About Leap AI

Our mission

AI writing tools are everywhere now, and so are the detectors that try to flag them. Both sides of that arms race make mistakes, and writers are the ones who pay the cost — a student accused of cheating over a phrase they wrote themselves, a blogger deranked for content a detector misclassified, a researcher flagged by a journal policy nobody reads in full.

We started Leap because we think the detect-then-humanize loop deserves to be run by a tool that is fast, honest about its limits, and built by people who read the detection research. Our mission is simple: give writers working alongside AI the clearest, most accurate feedback we can, and let them decide what to do with it.

We don't train on user-submitted text. We don't sell detections to universities or publishers. We publish our methodology openly and update it when new research lands. What you read on the site is what we actually ship.

Our approach to detection and humanization

Detection and humanization are two sides of the same coin. A good detector models the statistical fingerprints of machine-generated text — perplexity, burstiness, stylometric regularity. A good humanizer rewrites text to close those gaps without destroying meaning.

Our detector outputs per-sentence scores, not a single document-level percentage, because the granular signal is where the interesting decisions happen. A paragraph with three AI-feeling sentences and five human-feeling sentences tells you something a 62% overall score can't.

Our humanizer treats output as drafts, not finished work. The goal is to reduce detectability while preserving voice, facts, and structure — not to produce text that sounds like a different author. Writers who use the humanizer should still reread, edit, and own the final words.

For the technical detail — the metrics we track, the benchmarks we run, the detectors we test against — see our methodology page.

Leap AI Research — the editorial team

"Leap AI Research" is the byline on most of the content on this site. It refers to our in-house research team, not a single author. The team reads the detection literature, runs benchmarks on every major detector and humanizer, and writes up what we find.

To make that concrete, here are the roles that make up the team. These are descriptions of the work, not real individuals — we keep personal identities off the site because we'd rather be judged on the quality of the output than on credentials.

Research Lead

Reads published research on AI detection, watermarking, stylometry, and related fields. Decides which signals we measure, how we score them, and when a claim on the site needs a citation. Owns the methodology page and the prompt changelog.

Benchmark Engineer

Runs the evaluation harness. Builds the test corpora, calibrates the thresholds, and retests when we touch a detection or humanization prompt. Every number you see on a comparison or review page came out of a run they signed off on.

Content Director

Edits everything before it ships. Cross-checks that the explainer pages match the behavior of the tool, that comparison pages don't overstate Leap's advantages, and that we update dated content when reality changes. Owns our editorial standards.

We coordinate with outside academic and industry contributors when a topic needs expertise we don't have in-house. When that happens, we name the contributor explicitly at the top of the article.

How we write our content

Writers trust the site only if the content is right. We take editorial standards seriously. Here's how we work.

Primary sources first

When we make a claim about how a detector or humanizer works, it's because we tested it against our corpus or read the vendor's own documentation. We don't rewrite other sites' marketing copy.

Citations

Technical claims link out to published research, vendor documentation, or our own methodology page. If a claim doesn't have a source we can point to, we say so explicitly.

Updates

AI detection is a fast-moving field. We review every content page on the site at least once per quarter and mark the last update date clearly at the top. Major rewrites get a note at the top of the page explaining what changed. See our changelog for product-level updates.

Honesty about uncertainty

No AI detector is 100% accurate. No humanizer bypasses 100% of detectors 100% of the time. We say so, we quantify it where we can, and we don't pretend otherwise to close a sale. If the honest answer is "it depends," that's the answer we give.

Contact us

Questions, corrections, partnership ideas, or press inquiries: email hello@tryleap.ai. We read every message. Corrections and factual errors get priority — if a claim on the site is wrong, we want to know.

For account or billing questions, email support@mail.tryleap.ai. Our FAQ covers the most common questions about pricing, privacy, and use cases and will probably answer your question faster than we will.

For legal matters — DMCA, data requests, privacy inquiries — see our privacy policy and terms.

What's next

The tooling will keep getting better. Detectors will get harder to fool and humanizers will keep up. What stays constant is our stance: build tools that respect writers, measure honestly, and update when we learn something new. If you have ideas for what we should build next, the inbox is open.