Projects
HumanizerPro.ai
LLM Fine Tuning | Data Cleaning | Open AI Integration
Undetectable AI Essay Writer
HumanizerPro.ai is an AI-based rewriting and humanization engine designed to transform AI-generated text into natural, human-like writing patterns, optimized to bypass popular AI detectors. The system focuses on lexical diversity, structural variation, and pattern removal that commonly trigger detection flags while preserving semantic meaning. It is built to assist agencies, writers, and automation systems that rely on AI content but require increased authenticity.
Challenge
Role
Lead AI Engineer responsible for dataset collection, filtration, cleaning, prompt logic development, fine-tuning methodology, and building the rewriting pipeline that performs semantic-preserving transformation while altering linguistic structure.
Project Goals
- Rewrite AI Text into Human Style – Remove machine-like phrasing patterns and statistical footprints.
- Increase Bypass Success Rate – Specifically optimize rewritten content against detector behaviors.
- Maintain Meaning While Changing Expression – Focus on safe transformation preserving context integrity.
- Build a Scalable Rewrite Engine – Capable of processing multiple content types efficiently.
Development Process
- Dataset Collection Strategy – Curated samples from diverse sources to capture real writing variability.
- Cleaning & Deduplication Pipeline – Used Pandas for structural cleaning and noise reduction.
- Fine-Tuning & Prompt Constraint Engineering – Trained the model to rewrite with controlled change intensity.
- Detection Resistance Testing – Evaluated output against common AI detectors and refined prompts accordingly.
- Batch Inference Optimization – Designed logic for fast processing of multiple content pieces.
- Internal Deployment & Integration Pathways – Developed the pipeline to be consumable by external tools and workflows.
Final Outcome
The system uses a Humanization Model Pipeline that transforms AI‑generated text into a natural human tone without altering meaning. A dedicated Dataset Training and Cleaning Workflow, built from human‑labeled samples, enhances expressive quality and consistency. A fine‑tuned LLM rewriting model ensures semantic accuracy while improving readability. An internal AI rewriting utility powers external services behind the scenes, enabling automated, high‑quality rewriting across multiple applications.
