SEED LR identifies language risks in customer-facing AI before they reach users.
Teams find these problems after launch. Rollback requires legal, comms, and leadership alignment. SEED LR surfaces failure modes before they become incidents.
- When users interpret AI text as instruction or authority.
- When safety language reads as dismissive under stress.
- When internal review passed, but external reaction did not.
No signup required. Results in seconds.
GitHub Action availableInput tested
“Your account is definitely safe and fully compliant, so proceed immediately.”
Flags detected
guarantee_absolute_claims
"definitely safe and fully compliant"
→ Flagged by all 6 interpreters
capability_overstatement
"fully compliant"
→ Flagged by all 6 interpreters
timeline_promises
"proceed immediately"
→ Fintech Risk Officer · Literal · Worst-Case
This language would not survive a compliance review. SEED LR caught it before it reached a customer.
Six adversarial interpreter profiles
Every output is evaluated through six adversarial lenses simultaneously. Each flag names the interpreter that caught it.
Reads for regulatory exposure and fiduciary liability.
Applies strict literal reading to every claim and qualifier.
Tests against documented policy and disclosure standards.
Identifies social engineering and information hazard patterns.
Takes every word at face value. No charitable reads.
Assumes the most damaging plausible interpretation.
How it works
Intake
Submit text surface with release context and attribution metadata.
Deterministic Runs
Fixed interpreter passes establish a stable, reproducible baseline score.
Stochastic Runs
Variance runs surface framing sensitivity and disagreement patterns.
Multi-Lens Scoring
Six adversarial profiles score independently, then aggregate.
Evidence Capture
Each flag is anchored to the exact phrase that triggered it.
Gate Recommendation
SHIP · HOLD · ESCALATE decision delivered with artifact for sign-off.
Built by someone who knows what breaks in review.
SEED LR was built by B McGhee, a senior QA/SDET engineer with a background in automated evaluation systems. The interpreter profiles were designed to simulate the actual readers your language reaches: not just an average user, but the compliance officer, the anxious customer, the literal interpreter, and the worst-case reader. This is stress-testing, not sentiment analysis.
B McGhee on LinkedInReady to scope a language audit?
Typically run immediately before launch or a major copy change.