RelightDepot × Rastro AI
RelightDepot is a commercial lighting distributor built for contractors and project teams. Its catalog depends on exact product data: wattage, lumens, voltage, CCT, dimming, certifications, category placement, compatibility, images, spec sheets, and BigCommerce publishing rules.Rastro turned supplier-sheet ingestion, pricing normalization, and web enrichment into a repeatable catalog launch workflow — taking supplier launches from weeks of cleanup to days.
Context
Supplier files rarely arrive as launch-ready catalog data. Price sheets may contain costs, order multiples, MAP/MSRP, and model numbers, while content files may carry partial descriptions or image references. Critical contractor-facing details often live elsewhere: manufacturer pages, PDF spec sheets, image libraries, or legacy product records. Manually reconciling all of that does not scale, and small mistakes can break filters, categories, variant options, pricing, or customer trust.
Rastro combined supplier price sheets, load files, content spreadsheets, manufacturer product pages, PDFs, image assets, and existing catalog records, then normalized the findings into RelightDepot's schema with QA gates before publish — turning one-off cleanup projects into a repeatable catalog launch workflow.
What Rastro Did
1. Supplier Sheet Ingestion
Parsed supplier price sheets and load files to extract costs, MAP/MSRP signals, order multiples, SKU patterns, and product groupings — then mapped supplier rows into BigCommerce-ready products, variants, categories, prices, native fields, and custom fields.
2. Web Enrichment
Crawled manufacturer product pages and PDFs to recover missing specs, resource links, images, product-page URLs, and compatibility signals — capturing 8,400+ product and document URLs for enrichment and source traceability.
3. Content Generation
Generated concise product copy, meta descriptions, short descriptions, search terms, and technical spec sections from supplier and web-sourced data — including 66,000+ technical spec rows during a large catalog rebuild.
4. QA Gates and Review Routing
Routed uncertain values and business-sensitive choices into review, while allowing high-confidence records to move through repeatable validation gates — codifying 87 reusable QA rules to prevent regressions before publish.
Process
Ingest
Pull supplier price sheets, load files, and content spreadsheets into a normalized staging layer.
Enrich
Crawl manufacturer pages, PDFs, and image libraries to fill source gaps with traceable references.
Normalize
Map every record into RelightDepot's BigCommerce schema: products, variants, categories, prices, and custom fields.
Review
Route uncertain values and business-sensitive decisions into human review queues.
Publish
Push approved records through validation gates backed by 87 reusable QA rules.
Verify
Audit the live catalog to confirm pricing, taxonomy, content, images, and variants behave as expected.
Results
For a lighting distributor, catalog quality is not cosmetic. It determines whether contractors can find the right product, trust the specs, compare options, and buy with confidence. A supplier launch that could otherwise stretch into four to six weeks of cleanup can now be live in days.
Want supplier launches in days, not weeks?
Let's walk through your supplier files and a small test batch together.