What WordMark measures—and what it does not.

The product deliberately separates deterministic evidence from statistical heuristics. Hidden Unicode, control characters, metadata and protected-data preservation can often be checked exactly. Statistical authorship or proprietary watermark claims usually cannot.

Deterministic layer

The scanner enumerates Unicode code points, flags context-sensitive invisible characters, bidi controls, unusual spaces and mixed-script confusables. Safe Clean removes only artifacts with a deterministic cleanup rule; emoji joiners are preserved when used inside pictographic sequences.

Statistical laboratory

Metrics include token and sentence distributions, vocabulary size, lexical diversity, Shannon entropy, sentence-length variation, burstiness, repeated n-grams, average word length and punctuation density. These are descriptive features, not a binary “AI detector.”

Meaning and data preservation

Deep Clean masks code, URLs, dates, numbers, currencies, citations, named entities and technical tokens before transformation. A result is checked again afterward. The meaning score combines protected-data preservation with normalized content-word overlap. When a requested transformation falls below its configured preservation threshold, the engine automatically reduces the transformation strength.

Provider verification

The verifier adapter reports Unavailable unless the operator configures a compatible authoritative endpoint. The interface never converts “statistical transformation completed” into “watermark removed” without external verification.

Benchmarking

Every release runs deterministic correctness and performance tests before it goes live. Those tests use controlled fixtures, so they prove the tools behave correctly, not that every real-world document in every language will convert perfectly.