Our technology
The technology behind Phrasly.
Proprietary models and methods, refined over years. Built for natural writing that keeps your meaning.
- Our models
- Our methods
- Our infrastructure
Built with purpose
Every part of a better rewrite.
We develop our training data, models, and evaluation methods in-house. Together, they shape how Phrasly rewrites.
Methods unique to Phrasly
We develop proprietary training data and evaluation methods in-house. This combination is unique to Phrasly and shapes how our models preserve meaning, improve flow, and refine each rewrite.
How we train
We start with an existing foundation model and adapt it for humanization. Our team develops the training datasets, fine-tunes the model, and decides how its writing is evaluated.
Reinforcement learning uses detector feedback alongside assessments of meaning, grammar, and writing quality. These signals guide the changes we make to the model.
Checked for quality
We check how a rewrite reads, what it means, and how detectors classify it. Grammar and writing quality are part of the evaluation throughout model development.
What we measure
A detector score is one part of a rewrite. Our evaluation agents also assess whether the text keeps its meaning, uses correct grammar, and reads naturally.
We run these checks during training and after benchmarks. Our team uses the results together to refine the model, rather than treating a detector score as the only measure of quality.
Run by Phrasly
Every rewrite runs on models we deploy and manage. We optimize the models and supporting infrastructure together for responsive processing and efficient use of computing resources.
How we run it
Every production humanization runs on Phrasly’s deployed models. We do not send your text to OpenAI, Anthropic, Google, or another provider’s LLM API for humanization. That includes fallback routes.
We manage model configuration, inference, and deployment. Tuning the models and infrastructure together helps us balance response times, resource use, and the cost of each rewrite.
Our approach to sustainabilityWatermark-free by default
Your rewrites are free of AI watermarks. We control the models that generate them, so another AI provider cannot change our generation process through an API update.
About watermarks
We manage the models that generate your rewrites, including fallback routes. Another LLM provider cannot add a watermark to our generation process through an API update.
Text watermarking can add a statistical signal when text is generated. Control over our models lets us manage generation directly and refine the technology as detection methods change.
How the models improve
How our models improve.
We combine our own training data, fine-tuning, and reinforcement learning to improve detector performance, natural flow, and how closely rewrites preserve meaning. Every update builds on what we learn.
Detector feedback
AI checker results inform model updates as one of the signals in our training process.
Meaning and quality
We also evaluate grammar, meaning, and natural flow to guide how the model rewrites.
Inside the training process
Our team connects fine-tuning, reinforcement learning, and quality evaluation in one development process. Evaluation agents assess grammar, meaning preservation, and overall writing quality during training and after benchmarks. We use those assessments alongside detector results when refining the model.
The results
See how Phrasly compares.
Compare detector results and writing quality in our benchmarks.
Questions
Your questions, answered.
What makes Phrasly different?
We develop the training data, model adaptations, and evaluation methods behind Phrasly in-house. These proprietary methods shape how our models rewrite text, preserve meaning, and improve from evaluation results.
Did you train the model from scratch?
No. We start with an existing foundation model and adapt it for humanization. Our team develops the datasets, fine-tunes the model, and runs the training and evaluation process.
Does Phrasly use ChatGPT or Claude to humanize text?
No. Humanization runs on models deployed and managed by Phrasly, including fallback routes. We do not send your text to OpenAI, Anthropic, Google, or another provider’s language model API for humanization.
How do you check that a rewrite keeps its meaning?
Our evaluation process compares rewrites with the source text to assess whether the original ideas carry through. Meaning is checked alongside grammar and writing quality during training and after benchmarks.
What is reinforcement learning?
It is a way of improving a model using feedback on its outputs. We use detector results alongside assessments of meaning, grammar, and writing quality to guide model updates.
Are Phrasly rewrites free of AI watermarks?
Yes. We control the models that generate your rewrites and do not add AI watermarks. That applies to our humanization models and fallback routes.
What makes Phrasly fast?
We optimize our writing models and the systems that run them together. Model configuration, computing resources, and deployment are managed as one system to balance response times, resource use, and operating costs.
How does Phrasly improve, and where can I see the results?
Our team uses detector results and writing quality evaluations to refine and test model updates. The benchmarks page explains our comparisons, and the changelog records model and product improvements.
Can I use Phrasly in my own product?
Yes. Phrasly’s API provides humanization and AI detection for other products and workflows. The API page explains the available tools and how to start integrating them into your product.