Lettershred · Many models, one voice · Compliant by construction

Keep your generation process,
shred the watermark.

Shredders intelligently diversify long-form text through dozens of models to remove high-confidence watermarks. Proven to work.

Multi-model kernels · Reads like a human · Compliant by construction

lettershred / composeblending
drafting “Keep your process, remove the watermark” · a dozen judges voting, line by line · one voice out
complianton-brand · 1,240 words · published to your blog
Proof

The watermark falls below detection

We score Lettershred output against a KGW green-list detector, the same watermark family the labs ship. Watermarks register when z hits 4. Shredders keep it under.

kernels · /watermarkKGW green-list · threshold z = 4
evaded

z fell from 15.46 to −0.32, below the detection threshold of 4. The text now scores like it was never watermarked.

177 of 240 tokens rewritten
Watermarked (before)15.46
After shred-0.32
Never watermarked (floor)-0.58

The hairline marks the detection threshold (z = 4). Only the watermarked run clears it.

Before — as generated239/239 green (100%)z = 15.46
After — blue ring marks a rewritten token117/239 green (49%)z = −0.32
green-list (the mark)red-listrewritten

The sweep — mean z over 24 seeds at 150 tokens. Read the last two columns as the control: they rewrite just as much text, but blind to the key or in blocks. Evasion comes from diversifying across models and dispersing the rewrites, not from changing more words.

CoverageCross-provider, dispersedCross-provider, blockedRe-imprint, dispersed
0%12.2112.2112.21
25%6.369.069.21
50%2.145.416.18
75%-0.322.874.09

Beat the watermark
without changing your process.

One brief in, one polished, multi-model article out.