Example records

See what a record looks like before you make one.

One regression check and five model-comparison records. Each states where its data came from, how many cases it holds, how it was scored, and what it does not show. Open any of them and check the evidence case by case.

No client data and no PHI on any record. Each record states its own origin below.

A regression check

Synthetic · no model ran

Support-ticket router, one feature, two versions, the same 40 cases.

A router labels each ticket with one of four labels: billing, technical, account, or shipping. The reference run is version 1. Candidate v2 is checked against it under a policy written before anyone looked at the candidate: minimum exact match 0.95 and maximum regression 0.05.

failcandidate v2 did not meet the requirements

Candidate v2 scored 0.90 exact match. 4 of 40 cases changed, each from the correct label to a wrong one. Both quality rules were breached, so redcrown check exited 2. The control check, version 1 against itself, passes.

Limitation. The tickets and the router outputs are invented, and no model produced them. A synthetic scored-output fixture is not a RedCrown-witnessed model execution; it shows the shape of a check, not a result about any product.

Task
Support-ticket router, 4 labels
Origin
Synthetic: invented tickets and outputs, no model ran
Sample
40 cases
Scoring
Exact match against the reference label
Requirements
Minimum 0.95, maximum regression 0.05
Result
Candidate v2: 0.90, 4 of 40 cases changed, did not meet the requirements. Control: pass.
Date
2026-09-17
Runs offline, no account, no provider keyRun it yourself →

Model-comparison records

Each tile is one real run across several models, ranked on the quality bar it states. Origin, per record: the transcription clips, support tickets, contracts, and document images are synthetic. The reasoning record uses public benchmark data, a pinned 50-question subset of MMLU-Pro. Sample sizes and caveats are on each tile and on the record behind it.

TranscriptionNormalized WER
whisper · large-v3-turbo
✓ accuracy 0.94, level with the incumbent
−49%
✓ large-v3-turbo0.94
large-v3 (incumbent)0.94
8 synthetic clinical clipsreceipts →
SummarizationLLM judge
qwen3 · 235b
✓ judge score 0.78, above the incumbent
−99%
✓ qwen3-235b0.78
gpt-5.5 (incumbent)0.77
52 synthetic tickets · 5 modelsreceipts →
ExtractionField F1
qwen3 · 235b
✓ F1 0.98, clears the 0.80 bar
−54%
✓ qwen3-235b0.98
gpt-4o-mini (incumbent)1.00
52 synthetic contracts · 5 modelsreceipts →
Vision / OCRField F1
llama-4 · maverick
✓ F1 1.00 on document fields
−85%
✓ llama-4-maverick1.00
gemini-3.5-flash (incumbent)1.00
52 synthetic document images · 3 modelsreceipts →
ReasoningExact-match + cost-per-correct
deepseek · v4-flash
✓ cheapest over the 0.75 bar
−96%
✓ deepseek-v4-flash0.76
claude-opus-4.8 (incumbent)0.88
Known limit of this record: some outputs were truncated and scored 0. The record states it in full.
MMLU-Pro, 50 pinned questions · 6 modelsreceipts →