Transparent Methodology
How Sarenica Works
No black-box AI. Every metric has a documented methodology. See exactly how your data is analyzed.
Core Principles
Deterministic
Reproducible results using documented algorithms, not LLM guessing
Pattern-Based
Detects patterns in YOUR behavior, not generic benchmarks
Quality-Adjusted
Sensor confidence penalties for accurate readings
Individual Analysis
Compare you to yourself over time, not population averages
Detailed Methodologies
Our Transparency Promise
We believe users deserve to know exactly how their data is analyzed. This page documents our core methodologies, but every Hydrogen agent command also includes a /methodology flag to explain specific calculations in detail.
- All algorithms are deterministic and reproducible
- Quality adjustments are transparent and documented
- Statistical methods include confidence intervals and significance tests
- Confounding factors are identified and reported
- Updates to methodologies are versioned and announced
Questions about our methodology? Email us at methodology@sarenica.com
Methodology FAQ
Does Sarenica use black-box AI to calculate fatigue metrics?
No. Core fatigue and pattern metrics are calculated using deterministic, documented methods. The AI layer helps with interpretation, question understanding, and response presentation, not raw metric calculation.
Why are some advanced insights locked until I collect more data?
Sarenica uses a data maturity system so advanced insights are only shown when there is enough data to reduce misleading conclusions. This improves reliability for comparisons and correlations.
How does Sarenica handle low-quality or incomplete signals?
Sarenica applies confidence scoring and quality penalties to sensor-derived signals. Low-confidence readings are flagged and can be excluded from reliable-minute calculations and advanced analysis.
Can I inspect the logic behind a specific Hydrogen agent answer?
Yes. Sarenica provides methodology explanations, coverage details, confidence cues, and technical details on demand so you can understand how a result was produced.