Cruce Saunders
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Crawft

May 2026–nowAustin

Tip: I'm building Crawft for Alpha School, where I'm a student.

Cruce SaundersLevel 7
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Crawft's overview for a fictional demo organization: one flagged item, six people with their computer time and scores, a donut of where everyone's time went, and a team productivity score of 71.

Crawft

Founder & CEO

May 2026–nowAustin

Back to the worldCrawft
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  • Back to the world
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Crawft

Crawft is Mac software for private schools that shows teachers what their students have actually been working on.

I founded it, and I'm building it right now for Alpha School, the private school in Austin where I'm a student.

Its first beta ran on Macs at Alpha, and I turned down an offer that valued Crawft at $400,000.

commits since May 2026
4,5904,590
automated tests
4,700+4,700+
product decisions I've logged
560+560+

Teachers can't see the work behind the screen

Picture a private-school classroom where every student works on a laptop. The teacher sees the finished assignment but not the hour behind it: whether a student was stuck on one problem the whole time, drifted off to YouTube, or did the work in a few focused minutes. Walking the room catches a few screens at a time, so problems tend to surface late, when a grade finally shows them.

Crawft closes that gap. A small app on each school-owned Mac records the school day, and each day a teacher can read what every student actually worked on, in plain English, with the screenshots behind it.

How a school day becomes a report

Crawft works in four stages. On the Mac, a small agent notes which app and window are in front, reads the text on screen, counts keyboard and mouse activity and takes screenshots. It never records what's typed into password fields. It processes and uploads that in small batches every few seconds.

On the server, the raw facts are stitched into a timeline for each student, with the noise removed and the time added up. Then an AI model reads the timeline, hour by hour, and writes the day as activities, projects and outcomes, with every line pointing back to the moments it came from. The teacher reads the result in one dashboard.

From a student's Mac to a teacher's report

On each MacServerAI modelTeacherEvery line traces backNothing in a report stands without the moments it came from.CaptureProcessed on the Mac and sent in small batches. Neverwhat's typed into password fields.The app and window in frontText read off the screenKeyboard and mouse activityScreenshotsTimeline of factsEach student's day, minute byminute, noise removed and timeadded upAI daily reportReads the timeline hour by hourand writes the day as activities,projects and outcomesTeacher dashboardThe class, each student's day andan assistant that answersquestionseveryfewsecondseveryhoureverydayEach line points to the moments it came fromClick any hour to see itsscreenshotsOn each MacServerAI modelTeacherEvery line traces backNothing in a report stands without the moments itcame from.CaptureProcessed on the Mac and sent in smallbatches. Never what's typed into passwordfields.The app and window in frontText read off the screenKeyboard and mouse activityScreenshotsTimeline of factsEach student's day, minute by minute, noiseremoved and time added upAI daily reportReads the timeline hour by hour and writes theday as activities, projects and outcomesTeacher dashboardThe class, each student's day and an assistantthat answers questionsevery few secondsevery hourevery dayEach line points to the moments it came fromClick any hour to see its screenshots
Every line of a report links back to the screenshots it came from.Request pathEvery line traces back
  • Capture: Processed on the Mac and sent in small batches. Never what's typed into password fields.. The app and window in front; Text read off the screen; Keyboard and mouse activity; Screenshots
  • Timeline of facts: Each student's day, minute by minute, noise removed and time added up
  • AI daily report: Reads the timeline hour by hour and writes the day as activities, projects and outcomes
  • Teacher dashboard: The class, each student's day and an assistant that answers questions
  • Every line traces back: Nothing in a report stands without the moments it came from.
  • Every line traces back at AI daily report: Each line points to the moments it came from
  • Every line traces back at Teacher dashboard: Click any hour to see its screenshots
  • Capture to Timeline of facts: every few seconds
  • Timeline of facts to AI daily report: every hour
  • AI daily report to Teacher dashboard: every day

What a teacher sees

This is the dashboard a teacher reads, shown with a fictional demo organization. At a school, the people are students and the groups are classes. Each school also chooses whether to turn on three extras: alerts that flag off-task stretches by naming what was on screen, a live view of a student's screen, and an AI productivity score for the day.

The whole dashboard in 40 seconds, with demo data.
  1. Crawft's overview for a demo organization: one flagged item, six people with their computer time and scores, average computer time of 6 hours 4 minutes, and a donut showing 89% of the group's time went to work.
    1

    Step 1: Start with the whole class

    The overview shows who was in, each student's computer time, where the group's hours went across work, non-work and idle, the activities that took the most time, and anything flagged that day.

    Shown with demo data.

  2. One demo user's day in Crawft: when it started and ended, 7 hours 40 minutes on the computer, 100% on task, a longest focus of 4 hours 10 minutes, an hour-by-hour timeline colored by activity, and a donut of where the time went, led by reviewing pull requests.
    2

    Step 2: Open any student's day

    One click opens a student's day: when it started and ended, their time on the computer, how much of it was on task and their longest stretch of focus. A timeline colors each hour by what they were working on, or regroups the day by activity, and clicking any hour pulls up its screenshots. Below it, a donut shows where the time went, activity by activity.

    Shown with demo data.

  3. Crawft's full daily report for a demo user: what she built and shipped, what she was stuck on, what is still open and her meetings, with the minutes spent and the tool used for each piece of work.
    3

    Step 3: Read the full report

    Full report opens the AI's write-up of the day: what the student finished, where they got stuck and what's still open, with the minutes and the app behind each piece of work.

    Shown with demo data.

  4. Crawft's Assistant answering “What did the design team get done this week?” with a list of what one demo user shipped and what is still open, plus Copy and Export to PDF buttons.
    4

    Step 4: Ask anything in plain English

    The Assistant answers questions about a class's work, like what one student finished this week, from the recorded work and screenshots behind it. It can answer with tables and charts, and one click exports an answer as a PDF report.

    Shown with demo data.

Crawft's overview for a demo organization: one flagged item, six people with their computer time and scores, average computer time of 6 hours 4 minutes, and a donut showing 89% of the group's time went to work.

Shown with demo data.

One demo user's day in Crawft: when it started and ended, 7 hours 40 minutes on the computer, 100% on task, a longest focus of 4 hours 10 minutes, an hour-by-hour timeline colored by activity, and a donut of where the time went, led by reviewing pull requests.

Shown with demo data.

Crawft's full daily report for a demo user: what she built and shipped, what she was stuck on, what is still open and her meetings, with the minutes spent and the tool used for each piece of work.

Shown with demo data.

Crawft's Assistant answering “What did the design team get done this week?” with a list of what one demo user shipped and what is still open, plus Copy and Export to PDF buttons.

Shown with demo data.

It started as a tracker for myself

Crawft began as a tool for me. For two years I'd been asking what actually changes how people use their screens: first in my research on how Austin teens live with social media, which I presented in London, then in LimitlessMinutes, an app where friends keep each other off their phones, then in TaskGo!, a Mac task app I built whose early Activity tab charted my keyboard and mouse use. By May 2026 I was tracking my own habits with a homemade setup on my Mac. On the evening of May 21 I called it “janky” and asked an AI for something “more sophisticated, more comprehensive, and more robust.” Within about an hour I had two specs. Two days later the first version was running, and I pointed it at the place I knew best: my own school.

I'm building it for my own school

Alpha School is the private school in Austin where I'm a student. AI tutors cover academics there in two to three hours each morning, so a large part of every school day happens on a computer, and teachers need to know what happened on it. I know that problem from the student's side of the screen.

That makes Alpha the right first school for Crawft. I'm building it for Alpha right now, and its first beta ran on Macs at Alpha and on my own.

How I build it

I own the product and every architecture decision, and I log each one in a numbered decision record that now runs past 560 entries. AI agents write the code from my specs and test it the way a user would. Together we've shipped a native Mac app, a production backend on AWS, the dashboards teachers read and a console for setting up schools and their computers.

When I wanted a Windows version, a lead agent split the work into nine milestones, worker agents built each one, and separate reviewer agents approved or rejected every piece. It shipped as a private beta in September. The system behind that work is on the Workshop page.

It's in beta, and businesses are asking too

Crawft is in beta today. Every organization gets the same dashboard, so it works for a company that owns its Macs as well as for a school, and businesses have noticed. I demoed Crawft to Bill Perkins, one of the two investors who made me a $25,000 offer on a pitch show in Hawaii. My roommate from Wharton's summer program introduced me to his family's company group in Brazil, which asked about testing it with its employees, and in September I presented it to their team over a video call.

I turned down $20,000 for 5%

John Alznauer, my first boss and the founder of the film-marketing startup where I ran sales, offered $20,000 for 5% of Crawft. That offer valued the company at $400,000. I passed, because it wasn't “representative of how much my company was actually worth.” I kept the equity and went back to building.

Next, more private schools

Alpha comes first. After it, I want Crawft in other private schools, where teachers face the same gap between the work they assign and the work they can see. Businesses can come later. For now, I'm building Crawft for teachers.

Visit crawft.ai

Led to

  • Level 8Wharton + NestIQJul 2026
  • Level 10The WorkshopOct 2025–now

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