I was paying for five tools to do two jobs, so I built one app that does both
Understand what you read. Say what you mean.
I read and write in English all day, and it is not my first language.
That creates two kinds of friction, and they pull in opposite directions. Incoming text takes longer to understand, especially when it is dense, unfamiliar, or full of jargon. Outgoing messages take longer because I check the grammar, the phrasing, and the tone three times before I feel safe hitting send. And sometimes I know exactly what I want to say, but speaking the thought is far easier than typing it clearly.
So I did what most people in that situation do: I opened more tabs. A grammar checker for phrasing. A translation tab for anything foreign. A chatbot for the longer stuff. An OCR tool for text stuck inside a screenshot. A speech-to-text app for when typing was slower. A clipboard history to recover what I copied five minutes ago.
Each one solved a real part of the problem. Together they were still constant switching, prompting, copying, and pasting, plus several subscriptions, several shortcuts, and half of them locked to one browser, one ecosystem, or one platform.
At some point the pattern got obvious. All that switching was in service of just two jobs: understanding what came in, and saying clearly what went out.
So I built ClipWise, an AI reading and writing assistant for Windows and macOS that works across almost any app, with two ways to begin.
Start with text that already exists. Select or copy it, press a shortcut, and rewrite awkward phrasing, translate across 21 languages, summarize something long, clarify dense writing, structure rough notes, extract action items, or draft a reply from copied context. The result comes back where you were already working.
Or start with a spoken thought. Press the voice shortcut, speak, and ClipWise turns the recording into written text you can copy, rewrite, or translate right there. Not real-time dictation into the cursor, but a short note that becomes a finished message.
When text is trapped in a screenshot, a slide, or an unselectable PDF, it can pull the text out so you can carry on from there.
The honest part, which I think matters more than a bigger claim: ClipWise does not try to be the deepest specialist in any single category. Grammarly's live inline underlines are a real advantage I do not match. DeepL translates whole documents with glossaries; I translate selected text. Dedicated dictation tools stream straight into the active field. ClipWise is for the person who wants to retire the whole side-by-side stack and get the everyday version of those jobs done in one place, on both platforms, where the work is already happening.
What made the business case click for me was the price anchor. Framed as a clipboard tool, I would be competing with free. Framed as a reading, writing and voice tool, the comparison is a grammar subscription plus a dictation subscription, together about $24 a month, before any OCR utility. ClipWise starts at $4.99, and the free plan has no time limit: currently 10 AI actions a day, no API key required. You can also bring your own OpenAI or Anthropic key, or run a compatible local Ollama model if you would rather nothing left your machine.
The goal for 2026 is 10,000 copies.
The thing I keep going back and forth on is how much honesty to put in the marketing. My comparison page names what competitors do better than me, in the same breath as the pitch. It feels right, and early readers say it builds trust, but I have no idea yet whether it converts. Has anyone here tested a genuinely self-critical comparison page against a conventional one?
“AI for clipboard is a great productivity angle.
Would be interesting to see which use cases users actually stick with long-term vs just try once — that retention insight can be gold.”
Honestly, I don't have long-term retention data worth quoting yet. The app is young, and a small sample dressed up as a trend would mislead both of us.
My expectation, which is exactly what I want to test: the two halves of the product should behave very differently. Rewrite and translate have an external trigger (someone sends you something, or you owe someone a reply), so those should be daily. OCR and summarize are situational; you use them in bursts when the work calls for it, then not for a week. If that holds, retention lives in the everyday actions, and the situational ones are mostly what get people to install in the first place.
So the measurement I'm setting up is: for users past day 30, which action was their first, and which one are they still running in week four. If those are usually different actions, then the thing that sells the app and the thing that keeps it are not the same feature, which changes both the marketing and the onboarding.
Thanks for the nudge, it's the right question to be asking this early.
The line about “retiring the whole side-by-side stack” was what sold the idea for me. It explains the product much better than listing all 13 actions.
I’m curious have you found users buy into the convenience first, or do they usually come in looking for one specific feature and discover the rest later?
That's useful to hear, thank you. I've gone back and forth on whether to lead with the story or the spec sheet, and "thirteen actions" clearly loses.
On your question: I don't have clean data to answer it properly yet, so treat this as an impression rather than a finding. People seem to arrive for one specific thing, usually whatever they were already searching for: text stuck in a screenshot, or fixing a message before they send it. The breadth only registers later, once they hit a second job and find it is already there. Nobody goes looking for "an app that does five things"; that is something you discover, not something you type into a search box.
If that is right, it has an awkward implication. The consolidation story is what makes someone stay and tell a friend, but a single specific job is what makes them click in the first place. So the honest move is probably to lead each channel with one concrete job and let the rest be a pleasant surprise, rather than opening with the full list, which is exactly the mistake I made in the first version of this post.
Did you come at it from one of those jobs yourself, or from the stack problem?