ify 是一款叠加在现有客服系统上的 AI 客服代理，自动跨渠道回复用户请求，无需替换 helpdesk。
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PH 用户
Hey Product Hunt 👋,
We built Ify because every AI support tool we looked at asked for the same trade: rip out your helpdesk, spend weeks migrating, then maybe get an AI agent that's actually useful.
Ify skips that. It works directly on top of Freshdesk, Zendesk, Salesforce, or HubSpot — so you keep what you have and just add the part that resolves tickets. If you don't have a helpdesk yet, it can run standalone too.
The thing we spent the most time on isn't the chat widget or the automations — it's the knowledge base. Almost every team we talked to had messy or incomplete docs, and that's usually what kills an AI support rollout before it starts. So Ify builds its own: it scrapes your site and docs, turns release notes and past resolved tickets into SOPs, and keeps learning from what your team resolves manually.
We're in private beta right now, working closely with early SMB and mid-market support teams to get this right before we open it up further. If you're curious how it'd fit with your current stack, or you've hit the "our docs aren't good enough for AI" wall yourself, I'd love to hear about it in the comments.
Would really appreciate any feedback, questions, or just tell us what you'd want an AI support agent to actually do.
Regards,
Karthik - Founder @ ify
PH 用户
Hey everyone! 👋
Sarnith here, CTO & Co-founder at @ify.
Adding a quick note on the technical side of why we built ify this way:
When we set out to build this, one thing became clear very quickly:
AI is only as good as the context behind it — and support documentation is almost never perfect.
A lot of a team’s real tribal knowledge doesn’t live in pristine docs. It’s buried inside resolved tickets, Slack threads, release notes, and agent workarounds.
Instead of forcing support teams to clean up or rewrite their documentation before they can use AI, we built ify to continuously map, absorb, and turn that scattered historical context into usable SOPs.
The goal is for the AI to understand not just what the documentation says, but how the support team actually solves problems.
I’d love to hear from the technical and support folks here:
What’s the trickiest support query or workflow you’d still be hesitant to hand off to an AI agent today?
PH 用户
I've sold many similar products over the last decade and this is impressive. Worth checking out for sure.
PH 用户
What’s one feature you built because customers kept asking for it—and what surprised you about how they use it?
PH 用户
Works great and so easy to set up ! Congrats Karthik and team!
We built Ify because every AI support tool we looked at asked for the same trade: rip out your helpdesk, spend weeks migrating, then maybe get an AI agent that's actually useful.
Ify skips that. It works directly on top of Freshdesk, Zendesk, Salesforce, or HubSpot — so you keep what you have and just add the part that resolves tickets. If you don't have a helpdesk yet, it can run standalone too.
The thing we spent the most time on isn't the chat widget or the automations — it's the knowledge base. Almost every team we talked to had messy or incomplete docs, and that's usually what kills an AI support rollout before it starts. So Ify builds its own: it scrapes your site and docs, turns release notes and past resolved tickets into SOPs, and keeps learning from what your team resolves manually.
We're in private beta right now, working closely with early SMB and mid-market support teams to get this right before we open it up further. If you're curious how it'd fit with your current stack, or you've hit the "our docs aren't good enough for AI" wall yourself, I'd love to hear about it in the comments.
Would really appreciate any feedback, questions, or just tell us what you'd want an AI support agent to actually do.
Regards,
Karthik - Founder @ ify
Sarnith here, CTO & Co-founder at @ify.
Adding a quick note on the technical side of why we built ify this way:
When we set out to build this, one thing became clear very quickly:
AI is only as good as the context behind it — and support documentation is almost never perfect.
A lot of a team’s real tribal knowledge doesn’t live in pristine docs. It’s buried inside resolved tickets, Slack threads, release notes, and agent workarounds.
Instead of forcing support teams to clean up or rewrite their documentation before they can use AI, we built ify to continuously map, absorb, and turn that scattered historical context into usable SOPs.
The goal is for the AI to understand not just what the documentation says, but how the support team actually solves problems.
I’d love to hear from the technical and support folks here:
What’s the trickiest support query or workflow you’d still be hesitant to hand off to an AI agent today?
我很好奇。这跟 Fin 之类的有什么不同？