Collect
Methodology
How We Process Review Data
eSIMBite converts raw public reviews into structured provider and destination insights using local LLM extraction, strict validation, evidence linking, and confidence-based suppression. This methodology is designed for transparency, comparability, and auditability.
At a glance
- - We normalize and deduplicate source reviews before scoring.
- - We require evidence-linked category and country signals.
- - We suppress low-confidence or ambiguous outputs by design.
Methodology, in plain words
We read public reviews like a careful travel editor. First we clean and organize the raw text, then we extract structured signals, then we only publish what is supported by real evidence.
Route view: how one review becomes insight
Normalize
Extract
Validate
Aggregate
Suppress
What this means
Scores come from a repeatable system, not from hand-picked opinions.
Why it matters
You can compare providers using consistent rules across destinations.
How we prevent mistakes
Weak or vague signals are hidden by default instead of guessed.