SyncLogic Audit Framework

Audit an AI output before you rely on it

Great answers aren't enough. Trust requires proof.

SyncLogic audits AI outputs so you know how a conclusion was reached, what supports it, where it can fail, and what it's safe to be used for.

AuthorWalter Shepherd
Published24 July 2026
Updated26 July 2026
Version2.1.0
StatusPublic Research and Pilot Release
Primary questionHow do you audit an AI output?

Transform any AI output into an audited, evidence-based result with a clear level of reliance and a defined permitted use.

The SyncLogic Difference

1

Looks beyond the answer to the reasoning behind it

2

Traces every claim to sources, data or assumptions

3

Tests alternatives, contradictions and edge cases

4

Finds the weakest link that limits reliability

5

Assigns a Reliance Class (RC 0–7) and states what it's safe for

6

Issues a Permission-to-Rely verdict for the intended use

The ten-step audit

The same disciplined method, applied to this claim type: define, decompose, register evidence, test admissibility, explore alternatives, find the weakest link, assign reliance, and issue a verdict.

1

Define the Ask & Scope

Clarify the exact question, audience and intended use. Lock the scope to prevent drift.

What exactly is the output being asked to do?
2

Decompose the Output

Break the output into explicit claims, sub-claims, assumptions and inferences.

What must be true for this output to hold?
3

Evidence Discovery & Registration

Identify and register all sources, data, examples and references used or implied.

What evidence supports each claim?
4

Source Admissibility (HAT-7)

Evaluate each source for relevance, authority, timeliness, transparency, methodology, jurisdiction and limitations.

Is each source fit for this claim and purpose?
5

Alternatives & Contradictions (Fork First)

Identify credible alternative explanations and contradicting evidence. Test what would change the conclusion.

What else could explain this? What contradicts?
6

Analyze Links & Assumptions

Check causal or logical links. Flag hidden assumptions, generalizations and unstated premises.

Which links or assumptions are most fragile?
7

Find the Weakest Link

Locate the claim, assumption or data gap that most limits the reliability of the entire output.

Where could this output break first?
8

Reliance Class (RC 0–7)

Rate overall reliability based on evidence strength, uncertainty, dependency depth and sensitivity.

How much reliance is justified?
9

Permission-to-Rely Verdict

State what the output can be used for—and at what level (explore, inform, decide, act).

What is this safe to be used for, right now?
10

Document & Make Reproducible

Produce a transparent audit record others can inspect, challenge and reproduce.

Can others see, test and reproduce this audit?

The risks a surface-level review misses

Confident answers can be wrong

SyncLogic tests the reasoning, not just the tone.

Hallucinations and fake sources

Every source is checked for existence and admissibility.

Scope drift and overstatement

Scope Lock and decomposition keep conclusions in bounds.

Missing alternatives and bias

Fork First forces consideration of real alternatives.

Models simplify uncertainty

We quantify uncertainty and identify the weakest link.

No clarity on what it's safe for

Reliance Class + Permission-to-Rely define safe use.

No audit trail or reproducibility

Transparent records make the output inspectable and repeatable.

The Bottom Line

AI can generate answers in seconds. SyncLogic delivers assurance in depth. We turn fluent text into trustworthy, auditable, decision-grade results.

The audit output

  • Claim decomposition map
  • Source register with HAT-7 ratings
  • Evidence strength summary
  • Alternative analysis & contradictions
  • Weakest link identification
  • Reliance Class (RC 0–7)
  • Permission-to-Rely verdict
  • Transparent audit report
  • Reproducible audit package

Where this audit applies

Business analysis & strategy
Research summaries
Legal & compliance
Technical reports
Code & documentation
Market & competitor intel
Medical & health information
Policy & regulatory advice
Risk & threat assessments
Education & learning content

Illustrative example: an AI procurement recommendation

This is an illustrative method example, not a completed client audit or an independently validated finding.

“Adopting Supplier A’s AI assistant will reduce customer-service costs by 30% within six months.”

Decision context

The claim is intended to support a three-year procurement decision, so a polished forecast is not enough.

Claim decomposition

Separate the statement into adoption feasibility, cost baseline, expected saving, implementation period, service quality and supplier capability claims.

Evidence check

Register the supplier model, internal cost data, pilot evidence, integration assumptions and any independent benchmarks.

Alternatives

Compare a limited pilot, process redesign, another supplier and no-change scenario using the same criteria.

Weakest link

The forecast depends on an untested assumption that automation rates from another organisation will transfer to this organisation.

Reliance finding

Suitable for a controlled pilot proposal; not yet suitable for organisation-wide procurement or a guaranteed savings claim.

Reliance note: the example demonstrates the audit pathway. It does not establish the underlying claim as true or false.

Trusted. Transparent. Decision-grade.

TRUST THE REASONING.
RELY ON THE RESULT.