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2137 contributions to AI Automation Agency Hub
2 likes • 22m
Nice use case, Bibhas. Abandoned cart automation can have a very direct impact because it targets leads who already showed buying intent. Adding personalized follow-ups through GHL based on cart value, product, or time since abandonment could make the workflow even more useful while keeping the messaging relevant.
First Discovery Call From Warm Outreach!
Conducting my first discovery call from my warm outreach!! Wish me luck!!!
First Discovery Call From Warm Outreach!
1 like • 25m
That’s a great milestone, Joe. Getting your first discovery call from warm outreach means the conversations you’ve been starting are actually turning into real opportunities. Go in curious, focus on understanding their current process and pain points, and don’t feel like you need to pitch everything on the first call. Good luck with it.
Do you guys experience enterprise deals getting stuck in procurement?
Because of my law background, I'm curious about this: have you lost weeks on a deal once the buyer's security and procurement reviews started? I’ve been seeing more signs that enterprise AI purchases can take materially longer than ordinary SaaS deals because of the additional security, legal, compliance, and governance scrutiny involved. You might have faced a long security questionnaire, or a buyer who wanted every model and subprocessor you use on paper. Maybe their lawyers pushed AI governance clauses into the contract, or their legal and IT teams each asked for evidence you hadn't prepared. If this has happened to you, what was the biggest source of friction? And how do you handle it today? I'm really curious about how common this actually is in real AI agency/builder deals. If this turns out to be a real problem people here are dealing with, I’d be happy to share some thoughts and practical tips in future posts based on what I know from the legal side.
1 like • 25m
Yes, this can become a major bottleneck with enterprise AI projects. The technical solution might be ready, but security reviews, data handling, subprocessors, access controls, and compliance requirements can add weeks to the process. I think having clear architecture and documentation around data flow, permissions, model usage, and integrations from the beginning can make those reviews much easier.
What happens when a customer sends 3 WhatsApp bubbles for one question?
For context, I'm building an AI customer service agent for a real business as part of my final-year college thesis. The business (subject) is also my client, and I'm doing the project for free. It's a pre-post field study on how far AI can actually transform a traditional/conventional business, end, to end. So, while testing the agent before going live, I realized I'd been typing like a well-behaved user the whole time like in ChatGPT or other AI chat apps: one message, one complete question, then wait for the reply. In reality, almost nobody chats like that on WA. People send "hi", then the "quick question", then the real thing. So I tried it and watched my agent greet me, ask what my question was, and only then answer it. That's three replies for one question, out of order, with each run seeing only a fragment. It read like nobody was listening. The fix is debouncing. Append each incoming message to a buffer keyed by sender, write a unique token for the run, wait a few seconds, then read the token back. If it's still yours, you're the last message in, so grab the whole buffer and send it to the agent as one prompt. If a newer message overwrote your token, stop silently and let that run handle it. Last writer wins and the final execution carries every bubble. ONE THING WORTH KNOWING before you build it is that where you clear the buffer matters more than it looks. I first cleared it at the very end, after the send. If the agent call fails (rate limit, timeout, expired credential), the execution dies before cleanup and those messages stay in the buffer. 2 hours later the same person says "hi again" and gets an answer to what they asked this morning. Clear the buffer right after you read it instead. Worst case, a message gets lost and the customer just asks again. The other way, old messages sit there quietly until the bot answers something nobody asked days later and your client hears about it from their customer. Small details like this are what separate a demo/prototype from something a real business can actually rely on. And it's not just a WA thing, the same problem may shows up on Instagram DMs, Telegram, Messenger, or any chat channel where people type the way they talk.
What happens when a customer sends 3 WhatsApp bubbles for one question?
1 like • 25m
This is a really important edge case, Jordan. WhatsApp users naturally send multiple short messages instead of one complete prompt, so the agent needs some kind of message buffering or debounce window before triggering the AI. Collecting messages for a few seconds, combining them in order, and then processing the final context can make the conversation feel much more natural.
Too many SPAM messages
I don't know why but almost every week I get spam message in the inbox from this group. Is anyone experiencing same issue?
1 like • 26m
I’ve seen similar issues in online communities where spam accounts target members through DMs rather than public posts. It may be worth reporting the messages and checking whether the community has any DM or member privacy settings that can reduce unwanted contact. Hopefully the admins can also identify and remove the accounts causing it.
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Malik Ahmed
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AI Automation Engineer | n8n & AI Agents | Voice AI | Web & Mobile Apps | Building Business Systems

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Joined Apr 2, 2026
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