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Legislative Drafting AI. Drafting cut from months to weeks; 15 bills drafted in the first 6 months across 8 jurisdictions (client-reported). Built in 5 months.
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For an AI scribe, I'd test what happens when someone switches encounters while a draft is still generating.
In my Arogya / Aurora EHR work, typed or dictated rounds become editable drafts. AI content stays advisory, and a clinician explicitly approves anything added to the chart.
The first response should stay attached to its original encounter. Before saving, I'd check that the encounter being reviewed still matches the draft, as well as requiring clinician approval.
That context check belongs in the save workflow, even when the draft reads well.
kapilchauhan.netlify.app
Arogya / Aurora EHR: AI for inpatient care | Kapil Chauhan
Production hospital and EHR platform that accelerates rounds, documentation, records, and care operations with clinician-reviewed AI.
Working on an AI agent that takes the hard work out of adding asset formats. It suggests hooks, you just add 4 to 40 images and see the magic .
Turning complex video creation processes into something as simple as possible.
Dropping in a pile of images and getting hooks suggested back would save so much time. Who's it mainly for, ad agencies or solo creators?