After 100+ conversations with app builders, the same thing kept coming up: Everyone wants an AI copilot in their product. No one has time to wire it up. So we built Crow to add a chat-first copilot that can take real actions on any product within minutes.
Hi Product Hunt, Jai from Crow here.
I kept seeing people screenshot product UIs and ask ChatGPT what to click.
That felt like a failure mode, not a feature.
So I built something small:
An agent that lives inside a product and executes real actions via its API.
Flow:
- upload OpenAPI spec
- tools get generated automatically
- drop in a script tag to your frontend
- users talk to the product
- agent executes API calls on behalf of user
This is early and rough, and I’d love honest feedback. I’ll be in the comments answering anything!
Do you plan to support integrations with common product development stacks (e.g., React, Vue for frontend, or SaaS backends like Airtable) to expand compatibility? Also, will builders be able to customize the Copilot’s permissions (e.g., restrict certain high-risk actions) or brand its interface to align with their product’s design?
When you see people constantly taking screenshots and pasting them into GPT, what kind of questions are they asking? If I want to boost my efficiency, what exactly do I need to do? Can simply chatting solve the problem?
@Crow@jai_bhatia2 Making products AI-native via copilot is smart positioning.
Does Crow support design tools (like adding AI to Figma plugins), or is it mainly for SaaS products?
Also curious about the API approach, how much customization do you get vs pre-built templates? Would be cool for design tool makers.
This is an intriguing idea. Loved your approach for solving the general pain of the customers.
I would love to see: 1) Conditional semantic workflow set up 2) API to get just responses, without the chat UI
Congrats!! but what sets it apart from all the similar projects out there?
Crow looks like an exciting tool for making products AI-native. It promises to streamline integration with ease. How does it tackle data privacy concerns while implementing AI features in products?
Congrats on the launch! Access to BE endpoints on one hand means the agent is powerful and can make useful tool calls, but not without risks. As a malicious user, can't do funny things?
Is the onus on the provider of the endpoints to add grade A security, or does Crow add a proxy in the middle to handle things like rate limiting (so the endpoint isn't spammed either by a malicious user or LLM gone rogue), strong schema validation etc.?
If you manage to add a proxy, it can change the perception of users who are more on the cautious side i.e., some extra confidence beyond the JWT auth
Hi @jai_bhatia2, do you use native AI agents under the hood that learns from the knowledgebase uploadd or scrapes the website based on the URLs provided? Also, when you kept seeing people take screenshots and pasting to GPT, what kind of questions were asked?
I suggest based on the adoption, you can let users build flows and run inside the widget making it a good rule-based and AI based web bot.
