I

I built the missing layer between email and DocuSign

Hacker News

I built the missing layer between email and DocuSign

Hi HN, I'm Pratik Garg. My back story is I built OneRequest because I kept running into the same problem at work and more than often personal life as well. Whenever I needed something from someone professionally like a document, a signed form, proof of something it always turned into a thread of emails, missed attachments and manual chasing and similar thing happened when someone wanted information from me and over a period, things were lost, scattered and ultimately gone. I looked for a tool that handled this properly. Nothing quite fit. DocuSign is for signatures only and assumes both sides have accounts. Google Forms doesn't track who's responded or chase them. Email has no visibility at all and then came the Whatsapp scare(all sitting in a chat thread with no tracking, no audit trail, no way to find them 6 months later) So I built OneRequest. You create a request and add the fields you need, attach context for the recipient. You get one link. The recipient opens it, submits everything, no account needed on their side. You see who's opened it, who's submitted, who's stalling and One click to nudge someone. It works both ways. The same inbox shows requests you've sent and requests sent to you. If you're chasing a candidate for documents and your accountant is chasing you for tax docs, everything is in one unified Inbox, all in one place, all your requests. Technical bits that might interest anyone reading (and interested me): • Built on Next.js 14, Supabase, Prisma, Clerk • Two-way thread model with rounds owner can request additional information after initial submission so one link keeps hold of all the ongoing conversation. • Field types include file upload, signature, booking, payment, availability polling, document extraction via Claude API • I also built analytics in from day one because I genuinely want to understand how information moves between people. Which field types get abandoned. Where recipients drop off. How many nudges it takes before someone submits. I don't know yet what I'll do with all of it but I have a feeling the patterns will be interesting. Interesting bit: Launched yesterday and the website kept crashing. I found a setInterval delay of 25 * 24 * 60 * 60 * 1000ms in production. That's 2,160,000,000ms and exceeds int32 max. Browser clamped it to 1ms. Was firing hundreds of requests per second and taking the site down. Took me 5 hours to find and apparently just one line to fix Still early. Rough edges. Would love feedback on whether the concept is clear and where the experience breaks. You can create a request without signing up at onerequest.app

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, google · Missing: mac, agents, macos
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, google · Missing: mobile apps, ios, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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