Multistage Card Identification
Other tools run your card through a single AI model and call it done. CardLuma uses a multi-stage pipeline — image analysis, AI identification, database verification, and automated listing generation — so every field is accurate before it ever reaches your listing.
Four Stages. One Verified Result.
Every card passes through multiple verification stages before a single field is populated. Here's exactly what happens under the hood.
Image Upload & Analysis
The pipeline starts the moment you upload a card image. CardLuma analyzes both front and back photos using OCR and visual pattern recognition to extract every readable detail — player names, card numbers, set logos, team marks, serial numbers, and design elements.
This isn't a simple text scan. The system reads the visual language of the card itself: border colors that indicate parallels, foil patterns that distinguish inserts, and design cues that narrow down the exact set and year before the AI even begins identification.
- OCR text extraction from front and back
- Visual pattern matching for set design recognition
- Parallel and insert detection via border/foil analysis
- Serial number and patch window identification
- Batch upload support for high-volume workflows
AI Identification
With the visual data extracted, CardLuma's AI engine takes over. Multi-model analysis cross-references what was found in the image against known card patterns to identify the year, set, player, card number, parallel type, and special attributes.
The AI doesn't guess. It evaluates multiple candidate matches simultaneously, weighing visual evidence alongside text data to arrive at the most accurate identification. Rare parallels, short prints, and numbered cards are flagged automatically — the details that matter most for pricing.
- Year and set identification from visual + text signals
- Player name and card number matching
- Parallel and variety classification
- Autograph and memorabilia patch detection
- Multi-candidate ranking for edge cases
Database Verification
This is where CardLuma separates itself. Every AI match is cross-referenced against a curated catalog of 8.9 million+ card records. The database doesn't just confirm the card exists — it enriches the result with verified metadata that the AI alone can't provide.
Rookie Card status, Hall of Fame flags, team history, feature tags — all verified at the record level against authoritative data. This stage catches the edge cases that trip up AI-only systems: a player who changed teams mid-season, a set with multiple base card designs, or a parallel that shares the same card number as the base.
- Match confirmation against 10.04+M verified records
- Rookie Card (RC) status verification
- Hall of Fame (HOF) flag enrichment
- Team and conference data at time of card release
- Feature tags: Rookie, All-Star, MVP, etc.
Complete Listing Generation
With the card fully identified and verified, CardLuma assembles the complete listing. Over 20 card attributes are populated automatically — from the eBay title built to maximize search visibility, to every Item Specific that drives filter exposure.
The title builder intelligently fits the most valuable keywords within eBay's 80-character limit, using automatic team name abbreviations when space is tight. Item Specifics, store categories, shipping policies, and best offer rules are all applied based on your configured automation — so every listing ships out consistent, complete, and ready to sell.
- 20+ card attributes auto-populated
- SEO-optimized title built to eBay's 80-char limit
- All Item Specifics set for maximum filter exposure
- Store category, shipping, and offer rules auto-applied
- Combined front + back image generated automatically
Why Multistage Matters
A single AI model can identify a card. It takes a pipeline to identify it correctly, consistently, and completely.
Verified at Every Step
Each stage checks the work of the one before it. The result: fewer errors, richer data, and listings that are complete before you ever review them.
- AI results verified against curated database
- Rookie, HOF, and feature flags added automatically
- Edge cases caught before they become listing errors
- 20+ attributes populated per card
- Consistent accuracy across all card types
One Model. One Chance.
Run an image through a single model and hope it gets everything right. No verification, no enrichment, no safety net.
- No database cross-reference
- Rookie and HOF status missed or guessed
- Parallel misidentification on similar designs
- Incomplete Item Specifics left empty
- Accuracy drops on vintage and obscure sets
Catch What AI Misses
Database verification catches the parallels, rookies, and features that single-pass AI overlooks. Every listing is enriched, not just identified.
Speed Without Shortcuts
Four stages, under 5 seconds. The pipeline runs in sequence but operates at speed — no waiting, no manual intervention between stages.
Value You'd Otherwise Miss
A missed HOF tag or RC flag means less search visibility and lower sell-through. The pipeline ensures value-driving details land in every listing.
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