AI Product Analysis Generator

Generate comprehensive product analyses using AI agents that gather and summarize product information, pricing, features, reviews, alternatives, and more from public internet sources.

How the AI Flow works - AI Product Analysis Generator

Flows

How the AI Flow works

Collect Product Data From Web.
Uses Google Search, YouTube, and URL retrievers to gather product information, reviews, and related content from the internet.
Analyze Product Aspects with AI Agents.
AI agents extract and summarize details such as product overview, history, customer segments, features, pricing, user insights, alternatives, and video reviews.
Coordinate Research Tasks.
A self-managed crew of AI agents collaborates to ensure all aspects of the product analysis are covered efficiently.
Generate Structured Product Report.
Combines the researched data into a structured product analysis report using prompt templates and a language model.
Deliver Results via Chat.
Displays the generated product analysis and insights via chat output for easy user access.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Product Alternatives

AI agent that searches for product alternatives, describes them, and identifies key differences and features.

                Role: Researcher Agent for Product Alternatives
Backstory: A product alternatives analyst specializing in searching alternative products, identifying key differences between product and product alternative
Goal: Search for top 10 product alternatives, give short description of product alternative, identify key features and key difference between product and product alternative

Products & Services

AI agent analyzing product customer segments, describing who uses the product and why.

                Role: Researcher Agent for Products & Services customer base
Backstory: A market analyst specializing in product differentiation, adept at navigating market research to categorize and summarize product lines and customer segments.
Goal: Identify and describe the customer segment which use the product or for which is product dedicated
Answer questions like For who is the product dedicated, why they need it, how it helps to customer or user

Prompt (Product Data Extraction)

Prompt template instructing LLM to extract structured product information including features, history, pricing, reviews, and alternatives.

                Given the product name in the input, Extract the following data based on the product name:

1. About the product (What it is)
2. Product History
3. Segment, Focus (For who is the product dedicated, why they need it, how it helps to customer or user)
4. Main product features
5. Pricing options
5. Key user insights (Pros, Cons, Overall user experience from reviews)
6. Youtube reviews (if available, list 1-3 links to youtube videos with title and short description)
7. Product Alternatives (with links to websites of alternatives)

PRODUCT NAME: {input}

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Message Widget

The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.

See Message Widget

Chat Output

Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.

See Chat Output

Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

See Chat Opened Trigger

AI Agent

The AI Agent component in FlowHunt empowers your workflows with autonomous decision-making and tool-using capabilities. It leverages large language models and connects to various tools to solve tasks, follow goals, and provide intelligent responses. Ideal for building advanced automations and interactive AI solutions.

See AI Agent

URL Retriever

Unlock web content in your workflows with the URL Retriever component. Effortlessly extract and process the text and metadata from any list of URLs—including web articles, documents, and more. Supports advanced options like OCR for images, selective metadata extraction, and customizable caching, making it ideal for building knowledge-rich AI flows and automations.

See URL Retriever

GoogleSearch Component

FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.

See GoogleSearch Component

YouTube Search Tool

FlowHunt's AI-driven YouTube Search Tool offers personalized video and creator recommendations. Go beyond standard YouTube searches and let AI agents find the best creators and content for your interests.

See YouTube Search Tool

LLM OpenAI

FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.

See LLM OpenAI

Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

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Self-Managed Crew

Unlock advanced collaboration in FlowHunt with the Self-Managed Crew component. Coordinate multiple AI agents under a manager agent to autonomously handle complex workflows and hierarchical tasks, maximizing efficiency and scalability.

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ChatInput

The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.

See ChatInput

SelfManaged Task

The SelfManaged Task component enables users to define and execute autonomous tasks within a workflow. Specify a clear task description, expected outcome, and assign an agent to manage execution—ideal for building structured, hierarchical automation in your flows.

See SelfManaged Task

Prompt Component in FlowHunt

Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.

See Prompt Component in FlowHunt

Flow description

Purpose and benefits

Overview

This workflow automates the process of generating a comprehensive product analysis report using publicly available data from the internet. It leverages multiple AI agents, information retrieval tools, and prompt templates to gather, analyze, and summarize a wide range of product information. The final output provides a detailed, human-readable report covering product details, history, features, pricing, user insights, video reviews, and alternatives.

How the Workflow Operates

1. User Input and Onboarding

  • User Initiation: The process begins when a user starts a chat or submits a product name through the chat input interface.
  • Welcome Message: A message widget informs the user that the report will take several minutes, as it involves analyzing data from multiple online sources.

2. Data Gathering

  • Web Search: The workflow initiates a Google search for the product, collecting relevant URLs and content.
  • URL Retrieval: Content from these URLs is fetched and processed, enabling deeper analysis and extraction of up-to-date information.
  • YouTube Reviews: A dedicated tool searches for the most recent and relevant YouTube reviews, gathering video links and brief descriptions.

3. Specialized AI Agents

Several AI agents process the gathered data, each focusing on a specific aspect of the product:

Agent RolePurpose
Product OverviewExtracts essential product details, explaining what it is and what it does.
Product HistoryGathers historical context, founding details, milestones, and investments.
Customer SegmentationIdentifies and describes customer segments and use cases.
Product FeaturesExplores key features, unique selling points, and reasons for customer choice.
Product ReviewsAnalyzes user reviews to summarize pros, cons, and overall user experience.
Pricing AnalystIdentifies pricing strategies and options, explaining suitability for different user segments.
Product Alternatives AnalystFinds top alternatives, highlights key differences, and provides links to their websites.
YouTube Reviews AnalystLists and summarizes the most up-to-date YouTube video reviews for the product.

Each agent is configured with a backstory and goals tailored to their specific task, ensuring thorough and focused analysis.

4. Task Coordination

  • Task Definition: A self-managed task specifies the expected outcome: a thorough, data-rich product report.
  • Task Assignment: A “SelfManaged Crew” component orchestrates the collaboration of all specialized agents, ensuring that each aspect is covered and the findings are integrated.

5. Prompt Engineering

  • Prompt Templates: The workflow uses structured prompt templates to guide the agents and the final report generation. For example, prompts instruct the system to extract:

    • Product overview
    • History
    • Market focus
    • Features
    • Pricing
    • User insights
    • Video reviews
    • Alternatives

  • Final Article Generation: An advanced prompt template, coupled with an OpenAI language model, generates a well-structured article with headings and subheadings, ensuring clarity and readability.

6. Final Output

  • Report Delivery: The finished analysis is displayed in the chat interface, presented as a comprehensive, human-friendly article. The output includes all gathered insights, structured for easy understanding and further use.

Why This Workflow is Useful

  • Scalability: By automating each step with AI agents and information retrieval tools, the workflow can generate analysis reports for any product with minimal human intervention.
  • Comprehensiveness: The report covers every key aspect a business, marketer, or researcher might need, from basics to deep user insights and competitive analysis.
  • Efficiency: Tasks that would take hours of manual research and writing are completed in minutes, freeing up valuable time and ensuring consistency.
  • Customization: The modular agent structure allows easy adaptation or extension to focus on other product dimensions or data sources.
  • Automation of Repetitive Tasks: Perfect for market intelligence, product comparison, or content creation teams who need regular, detailed reports at scale.

Workflow Structure Summary

Main Steps:

  1. User Input → Welcome message & instructions
  2. Automated Data Collection (web, YouTube)
  3. Specialized AI Analysis (overview, history, segmentation, features, reviews, pricing, alternatives, video reviews)
  4. Task Orchestration (agents work in parallel, results are merged)
  5. Prompt-based Report Generation (clear, readable format)
  6. Output to User (chat interface)

Key Tools & Components:

  • Google Search & URL Retriever
  • YouTube Search Tool
  • Multiple AI agents with specialized roles
  • Prompt templates for analysis and article generation
  • OpenAI language model for final synthesis

This workflow is ideal for anyone needing to scale up and automate product research, competitive benchmarking, or content creation, ensuring reliable, comprehensive, and up-to-date analyses with minimal manual effort.

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