/2000/svg"><g d="M 21.134 0 L 19.972 2.046 L 11.658 2.046 C 11.169 2.046 10.772 2.443 10.772 2.932 L 10.772 24 L 8.726 22.838 L 8.726 2.932 C 8.726 2.443 8.329 2.046 7.84 2.046 L 0 2.046 L 1.162 0 Z M 19.973 3.887 L 21.064 5.796 L 15.408 5.796 C 14.919 5.796 14.522 6.193 14.522 6.682 L 14.522 22.844 L 12.613 23.981 L 12.613 3.887 Z M 6.885 3.887 L 6.885 5.864 L 1.222 5.864 L 0.026 3.887 Z M 23.853 2.94 L 22.696 4.928 L 21.488 2.93 L 22.644 0.988 Z" fill="transparent" height="24px" id="gnQLUM7bZ" transform="translate(1 1)" width="23.85253667522511px"><path d="M 21.134 0 L 19.972 2.046 L 11.658 2.046 C 11.169 2.046 10.772 2.443 10.772 2.932 L 10.772 24 L 8.726 22.838 L 8.726 2.932 C 8.726 2.443 8.329 2.046 7.84 2.046 L 0 2.046 L 1.162 0 Z M 19.973 3.887 L 21.064 5.796 L 15.408 5.796 C 14.919 5.796 14.522 6.193 14.522 6.682 L 14.522 22.844 L 12.613 23.981 L 12.613 3.887 Z M 6.885 3.887 L 6.885 5.864 L 1.222 5.864 L 0.026 3.887 Z M 23.853 2.94 L 22.696 4.928 L 21.488 2.93 L 22.644 0.988 Z" fill="rgb(0, 0, 0)" height="24px" id="IhpQBSBxF" stroke-dasharray="" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="10" stroke-width="0.95" stroke="rgb(0, 0, 0)" width="23.85253667522511px"/></g></svg>)
Trame. All right reserved. © 2026
Resources
Trame. All right reserved. © 2026
Resources
Trame. All right reserved. © 2026