BrowserAct-Powered Movie Recognition & Search Bot

Make AI Film Finder Machine helps users quickly identify a movie and discover similar recommendations based on vague descriptions, scenes, images, or short video clips — without manually searching multiple platforms or guessing movie titles.

When users remember only fragments of a movie (a scene, a character, a mood, or a visual clip), traditional search methods often fail. This workflow solves that problem by combining Make orchestration, AI intent understanding, and browser-level search automation to transform unstructured input into accurate movie identification and recommendations.

The workflow starts when a user sends a movie description, image, or video through Telegram. Make acts as the central orchestration layer, first validating whether the input is movie-related. If valid, AI refines the input into a structured, search-ready description. Make then triggers a BrowserAct workflow to search the web and film databases for candidate movies. The returned results are analyzed again by AI to identify the most likely match, generate concise summaries, and recommend similar movies. The final result is delivered back to the user as a clean, Telegram-ready message.

Input:

• Movie description (text)
• Movie scene image or short video clip

Output:

• Identified main movie
• Short movie summary
• List of similar movies with brief descriptions
• Genre-based tags

This automation is ideal for movie lovers, content creators, reviewers, and researchers who want fast, accurate movie identification and recommendations without manual trial-and-error searching.

Typical use cases include:

• Finding a movie from a vague scene or memory
• Identifying a movie from an image or short clip
• Discovering similar movies based on style or genre
• Creating movie recommendation content for social platforms

Working Principle (How it works)

1. Trigger (Telegram)
   The workflow starts when a user sends a movie description, image, or video clip via Telegram. Make receives and standardizes the input as the workflow entry point.
2. Input routing & type detection (Make)
   Make uses routers and filters to determine whether the input is text-based or file-based, automatically selecting the correct processing path.
3. Intent validation (AI)
   AI checks whether the input represents a valid movie-related request. If the input is unclear or insufficient, the user is prompted to provide more information before continuing.
4. Description enhancement (AI)
   AI refines the user’s vague or fragmented input into a concise, professional, and search-optimized movie description, suitable for downstream search and analysis.
5. Movie search execution (BrowserAct)
   Make triggers a BrowserAct workflow with the enhanced description. BrowserAct performs browser-level searches across Google and film-related sources to collect candidate movies and summaries.
6. Result analysis & synthesis (AI)
   AI compares the user description with the returned movie list, identifies the most likely match, generates compact summaries, selects similar movies, and creates genre-based tags.
7. Formatting & delivery (Make)
   Make formats the final output into a Telegram-compatible HTML message and sends the structured movie analysis back to the user automatically.