AI still can't judge good writing on its own. Our managed expert editors, many with PhDs, define scoring rubrics, measure model output, and build gold SFT and preference data for AI labs and LLM product teams—so your models learn what good writing is.
Services
Rubrics that make writing quality measurable. Expert evaluations that show where your model stands. Training data that moves it forward.
Define
A quality bar you can apply, audit, and defend.
Measure
Expert scoring that shows exactly why output falls short.
Build
Expert-written, rights-cleared, ready to train on.
Use cases
Whether you're a frontier lab or building on top of one, the service mix changes with how you build; the quality bar doesn't. We're deepest in the hardest writing: scientific, medical, and academic texts.
Whether RAG, agents, or straight generation, your product is the quality of the output. Expert evaluation shows that quality holds, even as the models underneath you change. Typical first engagement: recurring SxS evals that track quality across model updates.
Start withMeasureDefine
Frontier APIs for some tasks, your own models for others. Evaluate the whole system; train the parts you own. Typical first engagement: a system-wide evaluation, then data for what you train.
Start withMeasureBuild
You tune a foundation model to your domain, so your value is the adaptation layer. In-domain preference and edit-pair data is what improves it. Typical first engagement: a domain-calibrated rubric and the gold data to tune on.
Start withBuildMeasure
You own every stage of training, so your ceiling is your data. Expert-authored gold sets raise it where it counts: post-training. Typical first engagement: SFT gold sets and preference pairs for your next training run.
Start withBuildMeasure
Expertise
Since 1997, we've edited billions of words of academic, scientific, and professional writing. We can explain why good writing works, in criteria models can learn from.
Less than 1% of applicants make our roster, and many of our editors hold PhDs. Every expert is trained, calibrated to our standards, and QA-scored on every piece of live work. One managed, accountable team: no crowdworking marketplace underneath.
Our team publishes the science of judging writing, including metrics such as the Scribendi Score (EMNLP 2021, reimplemented by other labs) and PEET (2025), which scores correction tools by the human effort needed to fix their output. Your rubrics come from people who ship language-quality models for a living.
English is the only language we work in, and we go deepest where it's hardest: non-native writing for scientific, academic, and professional contexts. Scribendi has refined ESL prose from clients worldwide since 1997; Edanz has spent two decades helping Japan's researchers publish in English.
335,000+ clients in 150+ countries since 1997, including 1600+ journals, universities, and academic societies. We run on the corpus, the editors, and the QA machinery behind those numbers.
Team
Our CTO and Lead AI Researcher work directly on every engagement. No handoff to a delivery team.
Chief Technology Officer
Publishes AI research and ships our production LLMs, GEC models, and evaluation methodology. Fifteen years at Scribendi. Built EditorWorks, the platform every engagement runs on, and led the technology organization through two acquisitions.
Connect on LinkedIn
Chief Technology Officer
Publishes AI research and ships our production LLMs, GEC models, and evaluation methodology. Fifteen years at Scribendi. Built EditorWorks, the platform every engagement runs on, and led the technology organization through two acquisitions.
Connect on LinkedInDesign partner program
We're taking on a small group of design partners in 2026 and shaping our services around their needs.
Founding rates are subsidized through 2026. In return, we ask for a case study, anonymized if needed.
Let's talk
Tell us what you're building. We'll come back with a pilot scoped to your stack: deliverable, price, and timeline.
If your product depends on expert judgment at scale, we can supply it under your spec, your brand, and your QA bar.
White label stays white label. NDAs first.
Scribendi Expert Data