AI-Based eCommerce Recommendations and Metadata Enrichment Reduce Quote Preparation Time by 70%
Document Intelligence
eCommerce
Recommendations
A U.S. eCommerce SaaS platform turned invoices and competitor SKU lists into AI-generated alternative product quotes, enriched missing catalog metadata, and improved product search. The solution cut quote preparation time by 70% and expanded standardized metadata coverage to 95%.
Organization (NDA):
U.S. eCommerce SaaS platform serving both SMB and enterprise distributors.
United States
The client needed to turn invoices and competitor SKU lists into accurate quotes without forcing sales teams to manually search fragmented product data.
Product information was spread across multiple sources, equivalent or better alternatives to competitor SKUs had to be researched manually,
and most catalog items were missing the metadata required for reliable search, filtering, and cross-sell scenarios.
Challenge
- Slow quoting. Sales representatives could spend up to 30 minutes preparing a single quote because they had to gather product data from disconnected systems.
- Manual alternative matching. Identifying equivalent or better alternatives to competitor SKUs required repeated research and depended heavily on employee experience.
- Incomplete catalog metadata. More than 80% of catalog items lacked standardized attributes, which limited search quality and reduced the effectiveness of recommendations.
Solution
DevRain implemented an AI-powered product intelligence workflow that accepts invoices, CSV files, and competitor SKU lists as input.
The system extracts product references, normalizes them, and generates alternative product quotes directly in the platform.
At the core of the solution is a recommendation engine that evaluates product alternatives using technical specification similarity,
manufacturer agreements, and margin requirements. This allows sales teams to receive recommendations that are not only relevant,
but also aligned with commercial priorities.
In parallel, we introduced an AI-based metadata enrichment pipeline that fills in missing product attributes and standardizes catalog content.
This improved searchability across the catalog and created a stronger foundation for cross-sell and upsell scenarios.
We also added AI-powered search that helps users find products and alternatives using whatever information they have available:
SKU, product name, partial description, or other identifying details. As a result, both experienced and newly onboarded sales staff can work with the catalog much faster.
Business impact
- 70% faster quote preparation for sales teams.
- 95% of the catalog enriched with complete and standardized metadata.
- Faster onboarding because new sales reps could prepare quotes at the same speed as tenured staff within one week.
The implementation combined LLMs, retrieval, vector search, and catalog data services to support both quoting and metadata enrichment workflows.
Technologies
Google API
Google APIs and developer services used to integrate external AI and data capabilities.
Perplexity API
A search-augmented API for grounding LLM responses with up-to-date external information.
Pinecone
A managed vector database for semantic search, recommendations, and retrieval workloads.
Conclusion
By combining AI-generated recommendations, metadata enrichment, and intelligent search into a single workflow,
the client transformed quoting from a slow manual process into a scalable capability. The result was faster sales execution,
higher-quality catalog data, and a significantly lower dependency on individual product experts.
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