Brief

🎯 Core Function: Dual-Source Market Intelligence & Sentiment Analysis

This workflow is a powerful "Buying Guide" engine. It goes beyond simple searching by combining two distinct layers of truth: Search Engine AI Summaries (for technical specs and general consensus) and Reddit Community Discussions (for unfiltered user feedback). It synthesizes these diverse sources into a single, unbiased "Pros & Cons" report delivered via Telegram.

Part 1: BrowserAct Workflow Description

This core module performs a multi-tab investigation to gather a 360-degree view of a product:

Google AI Summary Extraction:

Dynamic Search: The Agent visits Google and executes a targeted query: [Keyword] + customer reviews.

AI Mode Activation: Uniquely, the Agent clicks the "AI Mode" (or AI Overview) button in the search interface to trigger the search engine's native summarization.

Data Capture: It expands the side panels ("Show all inside the right ribbon") to capture the platform's high-level product summary and referenced sites into the G_Summary field.

Reddit Community Deep Dive:

Community Search: The Agent navigates to Reddit, locates the search bar, and inputs the [Keyword] + Reviews.

Discussion Mining: It scrapes the resulting thread summaries and user comments into the R_Summary field, capturing real-world usage experiences and pain points often missed by professional reviewers.

Consolidated Output:

The workflow merges the "Official/AI" summary from Google with the "User/Real-world" data from Reddit into a single JSON payload for processing.

Part 2: Automation Integration with Make.com

The Make.com scenario acts as the intelligent interface, ensuring the user gets a structured answer rather than a list of links:

Intent Classification (Trigger):

The workflow starts via Telegram. An initial AI node analyzes the user's message to distinguish between casual chat ("Hi, how are you?") and a research request ("Is the iPhone 15 worth it?").

Keyword Optimization:

If a search is detected, a second AI node refines the user's raw text into optimal keywords for the BrowserAct scrapers.

BrowserAct Execution:

The system calls the BrowserAct node to perform the dual-platform scraping described in Part 1.

Final Synthesis & Recommendation:

The raw data from Google and Reddit is sent to a final AI model with a strict "Buying Advisor" prompt. The AI is instructed to generate:

Product Summary: A brief overview of reception.

Upsides (Pros): Bullet points of top strengths.

Downsides (Cons): Bullet points of critical pain points.

Final Verdict: A definitive recommendation (Buy/Don't Buy).

Delivery: This structured report is sent back to the user on Telegram.

✨ Applicable Scenarios (Use Cases)

Smart Shopping Assistant: Use it as a personal bot to quickly vet electronics, software, or appliances before purchasing, saving hours of reading forum threads.

Brand Reputation Management: Companies can run this workflow on their own products to see the contrast between "what Google says" (SEO) and "what Reddit says" (User Sentiment).

Dropshipping/E-commerce Research: Quickly validate product ideas by seeing if real users on Reddit are complaining about specific quality issues that aren't obvious on Alibaba/AliExpress.

Tech Review Aggregation: Bloggers can use this to get a "pre-written" draft of the consensus on a new gadget to speed up their review writing process.

55-100credits per task (estimated)