Hi, I'm Jericho.

What does automation do?

• Starts with a voice or audio file input: Accepts an audio source (from a form, upload, or cloud folder).
• Transcribes the audio using Whisper/OpenAI API: Converts spoken content into written text via a transcription engine.
• Cleans and parses the transcription: Text parser module organizes the transcription into readable segments.
• Uses AI to summarize or rewrite the transcript: GPT model processes the transcript to summarize or rewrite in blog, social, or email formats.
• Routes content based on its type or purpose: A router divides content paths for blog publishing, social media, or visual tools.
• Posts final content to a platform or CMS: Depending on the route, content is pushed to a blog, Airtable, or Canva for visual generation.

Why do we do it?

• To convert raw voice recordings into useful content: Speeds up the workflow from idea (spoken) to final product (written and published).
• To support content creators, marketers, and thought leaders: Enables non-writers to create quality content just by speaking.
• To improve speed, accuracy, and scale of content production: Eliminates hours of manual transcription, editing, and reformatting.

What is the significance/study of it?

• Demonstrates multimodal automation (audio > text > publish): A complete loop from voice input to published media using AI and logic flows.
• Highlights real-time transcription-to-publication capability: Makes it possible to produce content daily from spontaneous voice notes.
• Enables rapid content repurposing: One input creates multiple content forms: blog posts, social snippets, podcast notes.

How do we do it?

• Step 1: Receive or trigger an audio file: Intake could be from an uploader, Drive, Dropbox, or webhook trigger.
• Step 2: Transcribe the audio: Whisper/OpenAI transcription service converts voice to text.
• Step 3: Parse the raw text: Module separates and structures the transcript logically.
• Step 4: Apply GPT to rewrite or summarize: Generates marketing-ready text using AI summarization models.
• Step 5: Route content to different endpoints: Blog text goes to WordPress, Airtable stores meta, Canva for visuals, etc.
• Step 6: Publish or store results: The final version is posted or stored for review or automation chaining.

Walk us through a complex automation/system you’ve built

Problem Solved:

Coaches and podcast creators were spending hours turning voice notes into blog posts, summaries, and captions. Manual transcription and editing led to inconsistencies and delays.

Tools/APIs Used:

• OpenAI Whisper API for accurate voice-to-text transcription
• Make.com routers and HTTP modules for flexible logic paths
• GPT-4 for summarization and format conversion (caption, blog, etc.)
• Airtable for metadata storage and workflow tracking
• Canva API for image generation (quote graphics, post templates)
• Google Docs or WordPress API for final publishing

Final Result/Impact:

• Reduced content cycle time from 3 days to 1 hour
• Enabled clients to produce 4x more content weekly
• Delivered more consistent tone and message alignment
• Allowed teams to publish real-time updates from voice notes during live events
• Improved audience engagement due to higher output and timely publishing

What does automation do?

This automation listens for image messages on Telegram, extracts text using OCR, logs the data in Google Sheets & Drive, then analyzes the content using an AI agent to send back an intelligent response via Telegram.

Why do we do it?

• To automatically process image files (e.g. receipts, forms, screenshots) sent to Telegram.
• To store text content in Google Sheets/Drive for recordkeeping or auditing.
• To provide AI-powered answers or summaries based on the extracted content.
• To eliminate manual data entry and response crafting.

What is the significance/study of it?

• Combines OCR (Optical Character Recognition) and AI NLP to interpret image content in real time.
• Fully automates a chat-based document processing pipeline using Telegram as UI.
• Demonstrates advanced AI agent integration with memory and contextual intelligence.

How do we do it?

Step-by-step Breakdown:

• Telegram Trigger: Starts when a message is received in Telegram.
• Telegram (Get File): Fetches the image file that was sent by the user.
• HTTP Request to OCR API: Sends the image to an OCR service (like OCR.space) for text extraction.
• Code Block: Cleans up or processes the raw OCR response for further use.
• Google Sheets (Append/Update): Logs the extracted text into a Google Sheet for structured storage.
• Telegram1 (Optional Redundant File Get): Possibly used for a second image or an alternative format.
• Google Drive (Upload File): Stores the original file for archiving or backup purposes.
• Edit Fields: Allows manual field updates or additions (if necessary).
• AI Agent (Tools Agent): Analyzes the extracted text and generates a reply or summary using the connected AI.
• OpenRouter Chat Model + Window Buffer Memory: Adds memory context for ongoing conversations and uses an LLM (e.g. GPT-style) via OpenRouter.
• Telegram2 (Send Message): Sends the AI-generated response back to the Telegram user.

Walk us through a complex automation/system you’ve built

Problem Solved:

Users frequently send screenshots or image files via Telegram and expect human-like understanding or documentation. Manually reading, interpreting, and responding wastes time and introduces human error.

Tools/APIs Used:

• Telegram Bot API – To handle incoming and outgoing messages
• OCR API (OCR.space) – To convert image content into machine-readable text
• Google Sheets + Drive – For structured data storage and archiving
• AI Agent via OpenRouter (ChatGPT-compatible) – To analyze content and generate intelligent replies
• n8n Workflow Builder – To orchestrate all logic and tools visually
• Memory Module – To keep context across conversations

Final Result/Impact:

• Fully autonomous Telegram bot that acts like a smart assistant
• Can receive, process, and respond to document-based messages in under 10 seconds
• All data logged securely for later use, review, or training
• Removes the need for human intervention in repetitive document understanding tasks