Newsroom
Press & media resources.
Writence builds writing tools for people who do not write in their first language. This page provides a concise overview, the essential facts, and access to brand assets. For additional inquiries, contact us directly; every message is read, not routed through a queue.
Company boilerplate
Writence is the company behind Diglot and Copyeditor — writing tools for people who think in more than one language. Grounded in research on ESL writing and AI-detection bias, the products keep the writer in control and produce a verifiable record of the writing process.
FACT SHEET
The essentials
Quotable facts about the company, ready to drop into a piece.
Company
Writence
The company behind Diglot and Copyeditor.
Founded
2026
Remote-first, independently built.
Products
Diglot · Copyeditor
Two products, one shared engine.
Focus
ESL writing
Fair, clear writing with a verifiable process record.
Grounding
Research-led
Built on studies of AI-detection bias.
Surfaces
5+
Web, extension, Google Docs, desktop, mobile.
STORY ANGLES
What we can speak to
For coverage of AI and writing, these are the topics we know best.
AI-detection bias
Detectors falsely flag non-native English writing at markedly higher rates than vendors report, a pattern confirmed by successive studies.
A tamper-evident writing record
A cryptographic, tamper-evident record of how a piece was written — documenting process, not claiming legal innocence.
Tools for a multilingual world
Most people write English as a second language; Writence is designed for that majority rather than treating it as an edge case.
The Authorship Certificate documents the writing process. It does not establish legal innocence, and coverage should reflect that distinction.
Press contact
Reach the press desk directly
For interviews, fact-checks, review access, or any question not addressed here, contact the press desk directly.
Covering AI
and writing?
The research that informs the company, and the products it has produced, are available for review.
Evaluating Perplexity Bias in Academic Detection
Quantitative assessments demonstrate that standardized models consistently mistake concise, rule-based second-language phrasing for synthetic generation.
The protocol generates a cryptographic signature confirming the record’s integrity, without exposing unpublished findings.