Random samples miss production shape
Unrepresentative data leads to over-optimistic results that do not hold in production.
MT Evaluation & Readiness Audit
A controlled benchmark for choosing translation systems with confidence.
Prompsit designs reproducible MT benchmarks that compare providers and custom models across quality, structural fidelity, latency, and statistical reliability.
Quality
Human, LLM and metric signals
Structure
Tags, placeholders and markup
Latency
Throughput-based stress testing
Evidence
Confidence, agreement and significance
Why MT benchmarks fail
Real-world deployments expose translation systems to content, formatting and workloads that random samples and single metrics do not capture.
One score hides different risks
A single metric cannot reveal issues in structure, locale coverage or system stability.
Fluent text can still break UI
Tags, placeholders and variables may be altered, dropped or misplaced.
Fast systems can fail under real throughput
Latency and errors often emerge only under realistic concurrency and load.
Production-shaped benchmark design
We build datasets that mirror production: preserving language distribution, content types, request size, segment length and formatting complexity.The result is a benchmark that predicts real-world performance.
Controlled provider comparison
Same inputs, blind outputs, anonymised providers and shuffled order reduce evaluation bias.Prompsit prepares blind evaluation packages and analyses human labels when available.
Quality evaluation with multiple signals
Human labels
Expert linguists assess adequacy and fluency in context, with rubric-driven scoring.
- Locale-specific reviewers
- Adequacy and fluency
- Error categorisation
- Confidence scoring
LLM judges
Calibrated LLM judges provide scalable, reproducible evaluations.
- Pairwise and absolute scoring
- Rubric-aligned prompts
- Judge agreement analysis
- Human vs LLM agreement
Automatic metrics
Industry-standard and custom metrics triangulate quality from multiple angles.
- COMET, BLEU, chrF, TER, METEOR
- Additional structural metrics
- Metric correlation analysis
- Signal complementarity
Structural fidelity
We audit tags, placeholders, ICU variables, Rails-style placeholders and other protected tokens.Word-alignment-based analysis verifies that tags appear around the correct translated words.
<strong>
%{count}
{0}
||||
Tag and placeholder preservation
ICU and Rails-style token checks
Tag positioning accuracy
Alignment-based verification
Statistical validation
We quantify uncertainty and make differences meaningful.Every recommendation is backed by evidence.
Confidence intervals
95% CI for scores and metrics
Paired testing
Significance tests across providers
Annotator agreement
Inter-annotator agreement measures
LLM judge agreement
Consistency across LLM judges
Human vs LLM
Correlation and agreement analysis
Sample size
Justification for dataset size and power
Latency and infrastructure readiness
We stress-test systems across concurrency tiers to measure throughput, p99 latency, error rate and successful characters per second.
Throughput tiers (concurrency)
100
500
1K
2K
4K
p99 latency
842 ms
at 2K concurrency
Error rate
0.28%
at 2K concurrency
Successful chars / sec
12.4K
at 2K concurrency
What you receive
A complete, reproducible evaluation package to support your MT decision.
Benchmark methodology
Transparent design and scope
Canonical evaluation dataset
Production-shaped and versioned
Quality comparison
Human, LLM and metric results
LLM judge report
Scores, agreement and analysis
Automatic metric analysis
Metric scores and correlation
Markup report
Tag and placeholder analysis
Latency report
Throughput, p99 latency and errors
Statistical validation
Tests, CIs and agreement measures
Failure examples
Segment-level error examples
Reproducible scripts
Evaluation and analysis scripts
Make your MT decision defensible
A reliable MT benchmark is not one score.It is a controlled chain of evidence.