SEO Content Gap Analyzer

This AI-powered workflow analyzes the content structure of your web page, compares it with top-ranking competitor pages, and provides tailored recommendations on how to improve your page’s SEO performance. By leveraging Google search, advanced language models, and smart content extraction, it helps you close content gaps and optimize your website for higher search engine rankings.

Flows

How the AI Flow works

Receive Web Page URL.: User submits the URL of the web page to be analyzed.
Extract & Analyze Page Content.: The workflow retrieves the structure and headings from the input web page.
Discover Competitor Pages.: Generates alternative search queries and uses Google Search to identify top competitor pages for the same topic.
Compare Content Structures.: Extracts competitor page structures and uses AI to compare them with the user's page, identifying strengths and content gaps.
Generate SEO Improvement Suggestions.: AI delivers actionable recommendations for optimizing the user's web page structure and keywords to enhance search rankings.

Prompts used in this flow

Relevant Search Queries to my page

Prompt template for displaying relevant search queries related to the analyzed web page.

---
# Relevant Search Queries to my page:

{context} 
---

Competitor Content As Plain Context

Minimal prompt template used to pass competitor content as context.

 {context}

SEO Report Output

Prompt template for generating a structured SEO report based on context.

---
# SEO Report

{context} 
---

Improved Heading Structure and Keyword Suggestions

Prompt template instructing the LLM to compare user's web page structure with competitors, suggest improvements, and propose long-tail keywords.

You are SEO specialist.
- Compare structure of my web page with competitor websites.
- Analyze what each competitor did better comparing to my page

- Suggest improved structure of headings for my page to rank higher as competitors
- Focus on keywords used in headings.
- Suggest 5-10 long tail keywords for each paragraph.

Don't return general well known SEO theory, be specific with each improvement, focus on details.

--- MY CURRENT PAGE CONTENT ---
 {input} 
---

--- COMPETITORS:
 {context}

Top Competitor Pages Context

Prompt template for injecting the top competitor pages' content as context.

--- TOP COMPETITOR PAGES ---
 {context} 
---

Query Expansion System Instructions

System message prompt for the Query Expansion component, instructing the LLM to generate competitor-finding queries.

Generate 4 alternative queries user should ask to find competitor pages similar to input page on google.
One of queries could be H1 heading of the input page
Generate query for google search in the way, that it will not match page on domain of input page (prepend query with -site: <name of domain of input document>)

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.

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

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

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

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

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

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.

See Generator

Query Expansion

Query Expansion in FlowHunt enhances chatbot understanding by finding synonyms, fixing spelling errors, and ensuring consistent, accurate responses for user queries.

See Query Expansion

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

Document Retriever

FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).

See Document Retriever

Flow description

Purpose and benefits

Workflow Description: Web Page Content Gap Analysis

This workflow is designed to automate and scale the process of analyzing a web page’s content structure, comparing it with top competitors, and generating actionable SEO recommendations to improve the page’s ranking and visibility. The flow brings together tools for web content extraction, competitor research, AI-powered text generation, and structured reporting.

Step-by-Step Process

1. User Onboarding & Input

Welcome Message: When the chat is opened, the user is greeted with a message explaining the tool’s purpose: to analyze their web page and compare it to competitors.
URL Input: The user provides the URL of the web page they wish to analyze through a chat input widget.

2. Extraction of the Input Page’s Content

Content Retrieval: The workflow uses a URL Retriever to extract structured content (specifically headings H1-H6) from the input URL. This ensures a clear understanding of the page’s organization and key topics.

3. Competitor Discovery via Query Expansion

Content Sampling for Query Expansion: A separate retrieval focuses on extracting the H1 and relevant metadata from the input page.
Alternative Search Queries: Using a Query Expansion AI model (OpenAI GPT-4o-mini), the system generates multiple alternative Google search queries. These queries are crafted to:
- Find competitor pages similar to the input page.
- Exclude results from the input page’s own domain.
- Use paraphrasing and the H1 heading for better coverage.

4. Competitor Content Aggregation

Google Search: The expanded queries are used to perform Google searches, retrieving top competitor URLs.
Content Scraping: The workflow then fetches the content from each competitor page for further analysis. This step aggregates a comparative dataset of top-ranking structures and keywords.

5. Contextual Preparation

Competitor Context Formatting: The competitor content is formatted and consolidated into a prompt (“TOP COMPETITOR PAGES”) for easy downstream use.
Input & Competitor Packaging: The workflow prepares a comprehensive prompt for the AI, including:
- The user’s current page content.
- The competitor context.

6. SEO Gap Analysis & Suggestion Generation

AI-Powered Analysis: An OpenAI language model (o1-preview) receives the prepared prompt, which instructs it to:
- Compare the user’s page structure with competitors'.
- Highlight what competitors are doing better.
- Suggest an improved heading structure.
- Focus on keyword usage in headings.
- Recommend 5–10 long-tail keywords for each paragraph.
- Provide specific, practical advice (not generic SEO theory).

7. Reporting & Results Display

SEO Report Formatting: The AI output is wrapped in a structured SEO Report format for clarity.
Results Presentation: The report and supporting documents are presented to the user through chat output widgets, and relevant documents are shown via a Document Widget for further review.

Additional Output Features

Relevant Search Queries: The workflow also presents the alternative search queries generated, helping the user understand what terms are most relevant for their page and what competitors are ranking for.
Competitor Content as Reference: The competitor pages’ content is made available for deeper inspection, offering transparency and supporting manual review if desired.

Why This Workflow Is Useful for Scaling and Automation

Automates Manual SEO Research: Traditionally, analyzing competitors and adjusting a page for SEO involves tedious manual work. This workflow automates the entire pipeline — from query expansion, Google searching, scraping, analysis, to reporting.
Ensures Consistency: By using AI for query expansion and comparative analysis, the process avoids human oversight and maintains a consistent, repeatable standard.
Saves Time and Resources: Multiple steps that would normally require various tools (search, scraping, prompt engineering, comparative analysis) are stitched together, drastically reducing turnaround time.
Supports Large-Scale Audits: The modular, automated nature means it can be reused for many URLs, making it ideal for agencies, in-house SEO teams, or large websites with hundreds of pages.
Actionable and Detailed Recommendations: By focusing on headings, specific competitor actions, and long-tail keywords, the output is tailored for direct implementation — not just theory.
Transparency and Customization: The user can review both the suggested queries and the competitor content, ensuring the process is not a “black box” and allowing for further customization or manual intervention.

Summary Table

Step	Description
User Input	User submits their URL.
Content Extraction	Extract headings and structure from input page.
Query Expansion	AI generates search queries to find competitors, excluding the user’s own site.
Competitor Retrieval	Google search + scraping to get competing page content.
Context Formatting	Competitor data is formatted for AI consumption.
AI Analysis	LLM compares structures, suggests improvements, and generates keyword ideas.
Reporting	Structured SEO report and reference materials are presented to the user.
Supporting Output	Shows relevant search queries and competitor page content as reference.

Who Can Benefit

SEO professionals seeking to scale site audits and page optimization.
Content strategists wanting actionable, data-driven recommendations.
Agencies handling multiple client sites and needing efficient, repeatable processes.
Webmasters aiming to improve rankings with less manual effort.

This workflow bridges the gap between manual SEO research and scalable, AI-assisted content improvement — making site audits more actionable, efficient, and transparent.

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