What started as a simple experiment has turned into 7 different AI call assistants, with another one currently in development. Some of the capabilities I've implemented so far: • Inbound call handling• Outbound calling workflows • Lead qualification• Appointment scheduling • Rescheduling and cancellations • SMS confirmations and follow-ups • Reminder calls before meetings • CRM updates and record management • Call transfers when needed • Multi-step business workflows The most interesting challenge wasn't getting the AI to talk. It was getting it to remember. One of the assistants can remember previous conversations across multiple calls, retrieve historical context, and continue the conversation naturally instead of treating every call like it's the first interaction. Building these systems taught me that the hard part isn't the voice model itself—it's the orchestration behind the scenes. Memory, workflows, CRM integration, scheduling logic, error handling, and all the small edge cases end up being where most of the work happens. Still a lot to improve, but it's been fascinating seeing how far AI voice technology has come in such a short time. Curious what everyone else is building in the voice AI space right now.

Recently worked on an AI system for a music industry website redesign. There were two parts to it: 1. A public-facing AI chat widget for general queries + lead capture 2. A private, full-screen AI assistant inside the client portal. The second one was more interesting. The client didn’t just want “AI support” — they were concerned about users overusing it and burning unnecessary tokens. So instead of a typical chatbot, I designed a session-based interaction system: - Each user gets a 5-minute active window - At minute 4 → a warning is triggered (“1 minute left”) - At minute 5 → session ends + redirects to an external GPT - Then a 5-minute cooldown kicks in - After cooldown → user can start again It basically runs in a loop. What’s interesting is how this changes behavior: Users become more intentional with their questions instead of treating it like an endless chat. Also ended up optimizing one of their internal workflows in the process: A task that used to take ~6 hours manually is now done in under 10 minutes. No fancy theory here—just structuring AI usage in a way that actually makes sense operationally. Still experimenting with how far this “controlled AI interaction” approach can go.

Hello everyone Just trying to share my Idea to you to if you want I started my YouTube channel last year and have been able to sort great things out , currently am making $5kmonly. Have been doing it alone been a while 😞 but not working perfectly for me till I suggest highering a specialist who help me in both monetizing and editing my video probably. More information ...

Oh, I see. YouTube is a competitive platform, so it has its own challenges. For me, the hardest part is script writing and keeping the content ideas flowing. I’m in the history niche, so there’s a lot of research involved, too. By the way, which niche are you interested in getting into?

I originally built this in Make, but thought let me see how it looks in Relevance AI - which I think worked better - however I am still using a Tally form in Make to trigger the Relevance Tool and then Make carries on and emails the list. I used the Openrouter with DeepSeek to change the text to HTML for sending in the email. Still have some issues with the 3 dots and the html code pieces that always seems to appear - any suggestions to remove that would be appreciated. Would love for you to try it out and see what you think. https://tally.so/r/mYpJ1B