DEV Community: Ranjan Dailata

A Vectorless RAG System for Smarter Document Intelligence

Ranjan Dailata — Sun, 05 Apr 2026 04:20:30 +0000

Modern AI applications rely heavily on Retrieval-Augmented Generation (RAG) to analyze documents and answer questions. Most implementations follow a familiar approach of

Split documents into chunks
Generate embeddings
Store them in a vector database
Retrieve the most similar chunks

While this architecture works well for small documents, it begins to break down when dealing with long, complex documents such as research papers, legal contracts, financial reports, or technical manuals.

Important context gets fragmented
Sections lose their relationships
Retrieval becomes noisy

To solve this problem, PageIndex introduces a fundamentally different approach to document retrieval.

Instead of relying on vector similarity search, PageIndex transforms documents into a hierarchical tree structure and allows large language models to reason over that structure directly.

The result is a vectorless, reasoning-based RAG system that more closely resembles how human experts read and navigate documents.

This article explores how PageIndex works and why it represents a new direction for document intelligence systems.

The Problem with Traditional RAG

Most RAG systems follow this pipeline:

Document
   ↓
Chunk text
   ↓
Create embeddings
   ↓
Store in vector database
   ↓
Retrieve similar chunks
   ↓
Send to LLM

This method introduces several problems.

Loss of Structure

Documents are inherently hierarchical.

Document
 ├ Chapter
 │   ├ Section
 │   │   ├ Subsection
 │   │   └ Subsection
 │   └ Section

Chunking destroys this structure by breaking documents into arbitrary pieces.

Context Fragmentation

Important ideas often span multiple paragraphs or sections.

Chunk-based retrieval may return only part of the information needed to answer a question.

Retrieval Noise

Vector similarity can retrieve text that is semantically similar but contextually incorrect.

For example, a query about clinical trial results might retrieve text from the introduction simply because the terminology overlaps.

Infrastructure Complexity

Traditional RAG pipelines require additional infrastructure:

Vector databases
Embedding pipelines
Chunking strategies
Similarity tuning

PageIndex removes much of this complexity.

Introducing PageIndex

PageIndex is a vectorless, reasoning-based retrieval framework.

Instead of embedding chunks into a vector database, PageIndex converts documents into a tree-structured index.

Each node represents a section of the document.

Document
├ Introduction
│  ├ Background
│  └ Objectives
│
├ Methods
│  ├ Study Design
│  └ Participants
│
└ Results
   ├ Efficacy
   └ Safety

Each node contains:

Section title
Sentence boundaries
Semantic summary
Parent-child relationships

This structure preserves the original organization of the document.

Rather than searching through fragments, the system can navigate the document hierarchy intelligently.

How PageIndex Works

PageIndex consists of several coordinated components that transform documents into a navigable knowledge structure.

The architecture includes:

PageIndex API
Indexer
Retriever
Reasoning module
LLM interface
JSON tree storage

Together, these components create a reasoning-based retrieval pipeline.

Step 1: Document Indexing

The Indexer converts the raw document into a hierarchical structure.

An LLM analyzes the document and identifies sections and subsections.

Example output:

Document
 ├ 1. Introduction
 │   ├ 1.1 Background
 │   └ 1.2 Objectives
 │
 ├ 2. Methods
 │   ├ 2.1 Study Design
 │   └ 2.2 Participants

Each section is stored with sentence-level indices so the system can retrieve the exact text later.

The tree is cached as JSON for reuse.

Step 2: Structure-Aware Retrieval

Instead of performing vector similarity search, the Retriever allows the LLM to reason over the document tree.

The system collects all nodes and sends them to the model with their summaries and hierarchical paths.

Example prompt conceptually looks like this:

Question:
"What were the safety outcomes?"

Available sections:
- Introduction
- Methods
- Results > Safety
- Results > Efficacy

The LLM selects the most relevant nodes.

This process is traceable and explainable, since the system can show exactly which sections were chosen.

Step 3: Context-Aware Reasoning

Once the relevant sections are identified, the system extracts the corresponding text and sends it to the reasoning module.

The LLM then generates the final answer using only the selected context.

Question
+ Retrieved Sections
   ↓
LLM Reasoning
   ↓
Answer

Because the retrieval step already narrowed down the context, the model can focus on the most relevant information.

Why PageIndex Is Different

PageIndex challenges several assumptions in traditional RAG systems.

1. No Vector Database

PageIndex does not require embeddings or similarity search.

This reduces infrastructure complexity.

2. No Chunking

Documents remain intact within their hierarchical structure.

This preserves meaning and context.

3. Reasoning-Based Retrieval

Instead of matching vectors, retrieval is performed by an LLM that evaluates document sections semantically.

4. Explainable Retrieval

Because the system selects explicit nodes from the document tree, the retrieval process is transparent.

Users can trace exactly how the answer was produced.

Example Workflow

A typical PageIndex workflow looks like this:

Upload Document
      ↓
Tree Index Creation
      ↓
User Question
      ↓
LLM selects relevant nodes
      ↓
Context extraction
      ↓
Reasoned answer

The system behaves much like a human expert scanning a document:

Identify relevant sections
Read those sections carefully
Extract insights

Where PageIndex Excels

PageIndex performs particularly well for long and structured documents, including:

Research papers
Financial reports
Clinical trial documents
Legal contracts
Technical documentation

In these domains, section hierarchy carries important meaning that chunk-based systems often lose.

Why This Matters for AI Systems

As organizations accumulate massive collections of documents, the ability to analyze them effectively becomes increasingly important.

Vector-based retrieval was an important first step, but it is not always the best approach for structured knowledge.

PageIndex demonstrates a different paradigm:

Retrieval through reasoning rather than similarity search.
Preserving document structure and allowing LLMs to navigate that structure intelligently, PageIndex enables more accurate and explainable document analysis.

Inspiration

PageIndex is an open framework designed to simplify and improve document intelligence systems.

https://pageindex.ai/
https://docs.pageindex.ai/

Conclusion

AI systems are rapidly evolving from simple chat interfaces into powerful research tools capable of analyzing large bodies of information.

The future of document intelligence may not lie in bigger vector databases, but in smarter ways of representing and reasoning over knowledge.

By combining hierarchical indexing with LLM reasoning, PageIndex offers a compelling alternative to traditional RAG pipelines, the key reason being it is simpler, more explainable, and closer to how humans actually read documents.

Building SEO Automation in .NET with SERankingSharp

Ranjan Dailata — Wed, 11 Feb 2026 12:41:20 +0000

Introducing

If you’re building SEO automation tools in .NET, you’re going to love this new project named "SERankingSharp" - A strongly-typed, async-first C# library that wraps the SE Ranking Data API with clear models and comprehensive coverage.

Whether you need to pull competitor insights, analyze backlink profiles, audit site health, or research keywords, this SDK gives you everything you need in an idiomatic .NET package.

What is SERankingSharp?

SERankingSharp is a production-ready C# SDK that gives .NET developers easy, type-safe access to the SE Ranking API. It ships with:

Support for all ~85 API endpoints
Modular design with dedicated clients (Account, DomainAnalysis, Backlinks, SERP, & more)
Async/Await patterns throughout
Strongly typed request & response models
Built-in error handling & custom exceptions
JSON serialization using System.Text.Json
Supports .NET 8.0 and up

All of this is wrapped up in a clean, intuitive API that feels like a natural extension of modern .NET development.

Why You’ll Love It

Here’s what makes SERankingSharp a great choice for your next SEO project:

Complete API Coverage

Unlike minimal wrappers, this SDK implements every core endpoint of the SE Ranking Data API from account info to SERP tracking and AI search metrics.

Async First

Built with HttpClient and async patterns, it plays nicely with modern .NET apps, web APIs, console tools, or background services.

Strong Typing

Every response and request is strongly typed, which reduces runtime bugs and improves IntelliSense support in your editor.

Modular Architecture

Rather than one huge class, the SDK splits functionality into logical modules such as:

Account – Subscription & usage
Domain Analysis – Competitor insights & keyword trends
Keyword Research – Longtail & related keywords
Backlinks – Link profile metrics
Website Audit – Technical SEO checks
SERP – Search result tracking
AI Search – Visibility in AI-driven search engines

Quick Start

Here’s how to get rolling using the SDK:

Clone the repo

   git clone https://github.com/ranjancse26/SERankingSharp.git

Build & reference the project

   dotnet add reference path/to/SERankingSharp/SERankingSharp.csproj

Or build the DLL and reference it directly.

Initialize and call the API

   using SERankingSharp;
   using System.Threading.Tasks;

   public class Program
   {
       public static async Task Main()
       {
           var client = new SERankingClient("YOUR_API_KEY");

           var balance = await client.Account.GetCreditBalanceAsync();
           Console.WriteLine($"Balance: {balance.Balance} / {balance.TotalLimit}");
       }
   }

Just make sure you store your API key securely! (GitHub)

Authentication Made Easy

The SDK automatically adds your SE Ranking API key as a Bearer token in headers no manual header management needed.

Use Cases

Here are some powerful things you can build:

Crawl competitive domains and visualize keyword overlap
Generate keyword lists for targeted SEO campaigns
Monitor backlink growth and lost links
Track SERP positions over time
Surface AI Search brand insights

All with just a few lines of C# code.

Contributing & Building

Want to hack on the SDK or add more features? The project welcomes contributions:

Follow C# conventions
Add XML docs to public methods
Include usage examples
Keep async patterns consistent
Update README for new endpoints or modules

Check the GitHub repo for contribution guidelines.

Conclusion

If you’re working in SEO analytics or integrating SEO data into .NET apps, SERankingSharp is now one of the strongest C# options out there. With full coverage, clean architecture, and async support, it takes a lot of the complexity out of working with SEO APIs.

Explore the project on GitHub: https://github.com/ranjancse26/SERankingSharp (GitHub)

Mastering Keyword Research with SE Ranking Keyword Research APIs

Ranjan Dailata — Sun, 18 Jan 2026 10:46:50 +0000

Introduction

Keyword research is still the foundation of SEO but the way you can do it has evolved. Modern teams don't just look for a few keywords; they analyze thousands at scale, enrich them with performance data, and continuously discover new opportunities.

That's exactly what SE Ranking Keyword Research APIs are built for.

In this post, you will see a break down how these endpoints help you move from raw keyword lists to actionable, data-driven SEO strategies.

What Is SE Ranking Keyword Research?

SE Ranking Keyword Research is a collection of APIs designed for large-scale keyword analysis and keyword discovery.

With these endpoints, you can:

Enrich up to 5,000 keywords at once with performance metrics
Discover new keyword opportunities from a single seed term
Build content strategies around questions and long-tail queries
Automate keyword research workflows for dashboards, tools, or internal SEO systems

The APIs are organized into two main categories:

Bulk Keyword Metrics
Keyword Discovery

Bulk Keyword Metrics: Analyze at Scale

Export Keywords Metrics

This endpoint is built for bulk analysis and data enrichment.

You can submit up to 5,000 keywords in a single request and receive detailed metrics for each keyword, including:

Search volume – Average monthly searches
CPC – Cost-per-click for paid campaigns
Competition score – Advertiser competitiveness
Keyword difficulty – Estimated ranking difficulty
Historical trends – How search demand changes over time

Common Use Cases

Enrich keyword lists from external tools
Prioritize keywords by difficulty vs. volume
Feed SEO dashboards or BI tools
Score keywords for content or PPC planning

Keyword Discovery: Find New Opportunities

Once you're analyzed what you have, the next step is expansion. Keyword Discovery endpoints help you uncover what you should be targeting.

Get Similar Keywords

This endpoint finds keywords that are semantically similar to your seed term.

It includes:

Synonyms
Close variations
Alternate phrasings

Perfect for:

Expanding core keyword clusters
Avoiding keyword cannibalization
Improving topical relevance

Get Related Keywords

Related keywords go a step further.

Instead of just semantic similarity, these keywords are identified based on:

Overlapping URLs in search results
Shared topical intent

This helps you discover:

Supporting content ideas
Topic clusters
Keywords Google associates with your niche

Get Question Keywords

Users search with questions especially at the top of the funnel.

This endpoint generates keywords phrased as common user questions, making it ideal for:

Blog posts
FAQ pages
Featured snippet optimization
AI-friendly content formats

Examples:

"how to…"
"what is…"
"best way to…"

Get Long-Tail Keywords

Long-tail keywords are where intent and conversion meet.

This endpoint surfaces:

Longer, more specific keyword phrases
Lower competition opportunities
Keywords often ignored by competitors

These are perfect for:

High-conversion landing pages
Niche content
Scaling organic traffic efficiently

How It All Fits Together

A typical workflow looks like this:

Start with a seed keyword
Expand using Similar, Related, Question, and Long-Tail endpoints
Enrich the full list using Bulk Keyword Metrics
Filter by difficulty, volume, and intent
Build content, landing pages, or PPC campaigns

All of this can be fully automated inside your SEO tools, internal platforms, or analytics pipelines.

Conclusion

Keyword research is no longer about guessing. It's about processing data at scale and uncovering intent-driven opportunities.

SE Ranking Keyword Research APIs give you:

Depth (rich keyword metrics)
Breadth (multiple discovery methods)
Scale (thousands of keywords per request)

Whether you're building an SEO platform, running large content operations, or powering data-driven growth experiments, these endpoints provide everything you need to turn keywords into strategy.

Great SEO starts with great keywords and great keywords start with the right data.

If you're new to SE Ranking, please take a deep dive into SE Ranking

Track Your Brand in the Age of AI Search with SE Ranking – AI Search

Ranjan Dailata — Sun, 18 Jan 2026 10:15:00 +0000

Introduction

Search is no longer limited to blue links. Users now ask questions directly to AI systems like ChatGPT, Gemini, and Perplexity and those systems decide which brands get mentioned, linked, or ignored.

This is where SE Ranking – AI Search comes in.

In this post, you will see how SE Ranking's AI Search endpoints help you measure, analyze, and grow your visibility inside LLM-generated answers.

Why AI Search Visibility Matters

Traditional SEO tools tell you:

Which keywords you rank for
Where your pages appear in SERPs

But AI-driven search introduces new questions:

Is my brand mentioned in AI answers?
Do LLMs link to my domain?
Which prompts surface my competitors instead of me?

AI Search Analytics answers these questions by treating LLMs as new search engines with their own rankings, traffic signals, and visibility metrics.

What Is SE Ranking – AI Search?

SE Ranking – AI Search is a collection of APIs designed to analyze how a domain or brand performs inside LLM generated responses.

With it, you can:

Track brand mentions and links in AI answers
Discover prompts that surface your site
Measure trends across different LLMs
Understand your overall AI search footprint

Overview & Discovery

Overview: AI Search Performance at a Glance

The Overview endpoint gives you a high-level snapshot of how a domain performs within a specific LLM.

What you can analyze:

Link presence – How often your domain appears as a source
Average position – Where your brand ranks within AI answers
Historical trends – Visibility changes over time
AI-driven traffic signals – Potential exposure from AI responses

This is perfect for dashboards, executive summaries, or tracking progress after content or SEO changes.

Discover Brand by URL

Before tracking brand mentions, you need a consistent brand identifier.

The Discover Brand by URL endpoint:

Takes a domain as input
Returns the primary brand name associated with it

This ensures brand-based queries are accurate even when your brand name differs from your domain or has variations.

Prompt Analysis: See Why You're Mentioned

AI Search isn't just about metrics it's about context.

Get Prompts by Target (Domain / URL)

This endpoint returns:

All prompts where your domain, subdomain, or URL appears
Whether you're linked or just mentioned
The exact user questions triggering your visibility

Use cases:

Identify content gaps
See which topics LLMs trust your site for
Optimize pages that already influence AI answers

Get Prompts by Brand

Brand mentions matter even when links don't exist.

With Get Prompts by Brand, you can:

Find every prompt where your brand name appears
Understand sentiment and context
Track awareness across multiple LLMs

This is especially useful for:

Brand marketing teams
PR and reputation monitoring
Competitive benchmarking

Practical Use Cases

AI SEO Strategy: Optimize content for prompts that already surface your brand
Competitive Intelligence: Discover which prompts mention competitors instead of you
Brand Monitoring: Track how your brand is described by AI systems
Reporting & Dashboards: Visualize AI search visibility alongside traditional SEO metrics

Conclusion

As AI-powered answers replace traditional search journeys, visibility inside LLMs becomes a new competitive moat.

SE Ranking – AI Search gives you the tooling to:

Measure what was previously invisible
Understand how AI systems perceive your brand
Take data-driven action to improve AI-era discoverability

If SEO was about ranking pages, AI Search is about earning trust in answers and now you can finally track it.

If you're building AI-aware SEO tools, dashboards, or growth workflows, SE Ranking – AI Search is the missing layer between traditional SEO and the future of search.

If you're new to SE Ranking, please take a deep dive into SE Ranking

SEO Data for Developers: Why Programmatic SEO Matters More Than Ever

Ranjan Dailata — Sun, 11 Jan 2026 03:27:58 +0000

Introduction

Search engine optimization is no longer just about tweaking meta tags or publishing blog posts. Today, SEO has evolved into a data-intensive engineering problem one that spans keywords, backlinks, site performance, competitors, SERPs, and now even AI-driven search experiences.

For developers and technical teams, this shift creates an opportunity: treat SEO as an API-driven system, not a manual marketing task.

That’s where platforms like SE Ranking come into play.

The Shift: From SEO Tools to SEO Infrastructure

Traditional SEO tools focus on dashboards and reports built for marketers. While useful, they often fall short when you want to:

Integrate SEO insights into your own SaaS product
Automate competitor analysis or keyword research
Build internal dashboards for growth teams
Run scheduled technical audits
Track brand visibility across search engines and AI systems

Modern teams increasingly want raw, structured SEO data delivered programmatically, at scale, and ready to plug into applications, pipelines, and analytics stacks.

Why APIs Are Becoming the Default for SEO

An SEO API allows you to:

Automate repetitive SEO workflows
Build custom analytics and reporting
Combine SEO data with product, sales, or AI insights
Trigger audits and checks from code
Scale beyond manual tools

Instead of exporting CSVs or logging into multiple dashboards, developers can treat SEO signals like any other data source.

Where SE Ranking Fits In

SE Ranking is known as a comprehensive SEO platform, but one of its most powerful aspects is its Data API.

Through a single API surface, it exposes data for:

Keyword research and search volume
Domain and competitor analysis
Backlinks and referring domains
Technical website audits
SERP tracking and locations
AI search and brand visibility

This breadth makes it especially attractive for developers building SEO-enabled products, not just running campaigns.

SEO Meets Engineering (and AI)

SEO today overlaps with multiple engineering disciplines:

Backend systems - Automation, scheduling, data aggregation
Data engineering - Trend analysis, historical comparisons
Product engineering - Surfacing SEO insights inside apps
AI & LLMs - Understanding how brands appear in AI-generated answers

As search engines evolve and AI powered discovery grows, SEO data is becoming foundational not optional.

Who Should Care About Programmatic SEO?

This approach is especially valuable if you’re:

A developer building a SaaS with organic growth baked in
A startup automating market or competitor intelligence
A data team combining SEO with BI or ML models
An agency building custom SEO tooling for clients
A product team exploring AI search visibility

In all these cases, SEO stops being “just marketing” and becomes part of your system architecture.

Conclusion

SEO is no longer a black box managed solely by marketers. It’s a rich, evolving data domain that developers can and should take ownership of.

With robust SEO data APIs and modern SDKs, teams can build smarter tools, automate insights, and stay ahead of how search is changing especially as AI continues to reshape discovery.

In upcoming posts, you will see how developers can work with SEO data in real-world applications, from competitor analysis to AI search tracking.

If you’re a developer curious about SEO, now is the best time to get involved.

Automating Amazon Product Research with n8n, Decodo & OpenAI

Ranjan Dailata — Sat, 22 Nov 2025 12:44:22 +0000

Download Workflow

Pre-requisite

New to Decodo, please signup here Decodo Signup

Introduction

Researching Amazon products can be surprisingly time-consuming. Whether you’re comparing prices, analyzing reviews, scouting competitors, validating a niche, or gathering data for affiliate content, the process usually involves jumping between product pages, copying details into a spreadsheet, scanning ads, and trying to make sense of it all manually. But what if you could automate that entire workflow from scraping product information to generating meaningful insights and storing everything neatly for later use?

That’s exactly what this n8n workflow enables. By combining the scraping power of Decodo with the analytical capabilities of OpenAI, and finishing with automatic export into Google Sheets, this automation transforms a tedious research process into a streamlined, intelligent, repeatable system. With a single click, it collects data, interprets it, and organizes it so you can focus on decision-making instead of data gathering.

This post walks you through a powerful n8n workflow that automates the entire process:

Scrapes Amazon product data using Decodo
Extracts product details, ads, and metadata
Uses OpenAI to analyze, summarize, and compare
Generates structured insights based on a defined schema
Automatically stores results into Google Sheets

What This Workflow Helps You Do

With a single Amazon product URL, the workflow performs a full analysis that can be used for:

affiliate product write-ups
competitive price evaluation
marketplace product quality assessment
SEO and content briefing
product comparison frameworks
sourcing and dropshipping decisions
market positioning validation

Instead of digging through listings, reviews, ads, and price patterns yourself, the automation collects the information, interprets it, and stores it in a usable format for you.

Scraping the Product Page with Decodo

The workflow starts by triggering manually and feeding in an Amazon product link.

Decodo then fetches the information that would normally require:

scrolling
expanding sections
parsing ad placements
checking pricing variations
identifying ASIN clusters

It extracts:

product title
pricing and discounts
review counts and rating values
images and product identifiers
related ads and sponsored placements

Because Decodo handles rendering and dynamic content, you avoid the headaches of browser automation and blocked scrapers.

Extracting Relevant Data

Once the raw scrape is complete, the workflow filters and separates useful information into focused feeds. It pulls out:

product details
advertisement listings
structured attributes
customer review signals

This ensures that the AI components receive clean data, rather than unfiltered bulk HTML or nested response blobs.

AI-Powered Multi-Stage Analysis

This workflow does more than summarize it performs three different forms of AI evaluation in parallel.

Descriptive Product Summary

This creates a clear narrative overview, useful for:

product descriptions
editorial copy
feature highlights

Competitive Positioning Analysis

This identifies:

strengths and weaknesses
pricing stance in the marketplace
differentiating characteristics
how it compares to similar items

Structured Product Insight Engine

This part is especially compelling the workflow uses a strict JSON schema to extract measurable insights, such as:

how many items appear in the listing results
how many ASINs repeat
average and minimum pricing
review averages
best value item
most reviewed item
pricing spread
Prime eligibility ratios
metadata about product types and listing sources
recommended actions like pricing strategy or listing improvement

Because the output follows a schema, it can:

be charted
be compared
feed dashboards
populate reports
train future automations

No guesswork. No free-form text blobs.

Merging and Preparing the Output

Once all three AI results are generated, the workflow merges them into a single combined dataset. This makes the insights easier to store, reference, export, and use elsewhere.

Exporting Automatically to Google Sheets

The final stage pushes the combined result to a Google Sheet.

The workflow intelligently:

appends a new row if the product is new
updates an existing one if it already exists
preserves historical data
avoids duplication

This makes it ideal for:

tracking multiple product URLs
running daily or weekly data refreshes
monitoring competitor listings over time
building affiliate product catalogs
preparing comparison content at scale

What You Need to Configure

Decodo Credentials

Used to scrape Amazon product pages.

OpenAI Credentials

Used to generate insights and analysis.

Google Sheets Connection

Used to store results in a spreadsheet.
Each can be added easily through the n8n Credentials panel.

Ways You Can Extend This Workflow

Here are some ideas to evolve it into a full product research engine:

Process multiple URLs from a spreadsheet
Scrape entire category pages automatically
Generate comparison summaries between top items
Alert when pricing drops or reviews spike
Push insights into Notion, Airtable, or dashboards
Auto-generate blog posts for affiliate websites
Trigger on schedule instead of manually

Conclusion

This workflow shows how powerful automation becomes when scraping, AI analysis, and structured data handling are combined into a single seamless process. Instead of manually digging through Amazon listings, collecting product specs, comparing pricing, reviewing ratings, or trying to interpret positioning and value, you can now generate all of that insight instantly with one execution in n8n.

By leveraging Decodo for reliable product extraction, OpenAI for intelligent interpretation, and Google Sheets for clean storage and tracking, the workflow turns what used to be a repetitive research task into an effortless, repeatable system. It not only saves time but also produces richer, more consistent results than manual research ever could.

Whether you're an affiliate marketer, product researcher, content creator, marketplace seller, or someone exploring a new niche, this automation gives you a smarter and more scalable way to evaluate products and turn raw marketplace data into meaningful understanding.

Automate Resume Parsing with n8n, Thordata Universal API & OpenAI GPT-4.1-mini

Ranjan Dailata — Thu, 30 Oct 2025 00:33:48 +0000

Download Workflow

Pre-requisite

Signup Throdata

Introduction

Recruiters and HR tech developers spend countless hours manually parsing resumes, structuring data, and entering candidate details into CRMs or ATS systems. What if we could automate all of that — turning unstructured resume files into clean, machine-readable data with a single workflow?

Let’s see how to build an AI-powered Resume Parser using n8n, Thordata’s Universal API, and OpenAI’s GPT-4.1-mini.

What this workflow does

This n8n workflow automatically:

Accepts resume set as an URL within the Edit Fields n8n node.
Sends them to Thordata’s Universal API for the web scraping and extraction of content in a seamless manner.
Feeds the clean text into OpenAI GPT-4.1-mini to extract structured fields such as:
- Full name
- Contact details
- Education
- Work experience
- Skills
- Certifications
- Achievements
Outputs a structured JSON resume object ready for storage or analysis

In short, it turns unstructured resumes into structured data — instantly and at scale.

Who this is for

This workflow is ideal for:

Recruiters & HR automation teams who want faster resume screening
ATS/CRM developers looking to enrich candidate profiles automatically
AI engineers building intelligent hiring platforms
Talent analytics teams seeking structured data for predictive hiring models

Why it matters

Traditional resume parsing relies on keyword matching or complex regex logic — which breaks easily when formats vary. By combining Thordata’s Universal API with OpenAI’s LLM understanding, we unlock:

Robust text extraction across formats
Smarter semantic understanding (e.g., “5 years at Google” = Work Experience)
Customizable JSON outputs for your data pipelines

Setup Guide

Get your API keys

Create a Thordata account and get a Universal API key
Get your OpenAI API key

Import the workflow

In n8n, click Import > JSON, and load the workflow file

Configure credentials

Add your Thordata Universal API key under HTTP Bearer credentials
Add your OpenAI API key under “OpenAI Account”

How It Works (Step-by-Step)

Edit Fields(Set) – The resume URL needs to be set.
Thordata Universal API Node – Extracts readable text from any format.
OpenAI GPT-4.1-mini Node – Converts extracted text into structured resume data.
JSON Parser Node – Formats the model’s response into a standard JSON schema.
Output Node – Persists JSON resume on disk and also writes parsed resumes into a Google Sheet.

How to Customize

You can easily adapt this workflow:

Change AI Model: Swap GPT-4.1-mini with Gemini-1.5-Pro or Claude.
Modify Output Schema: Adjust the JSON fields to match your ATS schema.
Add Notifications: Send a Slack or Email alert when parsing is done.
Store in DB: Replace the Write node with a MySQL/Postgres connector.

JSON Schema

{
  "basics": {
    "name": "Your first and last name",
    "label": "",
    "picture": "",
    "email": "Your email address",
    "phone": "A phone number, with any formatting you like. E.g. (555) 555-5555.",
    "degree": "",
    "website": "Your website URL",
    "summary": "A one-sentence to one-paragraph overview text. Do not include any line-breaks.",
    "location": {
      "address": "Your street address or mailing address",
      "postalCode": "Your postal code (ZIP in the U.S.)",
      "city": "Your city",
      "countryCode": "Your country (e.g. USA)",
      "region": "Your region (state in the U.S.)"
    },
    "profiles": [
      {
        "network": "A social media or other profile that you would like to include (e.g. LinkedIn, Twitter)",
        "username": "Your username on this network",
        "url": "A URL to your user profile page"
      }
    ]
  },
  "work": [
    {
      "company": "Your employer name",
      "position": "Your job title",
      "website": "The URL for the employer's website",
      "startDate": "Your start date, in YYYY-MM-DD format",
      "endDate": "Your end date, in YYY-MM-DD format (leave blank for a current position)",
      "summary": "A one-sentence to one-paragraph summary of this employer or position",
      "highlights": [
        "Bullet-point list items that you would like to include along with (or instead of) a summary paragraph."
      ]
    }
  ],
  "volunteer": [
    {
      "organization": "Your volunteer organization name",
      "position": "Your job title",
      "website": "The URL for the employer's website",
      "startDate": "Your start date, in YYYY-MM-DD format",
      "endDate": "Your end date, in YYY-MM-DD format (leave blank for a current position)",
      "summary": "A one-sentence to one-paragraph summary of this employer or position",
      "highlights": [
        "Bullet-point list items that you would like to include along with (or instead of) a summary paragraph."
      ]
    }
  ],
  "education": [
    {
      "institution": "Your school name",
      "area": "Your area of study or degree earned",
      "studyType": "",
      "startDate": "Your start date, in YYYY-MM-DD format",
      "endDate": "Your completion date, in YYYY-MM-DD format",
      "gpa": "",
      "courses": [
        ""
      ]
    }
  ],
  "awards": [
    {
      "title": "Your award title",
      "date": "Your date, in YYYY-MM-DD format you received the award",
      "awarder": "Your award given by",
      "summary": "A one-sentence to one-paragraph overview of this award"
    }
  ],
  "publications": [
    {
      "name": "Your publication title",
      "publisher": "Publisher name",
      "releaseDate": "Publication date, in YYYY-MM-DD format",
      "website": "The website URL for this publisher or book",
      "summary": "A one-sentence to one-paragraph overview of this publication"      
    }
  ],
  "skills": [
    {
      "name": "A category of job skills (e.g. 'Programming Languages')",
      "level": "",
      "keywords": [
        "Keywords under this category (e.g. 'Java', 'C++', etc)"
      ]
    }
  ],
  "languages": [
    {
      "language": "Language name",
      "fluency": "Your language fluency"
    }
  ],
  "interests": [
    {
      "name": "A category of interests (e.g. 'Sports')",
      "keywords": [
        "Keywords under this category (e.g. 'Cricket', 'Football', 'Golf')"
      ]
    }
  ]
}

Why n8n + Thordata + OpenAI = The Perfect Combo

Building a reliable, end-to-end resume parsing system isn’t just about using AI — it’s about orchestrating multiple technologies that work seamlessly together. That’s where this trio truly shines.

n8n acts as the automation backbone, connecting APIs, AI models, and databases into one smooth workflow. It gives you the flexibility to visually map out your process — from document upload to parsed output — without writing hundreds of lines of code.
Thordata Universal API serves as the intelligent data gateway
It performs the heavy lifting of text extraction ensuring that the content fed to the AI is clean and consistent.
OpenAI GPT-4.1-mini brings the intelligence layer, transforming raw text into structured and meaningful resume data. Its natural language understanding allows it to infer roles, skills, timelines, and context — even when resumes are formatted inconsistently or written creatively.

When combined, these tools deliver a powerful, low-code AI pipeline that’s accurate, scalable, and easy to maintain. You don’t need to worry about regex patterns, inconsistent data formats, or complex infrastructure — just drag, connect, and automate.

Summary

With just a few nodes, you can automate the entire resume parsing and structuring workflow:

No regex
No manual review
100% customizable schema

This is more than automation — it’s intelligence in action.

Competitor Intelligence Agent: SERP Monitoring + Summary Insights with Thordata + OpenAI

Ranjan Dailata — Tue, 28 Oct 2025 16:26:12 +0000

Download Workflow

Pre-requisite

Signup Throdata

Introduction

Ever wished you could instantly analyze your competitors' SEO performance without spending hours scrolling through search results?

This guide will show you how to build a Competitor Intelligence Agent using n8n, Thordata’s SERP API, and OpenAI GPT-4.1-mini — all without writing a single line of code.

By the end, you’ll have a workflow that automatically:

Fetches live search results from Google, Bing, Yandex, and DuckDuckGo
Summarizes your competitors’ strengths and weaknesses
Highlights keyword gaps, content opportunities, and SEO insights — ready to use in your marketing strategy

Why this workflow?

Let’s face it — competitor research is a pain.
You have to open multiple tabs, copy search results, and make sense of it all manually.

This workflow solves that by:

Pulling real-time data from multiple search engines
Using AI to analyze and summarize insights automatically
Delivering structured, human-readable reports within minutes

No more manual SERP scraping. Just actionable marketing intelligence.

Who is this for?

This workflow is perfect for:

Digital marketers who track competitors’ content and keyword strategy
SEO analysts who need quick SERP summaries
Agencies managing multiple clients’ search visibility
AI enthusiasts experimenting with automated market research in n8n

What you’ll need

Before starting, make sure you have:

A free n8n.io account (or self-hosted setup)
Thordata API key (for search scraping) → https://thordata.com
OpenAI API key → https://platform.openai.com

How it works

Here’s a simple breakdown of what happens inside the workflow:

Step 1: Start the Search

You enter a keyword or topic — for example:

“Top AI productivity tools for startups 2025”

Step 2: Multi-Engine Data Fetch

n8n connects to Thordata’s SERP API and fetches search results from:

Google
Bing
Yandex
DuckDuckGo

Each request returns JSON results like titles, URLs, snippets, and ranks.

Step 3: AI Summarization

All those results are passed to OpenAI GPT-4.1-mini, which:

Identifies common competitors
Extracts recurring keywords
Finds topic clusters and ranking trends

Step 4: Output Insights

The final output is a clean, structured summary, for example:

{
  "top_competitors": ["HubSpot", "Ahrefs", "Semrush"],
  "content_trends": ["AI automation", "SEO forecasting", "keyword intelligence"],
  "keyword_opportunities": ["AI SEO tools", "content prediction software"],
  "summary": "Most competitors are focusing on predictive SEO and AI-based automation..."
}

You can push this into Google Sheets, Notion, or Slack for your team.

Setup Guide (5 Minutes)

Import the Workflow
Download the JSON file and import it into your n8n dashboard.
Add Credentials

Add your Thordata Bearer Token (for the search API)
Add your OpenAI API Key (for GPT analysis)

Edit the Search Query
Open the “Set Input” node and replace the sample query with your target topic.
Execute the Workflow
Hit Run Once — and watch the AI agent do its thing!
View the Output
You’ll see structured competitor insights in the n8n execution log.

Want to customize it?

Here are some ideas to make it your own:

Add Sentiment Analysis: Let GPT detect tone of competitor content
Add Alerts: If new competitors appear, send Slack or email notifications
Add Visualization: Push data into Looker Studio or Notion dashboards
Switch Models: Try gpt-4o-mini or Google Gemini for different perspectives

What you can do with it

Once your workflow runs smoothly, you can use it for:

Weekly competitor monitoring
SEO and content planning
Keyword opportunity analysis
Client reporting and insights

It’s like having a mini-SEO research assistant — running 24/7.

Conclusion

This workflow blends automation + intelligence — turning raw search data into competitive strategy insights.

Instead of wasting hours on research, you can focus on what matters — optimizing your content and outranking your competitors.

If you’d like to experiment with this workflow, you can publish it inside n8n’s community templates or fork it for your own AI research tools.

SERP AI Agent Search with Thordata + OpenAI GPT-4.1-mini in n8n

Ranjan Dailata — Mon, 27 Oct 2025 00:55:55 +0000

Download Workflow

Pre-requisite

Signup Throdata

Introduction

If you’re looking to build an AI-powered search assistant that can intelligently query multiple search engines and summarize results, this n8n workflow is a perfect starting point. Leveraging Thordata’s SERP API and OpenAI GPT-4.1-mini, this workflow performs multi-engine searches, synthesizes insights, and maintains a conversational memory for dynamic queries.

Who this is for

This workflow is ideal for:

SEO professionals who want automated SERP research.
Market researchers needing real-time competitive insights.
AI developers experimenting with multi-engine search agents.
Anyone who wants a smart conversational search assistant integrated into their workflow automation.

Problem this workflow solves

Manually searching across multiple search engines and analyzing results can be tedious and error-prone. With this workflow, you can:

Query multiple search engines simultaneously.
Aggregate results automatically.
Use GPT-4.1-mini to summarize and generate actionable insights.
Maintain context for multi-turn queries using memory nodes.

This workflow essentially acts as an AI SERP research agent, saving hours of manual work.

What this workflow does

Chat Trigger
The workflow starts when a chat message or query is received.
LangChain AI Agent Node
This is the reasoning core. It routes queries to SERP tools, interacts with OpenAI, and returns structured answers.
Thordata SERP API Nodes
Real-time search is performed across multiple engines:

Google
Bing
Yandex
DuckDuckGo

OpenAI GPT-4.1-mini Node Processes raw search results and generates:

Summaries
Keyword insights
Actionable recommendations

Memory Buffer Node
Maintains short-term conversational memory for multi-turn interactions.
Output Node
Returns structured results in text, JSON, or directly to the chat interface.

Setup

n8n Installation
Install n8n locally or use n8n.cloud.
Create Credentials

OpenAI API Key for GPT-4.1-mini
Thordata SERP Bearer Auth for multi-engine search

Import Workflow
Import the JSON template into n8n.
Configure Nodes

Replace API credentials in the OpenAI Chat Model and SERP API nodes.
Adjust search query parameters if needed.

Customization Tips

Add more search engines by duplicating the HTTP Request Tool node and configuring a new engine.
Change AI model if you need higher fidelity, e.g., GPT-4.1 or GPT-4.1-turbo.
Output formatting: Customize the final node to return JSON, Markdown, or HTML depending on your application.
Memory depth: Adjust the Simple Memory node to store more or fewer past interactions.

Workflow Summary

Trigger: Receives a query from chat.
AI Agent: Orchestrates multi-engine search and reasoning.
SERP Nodes: Perform Google, Bing, Yandex, and DuckDuckGo searches via Thordata.
OpenAI GPT-4.1-mini: Processes results, generates structured insights.
Memory Node: Maintains short-term context for follow-ups.
Output: Returns summarized, actionable results.

Use Case

A marketing analyst wants to understand the latest trends for a competitor. They type a query like:

"Google Search for Top SEO strategies for e-commerce in 2025"

The workflow:

Queries Google.
Summarizes key strategies and highlights relevant keywords.
Returns an actionable report instantly.

Conclusion

This workflow demonstrates how n8n, Thordata, and GPT-4.1-mini can be combined to create a smart, AI-powered SERP research assistant. It automates search, aggregation, and analysis, allowing professionals to focus on strategy rather than manual research.

Introduction to Thordata

Ranjan Dailata — Sun, 26 Oct 2025 06:21:24 +0000

Pre-requisite

Signup Throdata

Introduction

If you’ve ever dealt with messy data pipelines, endless integrations, or scaling headaches, you’ll know how challenging modern data infrastructure can be. That’s exactly where Thordata steps in. It's a next-generation platform built to make data management faster, smarter, and easier for developers and enterprises alike.

What Is Thordata?

Thordata is a global provider of proxy and web scraping solutions, offering businesses and developers access to a vast network of over 60 million ethically sourced residential IPs across more than 190 countries. Their services are designed to facilitate secure, scalable, and compliant data collection for various applications, including market research, e-commerce monitoring, AI model training, and brand protection.

Use cases

E-Commerce Monitoring: Track product prices and availability across various platforms.
Market Research: Collect data for competitive analysis and consumer insights.
Ad Verification: Ensure the accuracy and placement of digital advertisements.
AI & ML Data Collection: Gather large datasets for training models.
Brand Protection: Monitor online content for brand misuse or infringement.

Core Offerings

1. Proxy Services:

Residential Proxies: Over 60 million real IPs supporting sticky and rotating sessions, ideal for bypassing anti-scraping measures.
Mobile Proxies: Utilize real mobile IPs for device-specific data collection.
Static ISP Proxies: Combine the stability of data center proxies with the authenticity of residential IPs.
Datacenter Proxies: High-speed, cost-effective proxies suitable for large-scale scraping tasks.

2. Scraping Tools & APIs:

Web Scraper API: Fetch real-time data from over 120 websites without the need for development.
SERP API: Retrieve real-time search engine results with pay-per-successful-response pricing.
Universal Scraping API: Overcome CAPTCHAs and anti-scraping mechanisms for seamless data extraction.

3. Developer-Friendly Integration:
Thordata provides comprehensive API documentation and supports integration with various programming languages such as Python, Node.js, PHP, and Java. Their services are compatible with popular scraping frameworks like Scrapy and Selenium, as well as automation tools and cloud platforms.

Why Developers Love Thordata

1. Unified Data Architecture

Thordata gets along with any data type — structured, semi-structured, or unstructured. You can integrate it with relational databases, NoSQL stores, APIs, or even event streams, without the fear of integration silos.

2. Real-Time Intelligence

Its streaming-first architecture guarantees sub-second latency, so you can create real-time dashboards, AI-powered decisioning systems, or live monitoring applications without a hassle.

3. Elastic Scalability

Want to scale from thousands to millions of records per second? Thordata scales automatically — no human tuning, no batch jobs running overnight.

4. AI-Optimized Optimization

Integrated ML models constantly monitor your data streams to identify bottlenecks, forecast hotspots, and optimize performance dynamically in the background.

5. Developer-First APIs

With simple-to-use APIs facilitates the developers to directly integrate Thordata on any programming language they prefer. And, a graphical dashboard makes it easier for you to track and manage everything from one location.

Who Uses Thordata?

From tiny startups to large enterprise data teams, Thordata is assisting businesses:

Create low-latency AI pipelines
Aggregate and analyze API data
Manage ETL workflows for SaaS products
Power search and recommendation engines

Basically, if your work touches data — Thordata can make it faster and more efficient.

Key Features You’ll Love

60M+ Residential IP Network – Access data from real users across 195+ countries with high anonymity and geolocation accuracy.
4G/5G Mobile Proxy Infrastructure – Test and verify apps or ads under real mobile conditions.
Universal Scraping API – Automatically does IP rotation, CAPTCHA solving, and JavaScript rendering.
SERP API – Retrieve structured data from Google, Bing, and Yandex for SEO or market research.
AI-Ready Datasets – No more data cleaning — work with curated datasets for e-commerce, finance, or reviews.
Intelligent Proxy Routing – Adaptive routing maintains low latency and high uptime.
Secure by Design – Built-in end-to-end encryption, GDPR compliance, and role-based access.

Why Thordata Matters

Today's apps, particularly AI-driven ones, rely on clean, trustworthy, and real-time data. Yet existing infrastructure tends to hold back teams with complexity and expense.

Thordata disrupts that — a platform for developers designed to manage the messy bits of data so you can get on with creating products, not pipelines.

Conclusion

Thordata unifies speed, intelligence, and scalability in one place, simplifying complicated data pipelines and making them efficient.

No matter if you're designing AI models, real-time analytics, or SaaS platforms, Thordata enables developers and teams to get from data chaos to clarity — quicker, smarter, and more reliably.

Building RenderForgeArt AI: A Multimodal Creative Suite Powered by Google AI Studio

Ranjan Dailata — Fri, 05 Sep 2025 16:16:31 +0000

This is a submission for the Google AI Studio Multimodal Challenge

Background

The name RenderForgeArt AI combines three strong creative/tech terms:

Render → Refers to generating or visualizing content (common in graphics, video, and 3D design). In the AI context, it suggests the system renders images, videos, or even art from prompts.
Forge → Symbolizes crafting, shaping, and building. It gives a sense of creativity, innovation, and "forging new paths" in digital art.
Art → Clearly communicates the focus on creativity, visuals, and design.
AI → Highlights that artificial intelligence powers the entire process.

RenderForgeArt AI - An AI platform that forges and renders artistic creations from imagination into reality.

What I Built

RenderForgeArt AI is a multimodal creative suite that empowers anyone to generate, edit, and enhance high-quality visuals through AI.

RenderForgeArt AI leverages multimodal AI models to make design accessible, fast, and scalable.

Built on state-of-the-art diffusion models and multimodal transformers.
Designed for creatives, marketers, SMBs, and enterprises who need speed + quality.
Acts as a bridge between idea and execution, reducing design cycle times by up to 80%.
Built as an AI-first design tool, it combines text-to-image, image editing, and multimodal input (text + image + voice).
Offers export-ready assets for web, social media, presentations, and print.
Includes real-time generation of artwork with the editing of the assets that helps the creators, marketers, and businesses to work with ease.

The Problem

Traditional creative workflows are expensive and time-intensive, requiring skilled designers and software expertise.
Demand for visuals is exploding (social media ads, product branding, web design).
Non-designers (marketers, small business owners, educators) struggle to produce high-quality visuals quickly.
Current AI image tools are single-modality (mostly text → image only) and lack editing, collaboration, and workflow integration.

The Solution

RenderForgeArt AI solves this by providing an all-in-one multimodal creative suite:

Text → Image: Generate images from plain text prompts.
Image → Image: Transform sketches/photos into polished designs.
Text + Image Fusion: Refine visuals with hybrid inputs.
Voice-to-Text: Generate natural voice to text. It helps the end users to easily express their thoughts for producing the realistic images via the prompts.
One-Click Export: Optimized outputs for social, print, and web.

Use Cases & Real-World Applications

Marketing & Branding

Generate ad banners, social media creatives, and posters instantly.
Customize campaigns with consistent brand themes.

Product Design & Prototyping

Convert sketches into realistic prototypes.
Iterate design variations rapidly.

Education & Training

Create illustrations for e-learning materials.
Enhance presentations with AI-generated visuals.

Healthcare & Corporate

Generate infographics for reports, dashboards, and patient-friendly documents.
Create professional pitch visuals in minutes.

Creative Industries

Artists can co-create with AI, testing new visual styles.
Filmmakers/storytellers can prototype concept art and storyboards.

Demo

RenderForgeArt AI Demo

RenderForgeArt AI on Google AI Studio

RenderForgeArt AI Source Code

From Image -

From Text -

Yet another sample -

Apply the edit

From Sketch -

Sticker -

Flashcard -

You might get the below error say if you are directly running the above scenario within the Google AI Studio.

How I Used Google AI Studio

RenderForgeArt AI integrates Google AI Studio as its foundation model hub for multimodal creativity. The platform provides access to state-of-the-art generative models that power different creative workflows.

1. imagen-4.0-generate-001 → Text-to-Image Engine

Role in RenderForgeArt AI:

Backbone for high-quality visual generation from text prompts.
Used for marketing creatives, concept art, product mockups, and illustrations.

Capabilities leveraged:

Fine-grained style control (e.g., photorealistic, artistic, 3D render).
High-resolution outputs up to poster quality.
Image-to-image transformations (reference-guided generation).

Example in workflow:

User types: "A futuristic hospital dashboard UI in neon colors" → RenderForgeArt AI calls imagen-4.0-generate-001 → Generates export-ready UI concept images.

2. gemini-2.5-flash → Multimodal Orchestration Layer

Role in RenderForgeArt AI:

Functions as the multimodal reasoning engine.
Handles text + image fusion, prompt refinement, and creative suggestion.

Capabilities leveraged:

Cross-modal understanding → align text descriptions with visual references.
Creative assistant → suggests better prompts, style variations, and design improvements.
Real-time interactions → e.g., chat with the AI: “Make it brighter and add a neon glow.”

Example in workflow:
User uploads a product photo and types: "Turn this into a glossy magazine ad." → gemini-2.5-flash aligns the request + image → passes structured instructions to imagen-4.0-generate-001.

3. veo-2.0-generate-001 → Video Generation & Motion Design

Role in RenderForgeArt AI:

Powers short-form video creation and motion graphics.
Expands still images into animated visuals.

Capabilities leveraged:

Text-to-video (promo clips, ad mockups).
Image-to-video (animate a static scene).
Style transfer across video frames.

Example in workflow:
Prompt: "30-second clinic EHR promotional video with smooth UI animations" → veo-2.0-generate-001 produces a polished animated demo clip.

Multimodal Features

User Input (Text, Image, Voice)

Text → “Create a healthcare dashboard”
Image → Sketch/photo as reference
Voice → “Show me a logo with a phoenix”

Gemini (gemini-2.5-flash)

Interprets multimodal input
Suggests prompt refinements
Aligns user intent with visuals

Imagen (imagen-4.0-generate-001)

Generates high-res still images
Refines or stylizes existing assets

Veo (veo-2.0-generate-001)

Expands stills into motion graphics
Delivers marketing-ready videos

Export Layer

Optimized output for social media, print, or enterprise presentations

Why This Multimodal Stack Matters

Imagen ensures top-tier visual quality.
Gemini ensures intelligent orchestration + cross-modal reasoning.
Veo ensures video storytelling + campaign-level assets.
Together, they make RenderForgeArt AI a true Creative Suite, not just another image generator.

Conclusion

RenderForgeArt AI – Creative Suite represents the next evolution of creative tooling:

Democratizes design with multimodal AI.
Serves professionals, SMBs, and enterprises alike.
Positions itself as the go-to platform for AI-first creativity at scale.

BrightData Company Deep Research Report with AI Agent, Google Search, Glassdoor and CrunchBase + Google Gemini

Ranjan Dailata — Sun, 31 Aug 2025 16:20:26 +0000

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

Pre-requisite

New users of Bright Data, please make sure to sign-up here - Bright Data
n8n
Google Gemini. Please Sign up on Google AI Studio to get the API Key.

Download the Workflow

BrightData Company Deep Research Report with AI Agent, Google Search, Glassdoor and CrunchBase + Google Gemini

What I Built

I built an AI-driven company deep research workflow that combines Bright Data scraping, Google Search enrichment, and Google Gemini intelligence to automatically generate a human-readable company research report in Markdown format.

The workflow brings together three key sources of truth:

CrunchBase → Company funding, size, acquisitions, and market positioning.
Glassdoor Company Info → General overview, employee ratings, and employer branding signals.
Glassdoor Company Reviews → Employee sentiment, culture, leadership perception, and workplace insights.

These datasets are aggregated, cleaned, and then passed into Google Gemini, which transforms raw information into a strategic deep research report. The final output is a structured, insight-rich document ready for use in competitive intelligence, due diligence, recruitment, and sales prospecting.

The Problem

Conducting thorough company research traditionally involves:

Visiting multiple platforms (Google, CrunchBase, Glassdoor).
Copy-pasting data into spreadsheets or documents.
Manually reading and summarizing employee reviews, which are often noisy and inconsistent.
Struggling to merge hard business data (e.g., funding history) with soft cultural insights (e.g., employee sentiment).

This manual process is:

Time-consuming (hours per company).
Error-prone, since unstructured data is difficult to consolidate.
Not scalable, especially when researching dozens or hundreds of companies.

The Solution

The workflow solves these challenges by automating the end-to-end company research pipeline:

AI Agent (Gemini) as Orchestrator
- Constructs Bright Data and Google Search queries.
- Identifies relevant Glassdoor and CrunchBase URLs.
- Normalizes the links into JSON using structured parsing.
Bright Data Scraping & Extraction
- CrunchBase → Funding rounds, acquisitions, employee counts.
- Glassdoor Company Info → Ratings, headquarters, overview.
- Glassdoor Reviews → Extracts employee feedback for culture and sentiment.
AI-Powered Interpretation with Google Gemini
- Cleans and summarizes noisy review text into insightful narratives.
- Synthesizes multiple datasets into a cohesive deep research report.
- Formats the report in Markdown for easy export, sharing, or conversion to PDF.
Final Deliverable: Deep Research Report
- Provides a balanced view by merging:
  - Quantitative business data (CrunchBase).
  - Qualitative cultural insights (Glassdoor).
- Reduces hours of manual research into minutes of automated reporting.

The result is a scalable, reliable, and AI-powered research assistant that empowers teams across recruitment, investment, sales, and strategy.

Introduction

This workflow is designed to perform deep company research by combining Bright Data scraping, Google Search enrichment, and AI-driven interpretation with Google Gemini.

At its core, the workflow focuses on building a comprehensive Deep Research Report by integrating three critical data streams:

CrunchBase → Funding, acquisitions, size, and market positioning.
Glassdoor Company Info → Company overview, general facts, and employer branding signals.
Glassdoor Company Reviews → Employee sentiment, leadership feedback, and culture insights.

The extracted data is normalized, enriched, and finally synthesized into a human-readable Markdown report by Google Gemini. This ensures that raw data (e.g., JSON dumps from Bright Data) is transformed into strategic insights with clear narratives.

The workflow is particularly useful for:

Competitive Intelligence → Compare multiple companies on funding, growth, and employee sentiment.
Investor/VC Due Diligence → Validate funding data alongside employee perspectives.
Market Research → Understand brand perception and workforce satisfaction in target industries.
Recruitment Insights → Position employer branding by combining company facts with real employee experiences.

By merging CrunchBase’s hard business metrics with Glassdoor’s cultural insights, the workflow produces a well-rounded research report that supports both quantitative and qualitative analysis.

Use Cases & Real-World Applications

1. Competitive Intelligence

Gather data on competitors from CrunchBase and Glassdoor.
Analyze company size, funding, employee sentiment, and growth trajectory.
Build benchmark reports for strategic decision-making.

2. Recruitment & Employer Branding

Extract Glassdoor reviews to understand employee sentiment.
Present AI-enhanced summaries of culture, leadership, and employee satisfaction.
Aid recruiters in positioning companies more effectively when pitching roles.

3. Investor/VC Due Diligence

Pull CrunchBase funding data and combine it with Glassdoor reviews.
Generate AI-curated summaries of risks, strengths, and employee perspectives.
Accelerate investment decision-making with reliable research reports.

4. Sales Intelligence / Account Research

Enable B2B sales teams to perform deep prospect analysis.
Extract data from public search, Glassdoor, and CrunchBase before outreach.
Provide sellers with AI-driven one-pagers on target accounts.

Workflow Overview

The workflow follows these main steps:

Chat Trigger
- Starts when a user sends a company name via chat.
Set Input Fields
- Captures the company name and prepares it for downstream nodes.
AI Agent (Google Gemini)
- Constructs Bright Data search queries.
- Identifies and retrieves relevant URLs (Glassdoor, CrunchBase, reviews).
- Uses Structured Output Parser to normalize URLs into JSON format.
Bright Data Extraction
- Glassdoor Company Info → General company overview.
- Glassdoor Reviews → Employee sentiment and reviews.
- CrunchBase Data → Funding, size, acquisitions, market insights.
Human-Readable Content Extraction (Glassdoor Reviews)
- Uses Google Gemini to convert raw reviews into natural Markdown summaries.
Merge Responses
- Combines CrunchBase, Glassdoor overview, and review summaries into a single dataset.
Deep Research Builder
- AI Agent generates a comprehensive research report in Markdown format.
- Includes Glassdoor insights, CrunchBase data, and AI-curated analysis.
Respond to Chat
- Sends the research report back to the requester in real time.

Key Components

AI Agent (Gemini-powered):
Acts as the orchestrator for data retrieval, URL discovery, and query building.
Bright Data Scraping Tools:
Handle web scraping and structured data extraction from CrunchBase and Glassdoor.
Structured Output Parser:
Ensures the AI output is formatted as JSON for reliability.
Gemini Chat Models:
- Used at three levels:
- Query building & orchestration (AI Agent).
- Human-readable review extraction.
- Deep research synthesis into Markdown.
n8n Merge Node:

Combines multi-source insights into a single structured object.

Output

The final output is a Markdown Deep Research Report containing:

Company Overview (Glassdoor + CrunchBase data).
Employee Sentiment Summary (Glassdoor reviews).
Funding, Size, and Market Data (CrunchBase).
AI-generated strategic insights (growth trends, risks, opportunities).

Deep Research Report Sample: Bright Data

1. Company Overview

Website: www.brightdata.com
Headquarters: Netanya, Israel
Founded: 2014
Type: Private Company
Industry: Internet & Web Services
Employees: 201 to 500
Revenue: $100 to $500 million (USD)

Description

Bright Data positions itself as the world’s #1 web data platform, providing crucial public web data solutions to a diverse client base including Fortune 500 companies, academic institutions, and small businesses. Their platform enables users to research, monitor, analyze data, and make informed decisions through efficient, reliable, and flexible data retrieval.

Serving over 15,000 customers globally across nearly every industry, Bright Data offers a range of no-code data solutions for business owners, alongside a robust infrastructure tailored for engineers and IT professionals. Key benefits highlighted by users include cost-effective, fast, and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience. The company emphasizes its commitment to transparency and compliance.

In 2021, Bright Data launched The Bright Initiative, a separate organization dedicated to providing pro-bono access to Bright Data’s technology and expertise. This initiative partners with NGOs, NPOs, academic institutions, and public bodies globally to drive positive change, having collaborated with over 600 organizations to date.

Mission Statement

Bright Data's mission is to create technologies that preserve a transparent internet, ensuring easy access to and collection of public web data. They believe that making public web data readily accessible is vital for maintaining openly competitive markets, which ultimately benefits everyone. The company actively partners with and serves entities that align with its core values of transparency, innovation, and trust.

2. Ratings & Reviews

Based on 182 anonymous reviews, Bright Data maintains an overall rating of 3.7 out of 5 stars.

Overall Rating: 3.7 ★
Would Recommend to a Friend: 70%
CEO Approval (Or Lenchner): 75%
Total Reviews: 182

Ratings by Category (out of 5.0)

Culture & values: 3.5
Diversity, Equity & Inclusion: 3.8
Work/Life balance: 3.8
Senior management: 3.5
Compensation and benefits: 3.8
Career opportunities: 3.5

3. Awards & Accolades

Bright Data has received notable recognition, including:

Top 50 EMEA companies, G2, 2023
Best Estimated ROI, G2, 2023

4. Pledges & Certifications

Pledge to thrive: Bright Data has committed to taking steps to prioritize employee well-being.

5. Employee Insights & FAQs

Employee Sentiment

Overall Employee Rating: 3.7 out of 5 stars (based on 182 anonymous Glassdoor reviews).
Recommendation Rate: 70% of employees would recommend working at Bright Data to a friend.
Business Outlook: 72% of employees believe Bright Data has a positive business outlook.

Compensation and Benefits

A specific employee review (Dec 20, 2024) indicates a concern regarding compensation and benefits for remote workers, stating that the company "purposefully staff[s] the company with a ton of remote workers from foreign countries so they don't have to pay them living wages or provide benefits...."

Interview Experience

Positive Interview Experience: 45% of job seekers rate their interview experience at Bright Data as positive.
Interview Difficulty: Candidates report an average difficulty score of 3 out of 5 for job interviews.

How to Get a Job

Prospective candidates are advised to browse open positions, apply, and prepare for interviews, noting a moderate difficulty level in the process.

This report can be:

Delivered in-chat.
Stored in Google Sheets, Notion, or a database.
Exported as a PDF for reporting purposes.

Major Challenges and Solutions

Challenge 1: Fragmented Company Data Across Multiple Platforms

Problem: Company data was spread across Glassdoor, CrunchBase, and general Google Search results, each with unique structures and reliability levels.

Solution: Implemented Bright Data scrapers and standardized them with a Structured Output Parser in n8n, ensuring normalized JSON formats that could be merged seamlessly.

Challenge 2: Noise in Glassdoor Reviews

Problem: Raw employee reviews on Glassdoor often contained excessive noise, slang, and irrelevant commentary.

Solution: Applied Google Gemini summarization to extract human-readable insights from employee reviews, focusing on sentiment, recurring themes, and leadership perception.

Challenge 3: Identifying the Right URLs Dynamically

Problem: For each company, Glassdoor and CrunchBase URLs differ and may include duplicate or outdated results from search engines.

Solution: Used an AI Agent (Gemini) to construct smart search queries and parse Google/Bright Data results, filtering for the most relevant URLs with higher accuracy.

Challenge 4: Generating a Cohesive Research Report

Problem: The extracted data was too fragmented, making it difficult for end-users to gain actionable insights.

Solution: Designed a Deep Research Builder Agent (Gemini) that merged data streams (Glassdoor insights, CrunchBase funding info, and employee sentiment) into a single Markdown-formatted research report.

Challenge 5: Maintaining Reliability in Multi-Step AI Orchestration

Problem: Errors in one step (e.g., failed Bright Data scrape) could break the entire workflow.

Solution: Implemented error handling and fallback prompts in Gemini Agents, ensuring that partial data could still generate a useful report instead of failing completely.

Download the Workflow

BrightData Company Deep Research Report with AI Agent, Google Search, Glassdoor and CrunchBase + Google Gemini