Dedicated models. Our own engine.
Bypassify pairs carefully-selected large-language models with an 11-stage humanizer pipeline we built ourselves. Every stage is deterministic, open about what it changes, and runs the same way every time.
A model rewrite, then ten deterministic passes.
The model handles the heavy lifting; the surrounding stages fix the rhythm, vocabulary, and structure detectors actually score on.
Strip zero-width characters, fix curly quotes, collapse extra spaces, normalize line breaks. A clean canvas for every later stage.
Quotes, parenthetical citations, bracket references, code spans, and URLs are tokenized before rewriting and restored byte-for-byte at the end.
The masked draft is sent to a dedicated humanizer model (Mistral Large, GPT-OSS-20B, or our Gemini fallback) under a locked system prompt that forbids meta commentary and preserves meaning.
~400 high-signal AI phrases swapped for natural alternatives — "in conclusion", "a plethora of", "play a pivotal role", "it is important to note that".
Wordy noun forms become tight verbs: "make a decision" → "decide", "provides an explanation of" → "explains", "has an impact on" → "shapes".
Conservative word swaps from a ~500-entry table tuned for GPT vocabulary: utilize → use, leverage → use, robust → solid, meticulous → careful.
Neutral and Casual modes apply natural contractions (do not → don't). Academic mode keeps the formal forms.
Em-dashes — a huge GPT tell — are replaced with commas or periods. Oxford commas vary. Semicolons become periods more often.
Per-sentence reorders ("X because Y" ↔ "Because Y, X"), splits on conjunctions, merges of adjacent short sentences, and leading-adverb shuffling.
Sentence-length standard deviation is measured. If too uniform, one long sentence is split and two short ones are merged until the rhythm reads natural.
If any paragraph is identical to the input, a forced structural change fires. Spacing, capitalization, and punctuation are finalized. Masks restored.
How we chose the models and the guardrails.
Requests route to Mistral Large, GPT-OSS-20B, or our Gemini fallback based on load and text length. No consumer ChatGPT wrapper, no shared prompt with other tools.
Quotes, references, and URLs are masked before the request leaves our server and restored after — the humanizer literally cannot see them, let alone edit them.
We route through providers that contractually don't train on API traffic. Your drafts aren't kept, aren't logged past the response, and aren't recycled into anyone's dataset.
If a paragraph would come back unchanged, the safety-net stage forces one structural transformation so output is never the same as input.
Every stage after the model rewrite runs the same way on the same input. Same draft, same settings, same output — no surprise regenerations.
Stages target the exact features detectors score: burstiness, perplexity, em-dashes, connector density. We chase good writing, not statistical camouflage.
Try the engine yourself.
Paste a draft, choose a strength, see what every stage changes.