On

Onri.ai – find the expert in your team

Hacker News

Onri.ai – find the expert in your team

In the last month, I built Onri AI to scratch my own itch: "How to find someone in my company who knows about XYZ?" I think many Hacker News readers may find it useful, thus sharing it here :) https://onri.ai By connecting your company's Git and Jira, you have a search engine that gives you "who knows about ...?". Zero maintenance required from your team. Now, let me explain why I built it. Many years ago, I worked as a software engineer in a FAANG company with thousands of engineers. I noticed that, whenever I need to work on something out of my immediate team's scope, it's nearly impossible to know the right person to reach out to. I'd email my manager: "hey, do you know who worked on this old feature XYZ?". If I'm lucky, that email would then be forwarded to other managers and directors and, days later, reached the exact engineer who worked on it. If I'm in bad luck, the email went nowhere and I was left alone digging up old code and documents. The scenario above occurred daily in various shapes: 1. When I start on a project and I need to set up a new server --> "Who can grant me access to a new server?" 2. I picked up an old feature --> "Who can give me a quick knowledge dump on the system design from 5 years ago?" 3. My server log showed an error stack trace from api.phoenix.messaging... --> "Who worked on this internal messaging API called Phoenix?" There are many potential solutions: 1. Documentation: this is usually my first step but only if the documentation exists and is up-to-date, which is rarely the case. 2. Git blame: useful if I know which code I should be reading, but it's very time consuming when all I need is a high-level system design walk-through. 3. Ask around: this is what I usually do, and it's a hit or miss depending on my manager's work connection. 4. Force everyone to keep a work log and search that: doable but it comes with very high maintenance cost. Given that many engineers work with Git and some ticketing system (Jira, Asana, etc), I think it's possible to establish the organization knowledge of "who worked on what" for the entire company simply by looking at all Git history and tickets. I ended up building it as an internal tool in that FAANG company, and it worked! The search results were amazing, and I no longer needed those long email threads to find some team I hadn't even heard of. Got hundreds of daily users. That's why I'm building Onri AI, as I think it would be useful for the public! I chose the name Onri because this tool breaks down tribal knowledge, and the Onri logo symbolizes the process to tear down walls within a company, visually starting out with 4 walls, then 3 walls, then 2, then 1. I hope you enjoy it, and I'm open to any feedback! If you got questions, please leave a comment or reach me at support@onri.ai

Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
92%92% 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
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, hacker news, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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.

Incorrect prediction on native model

Similar products

alp90
alp9026%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

expert

Indie Hackerscommitment-full-time
Jackets
Jackets25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Jackets Expert

Indie Hackers
Expert Tutor Team
Expert Tutor Team32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Online Tutoring Platform for Students & Expert Tutors

Indie Hackerscommitment-full-time
Ai
AirBnBs for Team Retreats58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AirBnBs for Team Retreats

Hacker News1
Po
Pokemon Go Team Tshirts62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pokemon Go Team Tshirts

Hacker News6
Fi
Find a team for Ludum Dare58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find a team for Ludum Dare

Hacker News3
Ah
AhaBot – Overcome obstacles as a team (SlackBot)58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AhaBot – Overcome obstacles as a team (SlackBot)

Hacker News1
A
A Lichess Team for HNers58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Lichess Team for HNers

Hacker News7
Te
Team Icebreakers58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Team Icebreakers

Hacker News13
Te
Team – Coroutines and async semantics for C++49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Team – Coroutines and async semantics for C++

Hacker News35