I built a tool that reduces burnouts and increases efficiency

Hacker News

Hi HN, I have been working on a side project that turned into a real B2B Saas: Assignix My SaaS helps teams increase their workforce efficiency and prevent burnout by assigning the best employees to specific tasks using AI. The motivation came from my own experience of watching my father, a product manager, spend too much time analyzing people's profiles, trying to figure out if someone was ready to handle the task at hand. Initially, I built this tool for my dad to use, and since he liked it so much, he persuaded me into making it into a small SaaS, and here I am now. So what does my tool do exactly? Well, it: • Analyzes resumes of employees and creates profiles by extracting relevant information from the resumes and storing it for use. • It then uses that information, and the user can choose which employees are available, and it will give high-quality recommendations for which employees are best suited for a task. • Additionally, I added a Kanban board to better visualize the current work and progress. • The tool also uses additional information about the company to give a more tailored task assignment. Technical bits: • Built with Next.js, MongoDB, and Auth.js (JWT-based sessions). • Hosted on Vercel. • Uses OpenAI GPT-5 for AI-powered task assignment. • Other tools: Cloudinary, Resend, LemonSqueezy. Why I'm posting Well, it is still an early stage for my SaaS, and I am still validating the groups to reach out to. I'd also love feedback on: 1. What really slows down teams? 2. Do teams usually use multiple tools to manage tasks? 3. Are small teams willing to pay for this kind of service? I’d love feedback, brutal honesty, or just a conversation with anyone who has experience with workflow, productivity, or team management tools. Also happy to connect with other makers and early-stage founders. Check it out here: https://www.assignix.app/ Thank you so much for your time! -Mahir

Visit product View on Hacker NewsAug 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, openai · Missing: mac, agents, macos

87%87% 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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: visualize · Missing: mobile apps, ios, personal

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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