Connect JSON and Google Gemini AI
Explore ready-made JSON and Google Gemini AI templates and start automating in minutes.

Create a custom workflow
Integrate JSON with Google Gemini AI and automate the work that moves between them — no code required.
Triggers and actions
Explore what you can automate between JSON and Google Gemini AI.
Popular JSON and Google Gemini AI workflows
Connect JSON and Google Gemini AI with Make. Automate manual tasks, sync real-time data, and build more efficient workflows.
BrowserAct YouTube Publishing Strategy Generator
This end-to-end automation extracts YouTube channel video data, analyzes historical performance, and identifies the optimal publishing time to maximize viral potential. Using BrowserAct, the workflow dynamically scrapes all videos from a selected channel and stores Title, URL, Views, Likes, and Published Date into Google Sheets. The data is then aggregated and sent to Gemini AI with a structured analytical prompt that examines performance by day of week, hour of day, and minute. The AI model detects high-performing time windows, negative “dead zones,” and statistically significant publishing patterns. The final insights are automatically written back to a dedicated results sheet, aligned to the correct row, enabling fast review and action. This template is ideal for creators, content teams, agencies, and analysts who want a repeatable, data-driven method to optimize their YouTube publishing strategy. No manual data pulling, no repetitive analysis—everything runs end-to-end with one click.
BrowserAct Forbes Category News Scraper
This template automates the full pipeline of news content delivery from Forbes to Telegram. The workflow starts with a Google Sheet that defines the target category (such as innovation or business). Make iterates through the input and triggers a BrowserAct workflow to open the corresponding Forbes section, handle scrolling or pagination, and extract structured article data including headline, author, image URL, and article link. The extracted data is parsed and processed item by item. A short delay is applied to ensure stable execution and avoid rate limits. Each article is then sent to Google Gemini AI, which rewrites and refines the headline, formats the author credit, and generates Telegram-ready content following strict output rules. A router determines the delivery format: – If an article includes a valid image, it is published as a photo post with a caption. – If no image is available, it is published as a text-only message. All messages are sent using HTML formatting to ensure clean layout, bold headlines, and clickable links. This template is ideal for automated news curation, Telegram channel operations, media monitoring, and real-time content distribution without manual editing.
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A scenario represents a workflow or a project of your own creation, and it is made up of a series of modules that automate apps and services. Creating a scenario allows you to transfer and transform data between apps and services via these modules to automate anything and improve the way you work.
What is a module?
Modules are the main building blocks of automation in Make. Modules represent actions that Make performs with an app, like creating, updating, or deleting data.
What is mapping?
Mapping links the modules in your scenario. When you map an item, you connect the data retrieved by one module to another module to perform the desired action. For example, you can map email addresses and subject lines to create a spreadsheet using this data.
