Relaticle 是开源 CRM，内置审批门控的 AI 助手，可自主处理记录，给需要可控 AI 的销售团队使用。
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PH 用户
Hi Product Hunt, Manuk here. I've been building Relaticle for about two years, mostly solo.
It started as a conventional open-source CRM. Then I added agents and hit a question I couldn't dodge: how much authority should each kind of client get? Read-only access was too limited to be useful. Silent writes into customer data felt wrong.
I ended up with two trust levels. External MCP clients (Claude, Cursor, anything that speaks MCP) authenticate over OAuth and write directly through 37 tools, the same way an API client would. The in-app assistant works next to human users, so every write it wants becomes a proposal that shows the exact change and waits for approval. Batches are reviewed record by record: you approve or skip each one, and whatever you approved is kept.
The chat client itself was rebuilt after failures I hit in real use. A rate-limited retry could delete a conversation turn. A stale stream handler could write a draft into the wrong conversation. Drafts now survive reloads, failed sends stay visible, and answers link back to the records they mention.
Everything self-hosts under AGPL, including model inference with Ollama. Each workspace defines its own custom fields, and they show up in the agents' schemas automatically.
One question I'm still chewing on and would genuinely like opinions on: should an in-app assistant ever get policy-based auto-approval, or should every write stay explicit?
PH 用户
Would love an undo trail for approved writes, not just the approve step. Excited to see where this goes!
PH 用户
Really like the approach here! Letting AI do the heavy lifting while keeping users in control of every change feels like the right balance. Have you considered adding customizable approval rules for low-risk updates?
PH 用户
@manukminasyan Manuk jan, congrats on the launch!
It’s great to see a CRM that isn’t just adding AI on top, but is actually designed around AI from the ground up. And the MCP compatibility is really cool stuff! Good luck =)
PH 用户
This project has a solid based of Laravel that have helped me learn Laravel. I learned so much about how to setup Laravel in a professional - enterprise grade setup which alone is gold. In combination with AI - MCP, it really can turn a basic CRM into a full-fledge internal processing system, ie: an ERP.
It started as a conventional open-source CRM. Then I added agents and hit a question I couldn't dodge: how much authority should each kind of client get? Read-only access was too limited to be useful. Silent writes into customer data felt wrong.
I ended up with two trust levels. External MCP clients (Claude, Cursor, anything that speaks MCP) authenticate over OAuth and write directly through 37 tools, the same way an API client would. The in-app assistant works next to human users, so every write it wants becomes a proposal that shows the exact change and waits for approval. Batches are reviewed record by record: you approve or skip each one, and whatever you approved is kept.
The chat client itself was rebuilt after failures I hit in real use. A rate-limited retry could delete a conversation turn. A stale stream handler could write a draft into the wrong conversation. Drafts now survive reloads, failed sends stay visible, and answers link back to the records they mention.
Everything self-hosts under AGPL, including model inference with Ollama. Each workspace defines its own custom fields, and they show up in the agents' schemas automatically.
One question I'm still chewing on and would genuinely like opinions on: should an in-app assistant ever get policy-based auto-approval, or should every write stay explicit?
It’s great to see a CRM that isn’t just adding AI on top, but is actually designed around AI from the ground up. And the MCP compatibility is really cool stuff!
Good luck =)
Wish alot of success for Manuk for the launch!
我喜欢 AI 不只是生成文本，而是真的能做事，而且在它动你的数据之前，你仍然可以批准变更。
再加上整个东西开源 + 可自托管，是个巨大的加分项。干得漂亮！！