How It Works

A clear review workflow that produces documented findings.

SEED LR runs repeated evaluations to surface stable signals, sensitive language, and disagreement patterns. Traceable, audit-ready artifacts at every step.

The Landscape

How teams evaluate AI today

Most AI teams ship with some combination of these practices:

Golden dataset evals – A curated set of inputs with expected outputs, scored before each deploy. Catches capability regression. Does not catch how language lands with real readers.

Shadow mode – New model runs alongside the old one, outputs compared but not served. Surfaces behavioral divergence. Does not surface phrasing risk.

Human review queues – Sample a percentage of live outputs, route to internal reviewers. Expensive, slow, and does not scale to every release.

LLM-as-judge – A separate model scores outputs against a rubric. Fast and cheap. Inherits the same blind spots as the model being evaluated.

Red-teaming – Adversarial prompts designed to break the model. Usually manual or semi-automated. Focused on capability failure, not language behavior.

None of these evaluate how an output reads to a compliance officer, a regulator, a distressed user, or a worst-case interpreter. That is what SEED does.

Intake

Submit text tied to a release, workflow, or decision surface.

Each submission is stored with context and metadata for traceability. You define the surface: product copy, system prompt, error message, disclosure text.

Deterministic Runs

Fixed interpreter passes establish a stable, reproducible baseline score.

The same inputs are evaluated with the same interpreter configurations to produce a consistent reference point. Variance is treated as signal, not noise.

Stochastic Runs

Variance runs surface framing sensitivity and disagreement patterns.

Repeated evaluations with slight input perturbations reveal which language is stable under reframing and which is sensitive to interpretation context.

Multi-Lens Scoring

Six adversarial profiles score independently, then aggregate.

Fintech Risk Officer, Auditor Formalism, Compliance, Security Threat Model, Literal, and Worst-Case each evaluate independently. Disagreement patterns are surfaced explicitly.

Evidence Capture

Each flag is anchored to the exact phrase that triggered it.

Flags include the concern name, the triggering phrase, and the interpreter that raised it. Nothing is unattributed. Every finding is traceable to a source.

Gate Recommendation

SHIP · HOLD · ESCALATE decision delivered with artifact for sign-off.

The artifact is audit-ready: timestamped, attributed, and structured for human review. Your team owns the final decision. SEED LR provides the evidence.

What you receive

Evaluation artifact with ID, timestamp, and all flag evidence
SHIP · HOLD · ESCALATE gate recommendation
Interpreter disagreement patterns and variance analysis
Exact phrase anchoring for every flag
Audit-ready markdown report for leadership review

Request Audit Scope View pricing →GitHub Action for CI/CD integration