ASH™ Framework
Adaptive Stylometric Humanization
ASH™ (Adaptive Stylometric Humanization) is the core statistical transformation architecture behind TextHumanize. It applies a multi-layered pipeline of linguistically-grounded rewrites that shift a text's stylometric fingerprint from AI-like to human-like — operating entirely offline.
How It Works
Stylometric Fingerprinting
ASH™ measures 15 stylometric features of the input: entropy distribution, sentence-length CV (burstiness), Zipf deviation, punctuation density, connector repetition, clause opening diversity and more.
Profile-Aware Transformation
Based on the detected profile (web, academic, SEO, docs, chat) and requested intensity (0–100%), ASH™ selects the optimal combination of its transformation modules.
12-Stage Pipeline
Each stage targets a different feature: debureaucratization, connector diversification, sentence restructuring, paraphrase injection, entropy boosting, grammar naturalizer, cadence variation, and discourse surprise.
Language-Aware Rules
ASH™ uses language-specific synonym dictionaries, grammar rules and pattern libraries for 25 languages — from English morphological analysis to Ukrainian collocation tables.
Key Metrics
Use Cases
- Humanizing large volumes of AI-generated content (batch API)
- SEO content pipelines where naturalness affects ranking
- Multilingual content operations (25 supported languages)
- Integration into CMS, browser extensions, and editorial tools
Try ASH™ Framework Now
Powered by our proprietary technology stack. Available directly in the TextHumanize tools and via REST API.