Your DigitalVoice Twin

Twinscribe analyzes supplied writing (posts, emails, articles) to build a voice profile that captures tone, cadence, vocabulary, punctuation habits, and stylistic preferences. Additional samples improve the profile by reinforcing patterns in sentence structure, word choice, humor style, and typical topics. Edits made in the composer are used as corrective signals to refine the model over time.

LinkedIn and X (Twitter) are supported today. Instagram, Facebook, and Medium are on the roadmap, prioritized by early-access feedback, content-type fit, and integration complexity.

Yes. All content goes through manual review before scheduling or publishing. The composer provides real-time voice-match scoring to guide quick edits, and scheduling controls allow selecting platform, time, and cadence prior to posting.

Internal evaluations typically reach ~85–95% voice-match accuracy after analyzing 10–15 writing samples. Accuracy improves with more examples and with corrective edits. In blind evaluations, human raters were often unable to reliably distinguish generated text from the reference style.

Data is encrypted in transit and at rest. Writing samples and generated content are not shared with third parties or used to train models for other customers. Twinscribe aligns with GDPR/CCPA principles, provides data export and deletion controls, and uses OAuth for social integrations so passwords are not stored.

Internal time-on-task measurements indicate drafting, editing, and scheduling can be reduced to under five minutes per post (versus ~30–60 minutes). Aggregated across planning, writing, editing, and multi-platform scheduling, early-access feedback and experiments indicate typical savings of about 5–10 hours per week.