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Every AI output, signed and accountable.

Cryptographic signatures that bind AI outputs to the model, prompt, data and policy that produced them.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: Cryptographic attestation for AI-generated content

Where you are. Where you’ll be.

You need this if

  • AI makes or informs decisions about customers
  • You operate in a regulated sector with record-keeping rules
  • You can't reconstruct why an AI system gave a specific answer

What changes for your business

  • Defensible AI decisions in disputes and audits
  • Tamper-evident records of what your AI produced
  • Faster incident reviews when something goes wrong

What we hand over

  1. Signing and attestation architecture for AI outputs
  2. Tamper-evident logging of model, inputs and policies
  3. Verification APIs for internal and external parties
  4. Retention and audit-access procedures

What it is

AI content attestation is a signed, tamper-evident record created with each AI output, showing which model and version produced it, from which inputs, after which checks. It turns 'why did the AI say that?' from guesswork into a lookup, which regulators, auditors and courts increasingly expect.

When an AI system makes a recommendation that affects a customer, you need to show how it got there. We build attestation into AI pipelines: each output is signed with a record of the model version, input sources and policy checks applied, stored tamper-evidently. Disputes, audits and incident reviews start from evidence instead of reconstruction.

Why now
30% of breaches now involve a third party, double the year before. Verizon Data Breach Investigations Report, 2025 (opens in a new tab)
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

When do we need attestation for AI outputs?
When AI makes or informs decisions about customers, such as credit, claims, pricing or eligibility, or when sector rules require you to keep records of how decisions were made. If you can't reconstruct why an AI gave a specific answer, you need it.
What does an attestation record contain?
The model and prompt versions, the retrieved sources or inputs, the policy and safety checks applied, and a timestamp, all signed and stored in a tamper-evident log linked to the output.
Does this slow down our AI system?
Barely. Signing and logging add milliseconds per request. Storage is the main cost, and retention is set to match your regulatory record-keeping period.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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