Hello guys, I currently am running an AI agency for 3-4 months now and have been really starting to dive deep into the AIOS's that are currently out there right now. I have had lots of conversations as well with people and getting everyone's opinions and takes on the future of this and what people are recommending. I have had some mixed opinions and both sides seeming to have there pros and cons for sure. I wanted to post in this community and see if anyone in here has any information or feedback that could help make my decision for me. Claude Code or OpenClaw, which one of these is the right path to take and put my full focus into, while also helping with my agency's tasks on a day to day basis and just an overall operating system behind the scenes. Thank you

Hello guys, I was able to get a case-study client today. Their problem: Needs more leads in his niche. I have never worked with lead creation and enrichment before so if you have any tips for me/resources that I could look into I would appreciate it so much. Hope you all have a great day!

Most client problems start with communication failure. Here is the system that prevents them. THE WEEKLY UPDATE: Every Monday, every client gets: "Weekly Update for [Client]: AUTOMATION STATUS: Running normally / Issue detected THIS WEEK'S PROCESSING: - Documents processed: [X] - Success rate: [X%] - Flagged for review: [X] ISSUES (if any): - [Description and resolution] UPCOMING: - [Any planned changes or maintenance] Questions? Reply here or message in Slack." Takes 2 minutes per client. Prevents hours of confusion. THE STATUS LEVELS: GREEN: All systems normal "Everything running smoothly. No action needed." YELLOW: Minor issue detected "Noticed [issue]. Already investigating. Will update by [time]." RED: Major issue "[System] is down. Working on fix. Manual workaround: [steps]. ETA for resolution: [time]." THE PROACTIVE ALERT: Do not wait for client to find problems. Bad: Client emails "Nothing processed today. What is wrong?" Good: You message "Heads up - processing delayed this morning due to [reason]. Already resolved. Backlog clearing now." THE COMMUNICATION CHANNELS: Slack: Quick questions, status updates Email: Formal documentation, contracts Call: Complex issues, quarterly reviews THE RESPONSE TIME COMMITMENTS: Slack: Within 2 hours during business hours Email: Within 24 hours Urgent issues: Within 1 hour THE ESCALATION PATH: Level 1: Message in Slack Level 2: Email with "urgent" in subject Level 3: Phone call (for true emergencies) THE DOCUMENTATION: Every issue gets documented: - Date and time - Issue description - Root cause - Resolution - Prevention steps Share summary with client. Shows professionalism. THE QUARTERLY REVIEW: Every 3 months, schedule 30-minute call: - Review processing statistics - Discuss any challenges - Identify optimization opportunities - Preview upcoming changes - Ask about their evolving needs THE AGENDA TEMPLATE: 1. Performance review (5 min) 2. Issues and resolutions (5 min)

Hello guys, just a few weeks into this AI world and I have already been able to create something sought for. Was on a call the other day and they said they has a problem: When a CV arrives in their company email, they want an instant follow-up to tell the people that their CV is being evaluated. So I got down to it and built it for them. It's simple enough but the fact that it solves a real problem is the most important. What do you think? Any improvements? PS: Creating the IMAP and SMPT email credentials was such a pain😆 Anybody relates?

Yo everyone! I just finished building a new Make.com workflow for outbound campaigns and wanted to share the sauce. If you do any kind of cold email, you know the pain of scraped data making you sound like a robot (e.g., "Hey! I noticed you work at Elate Staffing Solutions Ltd..."). I built this Growth System to bridge the gap between raw scraped data and perfectly formatted, human-sounding cold emails. Here is the breakdown of how the automation flows: - Trigger (Apify): The workflow kicks off the second an Apify actor finishes scraping a list of target leads. - Validation (Mails.so): It automatically grabs the dataset and runs every email through an HTTP request to an API to check if it's deliverable. Gotta protect that domain reputation! 🛡️ - The Magic Sauce (GPT-4o): Here’s my favorite part. If the email is valid, the data gets passed to GPT-4o. I wrote a prompt that normalizes the company name by stripping out the generic corporate jargon (Inc, LLC, Ltd) and focusing on the most memorable element. So, "Walmart Inc" becomes "Walmart", and "JP Morgan Chase Bank" becomes "JPM". - CRM Push (Instantly): Finally, it pushes the validated email, first name, last name, and the cleaned company name directly into an Instantly campaign. I'm implementing this as part of the backend for my AI automation agency, Zestflow. Honestly, the AI company name cleaner alone is a massive game-changer for keeping personalization looking authentic at scale. Are you guys doing anything similar to clean up your lead lists before sending them to your sending tools? Drop your workflows or tech stacks below!