Cross-Encoder Ranking Models: Jointly encodes query and content for enhanced relevance comparison

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This project applies Cross-Encoder Ranking Models to evaluate and rank the semantic relevance of webpage content with respect to specific SEO-focused queries. The core innovation lies in the joint encoding of query and content snippet, which allows the model to assess fine-grained contextual interactions and deliver a precise relevance score for each block of text.

Rather than analyzing an entire page as a single unit, the system breaks down the page into smaller elements—such as paragraphs, headings, and list items—and evaluates each one individually. These elements are then ranked by their semantic alignment with the given query, showcasing which specific sections are best suited to appear in search engine features such as featured snippets, People Also Ask panels, and organic rankings.

The outcome is a ranked list of high-relevance snippets per query, supported by numerical scores derived from a robust transformer-based architecture. This enables more focused and impactful SEO optimization at the snippet level, where visibility and relevance matter most.

Project Purpose

The goal of this project is to bring precision and ranking intelligence into the process of content evaluation for SEO. Traditional SEO tools often evaluate pages based on keyword density or surface-level heuristics. This project introduces a more advanced and semantically aware method by using Cross-Encoder Ranking Models to:

• Jointly analyze search intent and content in a single pass.
• Generate a relevance score for each snippet that reflects how well it answers the query.
• Rank content blocks by relevance, guiding where to focus optimization efforts.
• Help identify top-performing sections and content gaps within individual pages.
• Support SERP-oriented editing by showing what portions of the page are most eligible for inclusion in high-visibility search features.

By highlighting the most semantically relevant snippets and enabling content comparisons through ranking, the project supports more informed decisions around content improvement, competitive positioning, and visibility enhancement in search engines. The ranking-centric approach offers a modern, query-aligned optimization strategy, rooted in the same principles that underpin real-world search engine ranking algorithms.

What is the significance of this project in the field of SEO?

This project introduces a precision-driven approach to SEO optimization by leveraging Cross-Encoder Ranking Models that simulate how modern search engines evaluate content. Instead of relying on basic keyword matching, the system understands semantic meaning and evaluates how well content directly answers user queries.

By ranking different content blocks based on their relevance to specific search intents, this method reflects how Google and other search engines assess and prioritize content for featured snippets, rich results, and rankings. It brings SEO practices closer to the actual algorithms powering modern search engines, resulting in more targeted and effective optimizations.

How does this project help improve on-page SEO strategies?

This solution enables fine-grained analysis at the snippet level. It breaks down a web page into individual content segments—such as paragraphs, headings, and list items—and evaluates each segment’s semantic alignment with a specific search query.

This helps identify:

• Which sections of a page are most relevant and could be optimized further.
• Which content blocks are underperforming or off-topic for the intended query.

Opportunities to adjust or restructure content to better target search features like People Also Ask, Featured Snippets, and Organic Listings.

In short, it supports data-backed content tuning for higher on-page relevance.

How does this approach benefit content optimization for user intent?

The use of Cross-Encoders ensures that content is not just keyword-rich but also semantically aligned with the intent behind the query. For instance, if a user searches for “benefits of HTTP headers,” the model will prefer content that directly discusses advantages or implications—rather than just repeating the phrase.

This approach:

• Surfaces the most intent-satisfying answers from within a page.
• Helps restructure content to better match informational, navigational, or transactional intent.
• Improves the likelihood of inclusion in AI-powered search features that prioritize meaning over keywords.

What are the practical benefits for website owners or digital marketers?

Website owners and marketers can:

• Identify and elevate high-performing content blocks to more prominent positions on the page.
• Create targeted SERP-ready snippets by leveraging high-scoring sections.
• Uncover content gaps or poorly aligned passages that need improvement.
• Compare multiple pages or sections to see which performs best for a given search intent.

This level of insight is particularly valuable for optimizing landing pages, improving topical authority, and enhancing content visibility across competitive SERPs.

How is this different from traditional content analysis or SEO scoring tools?

Traditional tools often rely on:

• Keyword density
• Readability scores
• Backlink metrics
• Generalized content grading

In contrast, this system uses deep semantic comparison between the query and content, assessing meaning, context, and relevance using transformer-based language models. It doesn’t just evaluate if the keyword is present, but whether the content meaningfully answers or addresses the searcher’s needs.

This makes it significantly more aligned with how Google Search, Bing AI, or AI-generated snippets evaluate content today.

sentence_transformers

Purpose: This library provides state-of-the-art transformer-based models for semantic similarity, classification, and ranking tasks.

Role in Project: The core component of the project is the CrossEncoder from this library. It allows simultaneous encoding of a query and content segment to compute a context-aware relevance score, which forms the basis for ranking different sections of the page.

requests

Purpose: Used to make HTTP requests to access web content.

Role in Project: This library fetches the raw HTML content from the provided URLs, enabling further parsing and content extraction.

BeautifulSoup (from bs4)

Purpose: A widely used HTML/XML parser for web scraping.

Role in Project: Parses the fetched HTML content to extract structured elements such as paragraphs (<p>), headings (<h1>, <h2>, <h3>), and list items (<li>), while automatically ignoring or removing non-content tags such as <script>, <style>, and <a>.

re (Regular Expressions)

Purpose: Built-in Python module for working with regular expressions.

Role in Project: Assists in cleaning and filtering text by removing unwanted characters, line breaks, extra spaces, and other noise from the extracted content.

nltk (Natural Language Toolkit)

Purpose: A powerful library for text processing and natural language tasks.

Role in Project: Used specifically for sentence tokenization. After extracting raw text, nltk helps break it down into clean, grammatically coherent sentence blocks for fine-grained analysis and ranking.

The following components from nltk are used:

• punkt: A pre-trained sentence tokenizer model.
• sent_tokenize: A utility that applies punkt to segment text into individual sentences.

Function: extract_structured_blocks(url)

This function is designed to safely fetch and process web content from a given URL. It extracts structured content blocks—such as paragraphs, headers, and list items—while filtering out irrelevant or non-visible HTML elements like scripts, navigation bars, or footers. The output is a clean, structured list of content blocks, each identified by its tag (e.g., <p>), the textual content, and its relative position on the page.

The function also includes robust error handling to deal with broken links, timeouts, or malformed HTML, making it reliable for real-world usage.

Detailed Explanation:

response = requests.get(url, timeout=15)

• This line makes an HTTP GET request to the specified URL.
• The timeout=15 ensures the request doesn’t hang indefinitely—if the server doesn’t respond within 15 seconds, it raises a timeout error.

response.raise_for_status()

• Checks if the HTTP response indicates success (status code 200).
• If there’s an error (e.g., 404 Not Found or 500 Internal Server Error), it raises an exception to skip processing that URL.

soup = BeautifulSoup(response.content, ‘html.parser’, from_encoding=”utf-8″)

• Converts the raw HTML content into a BeautifulSoup object, which allows structured parsing of the page.
• Specifies UTF-8 encoding to correctly interpret a wide range of international characters.

for tag in soup([‘script’, ‘style’, ‘noscript’, ‘iframe’, ‘a’, ‘nav’, ‘footer’, ‘header’]): tag.decompose()

• This loop removes non-content and decorative HTML elements that do not contribute meaningful content for SEO analysis.
• Elements like <script>, <style>, <nav>, and <footer> are stripped to ensure only user-visible and SEO-relevant content remains.

content_tags = soup.find_all([‘p’, ‘h1’, ‘h2’, ‘h3’, ‘li’])

·         Extracts only the relevant structured tags:

• <p> for paragraphs
• <h1> to <h3> for main and sub-headings
• <li> for bullet points and list content

Function : preprocess_blocks_to_snippets(blocks, min_len=40, max_len=300)

This function processes the raw content blocks extracted from a webpage by splitting them into individual sentence-level snippets. It cleans each sentence and retains only those within a specified length range. This ensures that the resulting snippets are semantically meaningful, concise, and appropriate for input into the Cross-Encoder ranking model.

The function improves content granularity, allowing for fine-grained relevance scoring between query and content.

Detailed Explanation:

sentences = sent_tokenize(block[‘text’])

• Uses NLTK’s sentence tokenizer to split the block’s text into individual grammatically coherent sentences.
• Sentence-level granularity improves the precision of semantic matching between content and query.

cleaned = re.sub(r’\s+’, ‘ ‘, sent).strip()

• Removes excessive or irregular whitespace, line breaks, and tabs by replacing all types of whitespace with a single space.
• Applies .strip() to eliminate any leading or trailing spaces.

if min_len < len(cleaned) < max_len:

• Filters out sentences that are either too short (e.g., fragments or boilerplate) or too long (e.g., complex, unstructured content).
• Default values: min_len = 40 and max_len = 300characters, which are empirically suitable for ensuring relevance and readability.

snippets.append({ ‘text’: cleaned, ‘tag’: block[‘tag’], ‘position_index’: block[‘position_index’] })

• Stores each cleaned sentence (that passes the length filter) in a dictionary format.
• Maintains the original HTML tag and relative position of the block from which the sentence came—important for tracking context and position later during ranking.

This function is a critical preprocessing step that ensures the ranking model receives high-quality, normalized input while preserving structural metadata.

Function : load_model(model_name=’cross-encoder/ms-marco-MiniLM-L6-v2′)

This function is responsible for initializing and loading the Cross-Encoder model used to compute semantic relevance scores between search queries and content snippets. By default, it loads a well-balanced, lightweight model (MiniLM-L6-v2) trained on the MS MARCO dataset, making it suitable for real-time or large-scale SEO tasks.

Detailed Explanation:

Defines a function to load a pre-trained Cross-Encoder model.

Accepts an optional argument model_name, allowing flexibility to switch to other model variants if required.

model = CrossEncoder(model_name)

• Loads the specified model using the CrossEncoder class from the sentence_transformers library.
• This class expects paired inputs (e.g., query and content) and produces a single scalar score representing semantic relevance.

return model

• Returns the fully initialized Cross-Encoder model, ready to use for scoring query-snippet pairs.

Model Overview

The project uses a Cross-Encoder model — specifically, the pre-trained model called ms-marco-MiniLM-L6-v2. This model is part of the Sentence-Transformers family and is specially designed for relevance scoring between two text inputs: a search query and a content snippet.

Unlike models that encode texts separately, the Cross-Encoder model combines both inputs and evaluates them jointly, allowing it to capture detailed semantic interactions between them. This results in more accurate relevance judgments, which is essential for ranking tasks.

How It Works (Joint Encoding)

At the core of the Cross-Encoder’s mechanism is a joint encoding process. Here’s how it works conceptually:

• Both the query and a content snippet are passed together as a pair to the model.
• The model processes them simultaneously, allowing it to understand the direct relationships between words and phrases across both texts.
• t outputs a single relevance score, which reflects how well the content snippet answers or relates to the query.

This design allows the model to detect more subtle, context-aware relevance than simpler models that evaluate each text separately.

Why It’s Used for This Project

The goal of this project is to rank web content based on how relevant it is to a user’s search query. The Cross-Encoder model is ideal for this task because:

• It provides fine-grained semantic relevance scoring.
• It helps rank content snippets from a webpage in descending order of relevance.
• It supports high-precision filtering, which is critical for understanding which parts of a page answer specific user intents.

By using this model, the system can simulate how a search engine evaluates content relevance, making it directly applicable to real-world SEO analysis.

Why This Specific Model Was Chosen (MiniLM-L6-v2)

The model cross-encoder/ms-marco-MiniLM-L6-v2 was selected for the following reasons:

·         Trained on MS MARCO: The model is fine-tuned on a large dataset specifically built for question-answering and retrieval — making it highly relevant to search intent tasks.

·         Lightweight and Fast: It uses the MiniLM architecture, offering a strong balance between speed and accuracy — suitable for both small-scale and large-scale webpage evaluation.

·         Effective for Ranking: Despite being compact, it demonstrates excellent performance on ranking benchmarks, making it reliable for client-facing SEO applications.

How the Model Helps in SEO-Related Domains

This Cross-Encoder model is particularly impactful for several SEO use cases:

·         Content Relevance Auditing: Helps identify which parts of a webpage directly support specific user queries or search intents. This is critical for optimizing on-page content.

·         SERP Snippet Optimization: By finding the highest-scoring content fragments, the model helps select or recommend ideal meta descriptions and featured snippets.

·         Query-to-Page Matching: Allows SEO professionals to test how well a page aligns with target keywords or questions — revealing content gaps or mismatches.

·         Improving Information Architecture: Insights from relevance scores can be used to reorganize or highlight content, ensuring important sections are easily crawlable and discoverable.

·         Competitor Analysis: When applied to multiple URLs, this model enables side-by-side relevance comparison between a site and its competitors for the same queries.

Code Summary

This part of the project prepares query–snippet pairs as input for the Cross-Encoder. The model expects paired inputs (a query and a candidate text), and this block ensures that the data is formatted accordingly. It processes each content snippet extracted from a webpage and aligns it with the search query to allow semantic relevance scoring.

Breakdown

pairs = [(query, snippet[‘text’]) for snippet in snippets]

·         This line creates a list of tuples, where each tuple is a pair:

• The first item in the pair is the search query.
• The second item is a snippet of content extracted and preprocessed from the web page.

·         Each pair is designed to be passed into the Cross-Encoder model, which will compute how semantically relevant the snippet is to the query.

Summary

This section passes the previously prepared query–snippet pairs to the Cross-Encoder model to compute semantic relevance scores for each pair. These scores reflect how well each content snippet answers or relates to the search query.

Code Breakdown

scores = model.predict(pairs)

·         This line uses the predict() method of the loaded Cross-Encoder model.

·         Each pair (query, snippet) is evaluated jointly by the model.

·         The model outputs a numeric score for each pair:

• Higher scores indicate stronger semantic relevance.
• These scores are continuous, and while typically they range from roughly -10 to +10, this can vary by model.

Code Breakdown

Attach scores to snippets

for snippet, score in zip(snippets, scores): snippet[‘score’] = float(score)

• Iterates over each snippet and its corresponding score (from model output).
• Adds a new key ‘score’ to each snippet dictionary.
• The float(score) ensures numerical consistency for sorting and future use.

Rank snippets by relevance

ranked_snippets = sorted(snippets, key=lambda x: x[‘score’], reverse=True)

• Uses Python’s sorted() function with a custom key (the score) to order snippets.
• reverse=True ensures highest scoring (most relevant) snippets appear first.
• ranked_snippets is a list of dictionaries, now ordered by semantic relevance.

Function def display_top_snippets(ranked, top_k=3):

This function displays the top-k ranked snippets based on their semantic relevance to a given query. It helps summarize the best-matching content blocks from the webpage in an easily readable format. Useful for reporting and validation.

Breakdown

print(f”\nTop {top_k} Snippet(s) Relevant to Query:\n”)

• Prints a heading for the output, stating how many top snippets will be shown.
• top_k is the number of top-ranked results to display (default is 3).

for i, item in enumerate(ranked[:top_k], start=1):

• Iterates through the first top_k items from the ranked list.
• Each item is a dictionary containing the snippet’s text, score, HTML tag, and position.
• enumerate(…, start=1) ensures the ranks start from 1.

Result Interpretation and Relevance Discussion

Understanding the Scoring System

Each snippet of content from the web page is evaluated based on how relevant it is to a specific search query. This relevance is quantified by a numerical score, where higher values indicate stronger relevance.

The scoring values are not normalized between 0 and 1. Instead, they follow a broader range (commonly between -10 and +10, though they may fall outside this in some cases). These scores are generated by a Cross-Encoder ranking model, which jointly considers both the query and the content while assigning relevance scores.

• Higher scores -> More semantically aligned with the search query.
• Lower scores -> Less directly connected to the query’s intent.
• The scores are relative — they help rank the available content snippets from most to least relevant.

Query Used for Evaluation

Query: benefits of http headers in url handling

The objective was to identify which parts of the webpage most effectively address the benefits of using HTTP headers, especially in the context of URL management and SEO.

Top 3 Ranked Snippets

Rank 1

• Relevance Score: 6.0855
• HTML Tag: <p>
• Snippet Position on Page: 102
• Extracted Snippet: “Utilizing HTTP headers correctly offers several SEO benefits:”

Why this matters: This snippet received the highest score because it directly answers the question being asked. It clearly states that HTTP headers offer SEO advantages — aligning precisely with the query. This type of sentence can be valuable in featured snippet optimizations or content structuring for search intent.

Rank 2

• Relevance Score: 5.9714
• HTML Tag: <p>
• Snippet Position: 3
• Extracted Snippet: “Proper implementation of HTTP headers can improve user experience, search engine rankings, and website security.”

Why this matters: This snippet elaborates on the specific benefits of HTTP headers, adding dimensions like security and user experience. It helps reinforce the importance of HTTP headers beyond just SEO, highlighting the broader advantages, which may appeal to both technical and strategic content creators.

Rank 3

• Relevance Score: 5.8119
• HTML Tag: <p>
• Snippet Position: 3
• Extracted Snippet: “In the constantly evolving field of technical SEO, HTTP headers serve as a powerful tool to optimize website performance, enhance crawl efficiency, and regulate indexing.”

Why this matters: Here, the snippet emphasizes the technical SEO value of HTTP headers — pointing to backend improvements such as crawl optimization and indexing control. These are key elements for improving how search engines interpret and prioritize web content.

What These Rankings Show

• The Cross-Encoder model is effectively identifying which parts of a webpage are most useful for a specific query.
• The ranking system provides a clear way to prioritize content that addresses real search intent — an essential part of SEO and content strategy.
• For content teams, this helps understand which sentences or paragraphs should be retained, highlighted, or potentially surfaced in snippets or metadata.

Analysis & Discussion

Understanding the Scoring Framework

This solution uses a Cross-Encoder model to measure how well individual content snippets align with specific search queries. Each piece of content is scored based on its semantic relevance to the query — meaning it evaluates how closely the snippet answers or relates to what users are likely searching for.

• The relevance score is a numerical value — generally ranging between -10 to +10, although this may vary based on content and context.
• Higher scores indicate a stronger match between the search intent and the snippet.
• These scores allow us to rank content within each page and compare entire pages across multiple URLs based on how well they satisfy a given query.

This system is relative, meaning it compares how relevant each snippet is in the context of all available content for a query. A high score doesn’t just mean “good content” — it means the content is highly relevant to the specific query being analyzed.

How to Read the Output

Each query returns:

• A ranked list of snippets, showing the most semantically relevant content blocks on the page.
• A page-level score, calculated by averaging top-scoring snippets, giving a general performance indicator for the full URL in relation to the query.

Scoring Example

Let’s consider a few generalized scoring scenarios to show how this helps interpret performance:

Example 1: High-Relevance Match

• Score: 8.95
• Snippet Location: Paragraph near the bottom of the page
• Interpretation: This snippet directly answers the query and uses terminology that reflects searcher language and expectations. It’s highly optimized for the intended intent.
• Actionable Insight: Highlight or move this section up the page. Consider expanding around it.

Example 2: Mid-Relevance Match

• Score: 5.97
• Snippet Location: Introduction paragraph
• Interpretation: This content supports the query indirectly, perhaps offering general context or a lead-in, but lacks specific detail or terminology.
• Actionable Insight: Consider rewriting to be more focused or supporting it with examples or definitions.

Example 3: Irrelevant Snippet

• Score: -6.77
• Snippet Location: In a different blog section or off-topic part of the article
• Interpretation: Likely unrelated to the query, despite being on the same page.
• Actionable Insight: Either leave untouched (if the section is still valuable for another intent) or move/restructure if it’s causing content dilution.

Page-Level Impact

• If a page has mostly negative scores for a given query, its average page score will be low (e.g., -7.35), signaling poor relevance.
• Conversely, a page score near +6 or higher reflects strong alignment and indicates high likelihood of ranking well if SEO fundamentals (title tags, crawlability, etc.) are also in place.

How This Helps in SEO-Driven Content Evaluation

In any SEO strategy, one of the key challenges is identifying which parts of a web page actually address user intent. Traditional audits often rely on structural or keyword-level analysis. This approach goes deeper by measuring semantic alignment, answering questions like:

• Which exact snippets from the page are most likely to satisfy the user’s query?
• Are there parts of the content that are underperforming in relevance and need revision or replacement?
• How do different URLs compare in their ability to answer the same question or intent?

Key Insights for Content and SEO Teams

Based on the query-specific analysis:

Content is Ranked Internally: Each webpage is broken down into structured blocks (like <p>, <h2>, or <li> tags), and the most relevant blocks are identified. This helps pinpoint which sentences or sections are working and which aren’t.

Page-Level Scoring: Beyond snippet-level insights, each URL is assigned a Page Score — derived from the average of its top relevant snippets. This helps answer:

• Which pages perform best for a given query?
• Which ones are off-topic or need re-alignment with search intent?

Multiple Queries Supported: The same evaluation process is applied across various search intents (queries), providing a multi-angle audit of existing content.

Data-Backed Optimization: These insights allow teams to:

• Prioritize updates to specific parts of content
• Create query-focused content sections
• Improve content targeting for featured snippets or knowledge panels
• Align blog topics more precisely with informational search trends

Strategic Value

Actionable Content Intelligence: Instead of guessing which parts of a page work, this analysis reveals the exact blocks driving SEO relevance.

Query-Intent Alignment: Ensures that published content is aligned with how people are actually searching — critical for ranking in modern SERPs.

Comparative Performance: Helps in comparing multiple URLs (even across competitors, if needed) to determine which domains dominate specific topics and why.

I’ve received the top snippets and relevance scores for my query. What’s my next move?

Start by reviewing the top 2–3 snippets with the highest scores. These are the most semantically aligned with the target query. These snippets are likely to perform best in terms of search relevance, user engagement, and even featured snippet opportunities.

Action:

• Keep these snippets unchanged or promote them higher on the page.
• Consider using them as meta descriptions, headers, or content previews.

Some of my page snippets have low or negative scores. What does that mean, and what should I do?

Low or negative scores indicate the snippet does not align well with the query’s meaning. This doesn’t mean the content is bad—it may just be off-topic for the target search intent.

Action:

• Reevaluate the messaging in these sections.
• Consider rewriting or repositioning them to better match the query’s theme.
• If they don’t serve the query at all, they may be better moved to a different page or removed.

Multiple snippets from the same page are scoring well. What does this tell me?

This is a good sign. It means the page is semantically rich and well-optimized for that query. Multiple strong snippets indicate broader topical coverage and more chances to rank well.

Action:

• Reinforce the focus on this topic within the page.
• Strengthen internal linking to this URL using anchor text that reflects the high-performing query.
• Consider sharing or promoting the page around that search topic.

What does the “page score” mean?

The page score is the average of the top-k snippet scores for that page, giving a summary measure of how well the entire page aligns with the query. It’s a fast way to assess overall query relevance.

How should I use the “page score” to prioritize work?

The page score is an average of the top snippet scores from each page. It provides a quick way to prioritize which pages are performing best per query.

Action:

• Focus SEO efforts on improving or leveraging high-scoring pages.
• Re-optimize or rework pages with mid-to-low scores.
• For low scores across all snippets, reassess whether the page should target that query at all.

How do we use these insights to improve SEO?

This system helps:

• Identify top-performing passages for highlighting or featuring.
• Spot weak or irrelevant sections that dilute keyword focus.
• Refine internal linking strategies based on query alignment.
• Validate whether a page is targeting the right user intent.

How can I use this output to improve my metadata and featured snippet eligibility?

High-scoring snippets often contain concise, informative sentences. These are ideal for:

• Meta descriptions (for better CTR).
• Structured markup like FAQ or HowTo schema.
• Voice search and zero-click results.

Action:

• Use top snippets as summaries in your meta tags.
• Create dedicated sections (like Q&A) using this content.
• Mark them up with schema.org structured data to increase SERP visibility.

How is this different from traditional keyword matching?

Traditional SEO tools often match exact keywords. This system uses semantic understanding, meaning it can detect relevance even when exact words aren’t used. It’s built to understand meaning, not just matching phrases.

Final Thoughts

This project demonstrates a practical and scalable approach to measuring how well web content aligns with real-world search intent using state-of-the-art semantic relevance modeling. By leveraging a Cross-Encoder architecture trained specifically for information retrieval, the system provides a nuanced scoring mechanism that evaluates the alignment between user queries and sentence-level content snippets across multiple web pages.

Unlike traditional keyword density checks or basic metadata audits, this methodology dives into the semantic structure of the content, assessing not just if a topic is mentioned, but how meaningfully it is addressed. The result is a prioritized, score-driven view of which content elements are most likely to satisfy user intent and perform well in organic search visibility.

Clients gain a clear, interpretable roadmap:

• Which snippets deserve promotion
• Which need improvement
• Which pages are semantically strong for a given query
• And where optimization efforts should be focused

As content marketing and SEO continue to move toward intent-based relevance rather than keyword matching, such AI-powered content intelligence tools will become central to staying competitive in search rankings.

This framework can easily be expanded to handle more pages, larger query sets, and deeper site audits. It is designed to evolve with your SEO strategy, offering both flexibility and precision.

The insights are actionable. The process is repeatable. And the value is measurable.

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Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.

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