AI-powered · Per-axis subgrades

AI card grading, engineered for accuracy.

SnapGrade uses AI vision models to analyze your card photos and predict a grade — calibrated against real PSA returns, with a confidence score on every prediction.

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AI-powered vision412 real PSA comparisons87% within ±0.5Open track record
14,200+
Cards graded
87%
Match within ±0.5
5,600+
Active collectors
$2.4M
PSA fees saved
How it works

How SnapGrade builds an AI card grading prediction

  1. Photograph front and back

    In even lighting, with no glare and the card filling the frame, take one photo of the front and one of the back.

  2. Upload from any device

    Drop both photos into SnapGrade from any phone or desktop browser — no app to install. Live image-quality checks flag a photo before it produces an unreliable prediction.

  3. Get a per-axis prediction

    SnapGrade analyzes centering, corners, edges and surface separately, each with its own confidence level, then combines them into a predicted PSA grade.

  4. Trust the confidence score

    If overall confidence falls below 70%, the credit refunds automatically instead of charging you for an unreliable guess.

How a grade is built

One photo, four axes.

Every prediction is broken down by grading axis, not just one overall number — so you can see exactly where a card is strong or weak.

Centering

Border-ratio analysis produces left/right and top/bottom centering estimates.

Corners

Flags whitening, rounding and corner chips.

Edges

Flags nicks, dings and bevel wear, including holo-edge silvering.

Surface

Flags scratches, print lines, holo scratches and surface artifacts.

Calibrated confidence

Every prediction carries a confidence score. When confidence drops below 70%, you get a refund automatically.

Named signals, not just a number

Each subgrade shows the specific signals behind it — e.g. tip sharpness, whitening, chipping — not just a rate.

Why a calibrated prediction beats a generic "what grade is this" answer

You can ask a general AI chatbot "what grade is this card?" and get a confident-sounding answer — but with no calibration against actual PSA outcomes, there's no way to know how reliable that answer is.

SnapGrade's prediction is calibrated against 412 real PSA-return comparisons, broken down per axis (centering, corners, edges, surface) with its own confidence level. The result is a grade we can stand behind — and refund automatically if our confidence is low.

Calibration figure

Common questions

Questions we hear all the time.

What powers SnapGrade's predictions?
AI vision models, calibrated against real PSA return outcomes and constrained to a structured, per-axis subgrade output — not a freeform chatbot answer.
How does the AI handle edge cases or rare cards?
For very rare prints, our model's confidence drops and we refund the credit automatically. We'd rather not charge than charge for an unreliable grade.
Can I see what the AI is "looking at"?
Yes — every report shows per-aspect confidence and the named signals (e.g. whitening, chipping, tip sharpness) behind each subgrade.
Is my card data used to train the model?
Only with opt-in consent. By default, your card photos are kept private to your account.
How often does the calibration figure update?
As we log more real PSA-return comparisons — see the Track Record page for the current number and how we get it.
Ready when you are

Try a prediction
right now.

Sign up free, get 1 free credit, and see what a calibrated AI prediction actually looks like — with subgrade-level confidence, not a chatbot guess.

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+2 credits
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