AI Truth Filter
Verify AI output before you trust it
Paste any AI-generated text and get a reliability score (0–100)
12 free checks per day — no API key needed
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0.38
0.42
Trend Overclaim
1
S→T
Overgeneralized
1
L→G
Unsupported Leap
2
Δe→∫de
Unreliable
This board deck presents optimistic projections as established fact. Pipeline figures are treated as confirmed revenue, single-quarter metrics are called industry-leading without benchmarks, and future efficiency claims lack supporting pilot data.
- Pipeline-based revenue projection presented as a confirmed growth trajectory
- Churn rate compared to unnamed industry benchmark — no source or methodology
- NPS score used as sole proof of customer satisfaction without context or trend data
- AI efficiency gains projected to 2027 without pilot results or implementation plan
Dimension Scores▼
Claim-Level Audit▶
Example output — paste your own text above to get a live analysis.
The question isn't whether the answer
is right or wrong.
Three AIs. Same report. None of them warned you.
Runs inside Claude and ChatGPT. 300 free credits.
Runs inside your existing AI workflow.
No integration project. No architecture change.
What happens when you run TPMN Checker
Four steps. Fully automated.
Input
Paste any AI-generated text — a report, forecast, analysis, PRD, research summary.
Score
Seven dimensions evaluated independently. Each scored 0.0–1.0.
Flag
Structural logic errors detected: S→T, L→G, Δe→∫de violations.
Rewrite
Auto-compose strips overclaims. Before and after, side by side.
Seven dimensions scored
| Dimension | What it catches |
|---|---|
| Source Attribution | Claims with no traceable evidence |
| Evidence Quality | Thin or outdated supporting data |
| Claim Grounding | Assertions presented as fact without basis |
| Temporal Validity | Stale data treated as current |
| Scope Accuracy | Local findings overgeneralized |
| Logical Consistency | Internal contradictions |
| Prompt Alignment | Does the output match what was asked? |
S→T
Snapshot treated as permanent trend
L→G
Local truth presented as universal
Δe→∫de
Sweeping claim from thin evidence
AI scores. You adjust. The standard improves.
Today, AI evaluates its own output. But who decides what “correct” looks like?
Not us alone. You.
When you use TPMN Checker, you see scores across seven dimensions. If you disagree with a score, that disagreement is data. Collected with your consent, aggregated across users, and analyzed for patterns — your evaluations become the ground truth.
Human at the edge, not in the loop.
Humans set the standard. AI enforces it at scale.
Start verifying AI output now
Free access
- 1
Connect in 30 seconds (Claude.ai, ChatGPT, or Cursor)
- 2
Ask your AI tool to run
gem2_truth_filter - 3
See your first reliability report
No credit card. No setup project. 300 free credits.
Pre-GA — lock in pricing before General Availability
| Tier | Price | Credits | RPM |
|---|---|---|---|
| Free | $0 | 300 | 5 |
| Starter | $9 | 2,000 | 15 |
| Builder | $19 | 9,000 | 30 |
| Founder | $29 | ∞ | 60 |
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