Stop gambling
with your cards.
57% of PSA submissions don't gem. AuraGrade gives you the honest odds in 7 seconds — before you spend $25–$600 and wait months to find out. You get the math. You make the call.
Garbage in, garbage out. Never.
Bad photos make bad predictions. Our client-side validator brutally rejects sub-threshold captures and tells you exactly how to fix them. Front, back, corner macros, or an 8-second video walkaround — we only run inference on clean, high-quality data. Perfect input. Calibrated output.
Required Captures PER SCAN
Quality Thresholds
Evaluated client-side via WebGL/HTML5 Canvas. No network round-trip required to reject a bad capture.
Edge Validator
Live // 60 HzRetake Feedback
Failed captures return one actionable instruction — never a generic error. Examples returned by the validator:
One 8-second video. Zero retakes.
Stop taking 15 pictures and praying. Record a slow walkaround. Our frame-extraction pipeline pulls the best frames, kills specular glare, and builds a stronger analysis than any single photo ever could. Smarter capture. Stronger predictions.
One weak pillar tanks the whole card.
We analyze Centering, Corners, Edges, and Surface independently — then apply the real floor logic PSA uses. No hopium, no averaging — just cold math on what your card can actually achieve.
Centering
Algorithmic pixel counting via Sobel edge detection — not a deep-learning guess. Enforces PSA's 55/45 front threshold for Gem Mint 10 with mathematical certainty.
Corners
Dedicated MTL head trained on 20K hard-negative crops. Detects micro-fraying and whitening at radii invisible to the naked eye.
Edges
Perimeter segmentation identifies silvering and rough factory cuts on vintage stock — defect classes most consumer-grade graders miss entirely.
Surface
Trained against synthetic glare and shadow augmentations so the network learns invariant defect features — dimples, print lines, scratches — even under bad smartphone lighting.
We don't promise PSA 10s. We give you the odds.
Three scenarios. Three calibrated outputs. Three decisions. Each card shows the AuraGrade prediction, the reasoning, and the band-level PSA reality if your card lands there — backed by 20,000 PSA-verified training samples and a continuous telemetry loop against real submission outcomes. When we say 72% PSA 10, the long-run resolution rate is ~72%. That's calibration, not overconfidence. You get the odds. You make the call.
Of every 100 cards we tag "72% PSA 10," roughly 72 return PSA 10 from PSA.
Calibration is the difference between a confident guess and a trustworthy probability. The Live User Telemetry channel (3,000+ freemium-beta captures, growing) closes the loop continuously: we record what we predicted, then we record what PSA actually returned. Every miscalibration becomes training data. The honest review is the moat — confident lies are a one-time trick.
A bad submission
is brutally expensive.
$25 to $600 per card. 5 days to 7+ months of your money tied up. 57% of submissions return below Gem Mint 10. AuraGrade costs pennies per scan and saves you from even one bad submission.
below Gem Mint 10
per card // sub-10s
That's not a tool. That's insurance.
The axes that actually matter.
First-generation AI graders validated the demand but failed the execution. We compete on the dimensions where their architectures collapse: surface-defect recall under variable lighting, sub-grade granularity, cryptographic transparency, and permanent data ownership.
A 20,000-image defensible moat.
Each label is cryptographically verified: OCR extracts the PSA cert number from the slab, then queries PSA's official Verification API to confirm the grade, set, year, and qualifiers. No seller descriptions. No mislabeled samples. No training contamination.
From pre-grader to grading authority.
The pre-grading product is the wedge. The proprietary corpus is the moat. The endgame is replacing the incumbent grading paradigm with cryptographic transparency.
AI Pre-Grader
SaaS pre-submission filter. Every paid scan generates cash flow and crowdsources real-world user images into the proprietary training corpus.
Hybrid Encapsulation
High-confidence pre-scans get invited to mail in for physical processing under controlled multi-spectral lighting. AI does 95% of evaluation; human technicians do final QA + sealing.
Defect Map →
Cryptographic Authority
Every slab carries a QR linking to a permanent on-chain ledger entry with the pixel-level defect map and per-pillar reasoning. Algorithmic transparency replaces the opaque legacy integer.