Proof for every decisionyour AI makes.
VeriAuth attests what your AI took in, how it decided, and what it produced — into signed, tamper-evident records that anyone can verify without trusting the operator. One engine. Any model. Any domain.
A self-attested log proves nothing.
AI now makes decisions that affect money, health, eligibility and rights. When one is challenged, the operator is asked to prove what actually happened — and can't. A row in a database you control is not evidence to anyone who doesn't trust you.
Unprovable
Your logs are written by you and editable by you. In a dispute, audit or court they carry no forensic weight — they're a claim, not proof.
Unverifiable
No independent party can confirm which model ran, on what input, to produce which output. “Trust us” is the only assurance on offer.
Un-backfillable
Tamper-evident history only exists for decisions attested as they happen. The day you need the proof is the day it's too late to start collecting it.
What the gap exposes you to.
Two kinds of risk follow from unprovable AI: concrete attacks that are invisible without attestation, and a fast-tightening regulatory expectation to demonstrate what your AI did.
Repudiation
A customer or counterparty disputes a decision and you cannot prove what your AI actually did.
Model-swap
A tampered or unapproved model is substituted and runs silently. You can't prove which version decided.
Evidence tampering
Decision history is deleted or rewritten after the fact — the exact records a regulator would demand.
Output tampering
An outcome is altered in transit or at rest with nothing to detect the change.
Data exfiltration
Sensitive data flows to an external AI service with no attested control over what was sent or used.
Examination failure
Asked for your AI decision trail, you assemble raw logs by hand — the worst posture under scrutiny.
RBI FREE-AI (India)
AdvisoryThe 2025 framework recommends audit trails, model-risk governance and assurance for AI in regulated finance. Advisory — but it signals where supervision is heading, and RBI enforces the underlying obligations through its existing powers.
DPDP Act 2023 (India)
StatutoryFixed, per-violation penalties for failing to protect personal data — up to ₹250 cr (security safeguards) and ₹200 cr (breach notification), imposed by the Data Protection Board.
EU AI Act
StatutoryHigh-risk AI systems carry record-keeping, traceability and post-market monitoring obligations — provable logging of how decisions were produced.
The global pattern
EmergingAcross sectors and jurisdictions the same expectation is forming: if an AI decides, you must be able to demonstrate — provably — what it did.
Attest three points. Seal it. Let anyone verify.
VeriAuth wraps around your AI without changing what it does. It captures the decision at three points, binds them into one signed certificate, and writes it to a tamper-evident log that any third party can check — with no access to your systems and no trust in you.
Domain-agnostic core
The model is treated as opaque; inputs and outputs are plain data. Only a compliance pack is domain-specific. Swap the pack, attest a different domain.
Consume hardware trust
Signing binds to an HSM / secure element via standard interfaces. We consume hardware roots of trust — we don't ask you to build them.
Erase & retain, at once
Per-subject key-shredding lets you honour a data-erasure request while the tamper-evident proof of the record survives — resolving retention vs. erasure.
One engine. Every domain that needs independent verification.
If a party who doesn't trust the operator must rely on an AI outcome, VeriAuth fits. Financial services is our live beachhead — not our boundary.
Financial services
LiveCredit, fraud, KYC/AML and collections decisions — attested for disputes, audits and RBI examinations.
Healthcare
ReadyDiagnostic and triage recommendations — provable model, inputs and output for clinical accountability.
Insurance
ReadyUnderwriting and claims decisions — verifiable records for contested outcomes and liability exposure.
Hiring & HR
ReadyAutomated screening and ranking — an audit-ready trail for fairness and challenge.
Public sector
ReadyBenefit eligibility and allocation — attestation citizens and oversight bodies can independently check.
Agentic AI & tool-use
ReadyAutonomous agents taking actions — a signed record of what an agent decided and did, per step.
Three calls. Any stack.
Register a model, attest a decision, verify a certificate. That's the whole surface. The engine runs in your boundary; sensitive data never leaves.
# Attest any AI decision — model is opaque, inputs/outputs are plain data from veriauth import VeriAuth va = VeriAuth(api_key="va_live_…") result = va.attest( model=risk_model, # any framework, treated as opaque inputs={"features": features}, # hashed, never stored raw output={"decision": "APPROVE", "confidence": 0.91}, use_case="credit", # the only domain-specific field ) print(result.cert_id, result.signature) # signed, tamper-evident receipt
Teaser surface shown. Full spec, SDKs and self-host guide available on request.
Find your gaps before someone else does.
Two ways to see where your AI decisions are unprovable — pick the one that fits how much you want to share. Both run without sending us your code.
Readiness self-assessment
Answer eight questions about how your AI decisions are recorded. Get a report mapping each gap to regulation and penalty exposure — instantly, in your browser.
- Nothing leaves your machine until you choose
- Statutory / advisory split shown honestly
- Downloadable report for your board
VeriScan code scan
Point VeriScan at your code — paste it, upload a .zip, or give a public Git repo or file URL. It flags every place a decision or outcome runs without attestation, maps findings to clauses and penalties, and returns a preview instantly.
- Static, read-only — no code executed, scanned in memory
- Findings mapped to VeriAuth modules & fixes
- You see a preview; the full report reaches our team