Pi

Pika – Custom ChatGPT-like chatbots for any website

Hacker News

Pika – Custom ChatGPT-like chatbots for any website

Hello everyone, My name is Alex and I am the founder a VC-backed startup. Recently, I’ve used the Open AI API to create an internal chatbot that the team could use. Basically, I wanted to allow the team to ask questions about any company-related information and get answers immediately. That chatbot was trained mainly on our Notion docs, as well as memos and notes taken by the team. Later on, that chatbot was also used by my company’s website, to help our acquisition efforts. The results were great and given that we didn’t have the capability to get back to every customer inquiry from our previous chat widget, the chatbot I created was a huge success. I ended up creating an entirely new product called Pika so that it can be used on any website. This is how it works. People login the platform, they add sources of information to the chatbot’s knowledge base (either documents, URLs or free-form text), and click on a button to create a chatbot. Then the chatbot is trained on the sources of information provided; this usually takes less than 30 seconds. The chatbot now knows everything about the sources of information provided. It can answer any question and every answer can be analyzed and later edited so the chatbot can learn from its mistakes. You can see a demo of Pika at https://trypika.com Please try it out and let me know if you have any feedback. I am also happy to take any other technical questions you may have. Thanks.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, mistakes · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, notes · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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: answers · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% 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
12%12% 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.

Incorrect prediction on native model

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