Where a proof
beats a promise.
Give customers, partners and reviewers evidence they can check. Explore where verifiable inference could strengthen your workflow, starting with small numeric models.
Give customers proof of the agreed model
When a customer selects a model version, a label in an API response offers limited evidence. A cryptographic receipt can connect each result to the weights both parties agreed on.
For providers of small scoring models, this offers a way to make model-version commitments independently checkable. Customers can verify the result without inspecting your infrastructure.
The provider must support proof generation. An existing third-party API response cannot be turned into this evidence on its own.
Keep evidence behind important decisions
A score influences a price, a ranking or an eligibility decision. When someone asks how that score was calculated, a receipt can establish its relationship to an approved model and committed input.
This gives reviewers a specific computation to verify. Accuracy, fairness and the authenticity of the source data remain separate parts of the review.
Start with one scoring step and define which evidence the reviewer needs.
Show your work. Keep your weights.
Your model is valuable intellectual property. Customers still need confidence in the results it produces.
Zero-knowledge proofs let a customer verify that a score follows from committed model weights and input values without receiving those private values. The result itself is public and may still reveal information, so privacy needs to be assessed across the whole workflow.
A strong starting point is a small proprietary scoring model with a clear need for independent verification.
Verify what changed when a model learns
A new checkpoint changes the model you rely on. An update proof can establish that the new weights follow from the previous checkpoint, committed training data and a specified update rule.
Inferstamp demonstrates this for a small linear model, checking each update before accepting the next checkpoint. Correct execution of an update does not establish that the model improved.
The current implementation checks individual updates. It does not provide a single proof of an entire training history.
Share results with verifiable evidence
A result may pass from a supplier to a customer, then to an independent reviewer. The evidence should remain useful at each handover.
A proof receipt can be checked using public verification files and the agreed model and input fingerprints. Recipients can verify the recorded computation without receiving the private weights or inputs.
The parties must agree on what the fingerprints identify. Freshness and acceptance policies need additional checks.
Choose the right level of verification
A proof is useful when independent verification justifies the cost of generating it. Your assessment compares that cost with the alternatives:
- Re-run an accessible model. If the model and input can be shared, direct recomputation may be simpler.
- Authenticate a trusted provider. Signed responses may be enough when you need evidence of who issued a result.
- Verify the data source. A computation proof cannot establish that the underlying data is true.
- Check model fit. Inferstamp currently demonstrates a small numeric model. Full LLM inference is outside its scope.
- Define the review requirements. A cryptographic proof does not by itself establish regulatory compliance.
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