This video serves as an introductory guide to AI Agents for businesses. With influential figures like Mark Zuckerberg and Jensen Huang highlighting the transformative impact AI Agents are set to have, it's crucial to get ready. I will explore various real-world applications of AI Agents and share insights into their future potential.

After a few days of battling small but annoying technical issues (you know the ones!) and carefully choosing the best infrastructure, I finally have my step flow ready. One thing I’ve learned during this process is that my developer background often leads me to think of large, complex solutions—which, while exciting, aren’t always necessary to deliver the best results at this stage. Keeping it simple but effective has been the key! Here’s the current workflow: 1️⃣ File Uploads: Files are uploaded to a Google Drive folder. 2️⃣ Format Conversion: Files are converted to WAV format using Zamzar. 3️⃣ Transcription: Both Whisper and Google Speech-to-Text API process the files. 4️⃣ Quality Check: ChatGPT compares the two transcriptions and selects the most accurate one. 5️⃣ Translation: The transcription is passed to both Deepl and Google Translate API. 6️⃣ Final Review: GPT-4 evaluates the translations, checks the original transcription, and produces the most accurate translation. The result? A smart and efficient workflow that leverages the best tools to optimize quality and results. 🌟 🚀That said, there’s one use case I still need to address: files larger than 25MB. These need to be split into smaller chunks before following the same flow. It’s a challenge I’m excited to tackle next! I’ll keep you posted with the outcomes and new developments as this project evolves. 🎉 #workflowautomation #ai #gpt4 #googlecloud #deepl #whisper #optimization

Hi everyone, 👋 I’m in the process of implementing an automated workflow for audio transcription and translation, and I’d love to hear your insights or experiences on ensuring the quality of results in such processes. Here’s the flow I’m building: 1️⃣ Audio File Submission: Clients upload files via a webpage or email. 2️⃣ Transcription: Using tools like OpenAI Whisper. 3️⃣ Translation: DeepL. 4️⃣ Delivery: Results are sent as a Google Doc via email and a WhatsApp notification. My Questions: Transcription Quality: ow do you handle accents, background noise, or overlapping speech to improve transcription accuracy? Are there specific settings, APIs, or pre-processing techniques you’ve found useful? Translation Accuracy: How do you ensure translations are contextually accurate, especially for industry-specific terms or phrases? Do you use a secondary QA step or additional tools for checking the translations? Automated QA Checks: Have you implemented any automated quality control steps in your flows? For example, using confidence scores or running translations through multiple engines for comparison. Handling Errors: What’s your process for flagging and resolving errors, such as low-confidence transcriptions or mistranslations? Do you involve human review in specific cases? Client Feedback Loop: Do you have a system to gather client feedback to improve future results? Any tips, tools, or strategies you could share would be incredibly valuable! I want to ensure my flow delivers both efficiency and reliable quality. Looking forward to hearing your thoughts! 🙏

In this video, I compare IVR Systems to AI Phone Agents. AI Phone Systems are the combination of AI models, think of ChatGPT, combined with AI Voice Models, which are AI generated human like voice overs. If we combine both of these technologies, we can create real-time human-like calling systems. An IVR system is a much more static system that uses the dial pad to allow users to select which department or team they would like to speak with. This system can also read out automated messages based on pathways selected by the user. Please leave a comment to help boost the video 🙏

This video will show you how to create an AI-powered Dental Receptionist capable of booking appointments in Google Calendar and answering service-related inquiries. Please drop a comment on the video! 💪