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Overview

The cryptocurrency market is notoriously volatile, and keeping ahead of the trends is essential. But manually gathering data from multiple sources like exchanges, social media, and forums can be a major bottleneck.

In 2025, there’s a smarter way to stay informed. With no-code crypto data scraping and automation tools, you can simplify the data collection process and get real-time insights, all without writing a single line of code.

This solution is ideal for crypto and finance enthusiasts who want to streamline data collection and make quicker, more informed decisions in the ever-changing crypto market.

Manual Data Collection is Holding You Back

If you're still copying data from sites like CoinMarketCap, Twitter feeds, or crypto forums into spreadsheets, you're wasting precious time. The challenges with this approach are clear:

• Dispersed Data Sources: Market data is scattered across multiple platforms, making it hard to track trends in one place.
• No Easy Way to Filter or Analyze: Without automation, extracting meaningful patterns from raw data becomes a tedious task.
• Time-Consuming Workflows: Manual data entry slows you down and increases the likelihood of errors.

Instead of getting bogged down by manual tasks, consider using crypto data automation tools and setting up an automated solution to pull the data you need, when you need it.

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Make Smarter Crypto Moves

No-code crypto data scraping tools are transforming the way we track, understand, and act on crypto market trends. By integrating crypto data visualization tools, you can easily track market trends and identify key patterns in real-time, helping you make more conscious trading decisions. Whether you're a trader, analyst, or content creator, a no-code toolkit helps you keep pace with the market and make better decisions.

Looking to automate your crypto data collection process? Relu Consultancy can create tailored solutions to help streamline your data gathering, so you can focus on making smarter, data-driven decisions.

Contact us today to get started.

Build a Centralized, No-Code Crypto Dashboard

By setting up a no-code crypto data scraping dashboard, you can pull data from multiple platforms and visualize trends effortlessly. Here's what a no-code setup allows you to do:

• Automate Cryptocurrency Insights: Gather data automatically from various crypto exchanges, social media platforms, and news sites.
• Create Custom Dashboards: Use tools like Google Sheets, Notion, or Airtable to visualize the data, filter trends, and spot changes in market sentiment or prices.
• Make Quick Decisions: With up-to-date insights and real-time crypto data extraction, you can act faster and with more confidence, whether you're trading, analyzing, or forecasting.

Setting Up Your No-Code Crypto Data Toolkit

Building a no-code crypto data toolkit is easier than you think. Here’s how you can set up your system:

1. Choose Your Tools
   You don’t need to be a developer to start. Look for crypto data automation tools that support the following:

• Web Scraping: Choose web scraping tools that can collect data from multiple platforms (e.g., Python, Scrapy, or more accessible platforms like Octoparse).
• Automation: Platforms like Zapier or Make (formerly Integromat) allow you to automate workflows without any coding.
• Dashboards: Tools like Google Sheets + Google Data Studio, Notion, or Airtable work great for creating a centralized hub for your crypto data.

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2. Set Up Web Scraping
   Now, it’s time to set up web scraping for crypto analytics. You can pull price data, social media mentions, sentiment analysis, or news updates. Set the scraping frequency based on your needs: daily for general trends, hourly for real-time data. Many crypto market data scraping tools allow you to automate the process, so the data is always up-to-date.

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3. Automate Data Flows
   Once the data is collected, the next step is automation. Use platforms like Zapier or Make to:

• Send scraped data directly into your chosen dashboard.
• Set up notifications or alerts based on price changes, news mentions, or social media sentiment shifts.
• Automatically tag or categorize new data to keep everything organized.

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4. Visualise Your Insights
   Now that your data is flowing, integrate it into a no-code crypto dashboard tool. Focus on key metrics such as:

• Price trends
• Trading volume spikes
• Market sentiment scores

You can make use of filters to focus on specific coins, exchanges, or news sources, and set up custom reports to spot patterns easily.

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What You Can Achieve

To get the most out of your no-code blockchain data tools, here are some tips:

• Eliminate Manual Data Entry: No more copying and pasting data from multiple sources into spreadsheets.
• Get Real-Time Updates: Stay on top of market movements and trends without delays.
• Save Time: Automate monotonous jobs to save up critical time for analysis and strategy.
• Make Smarter Decisions: With all your data in one place, you can make more informed decisions faster using automated crypto analytics platforms that provide real-time insights and trend analysis.

Tips for Success

To get the most out of your no-code crypto data toolkit, keep these tips in mind:

• Start Small: Focus on a few key data sources to avoid getting overwhelmed.
• Validate Data Regularly: Even with automation, it’s essential to ensure your data is accurate and reliable.
• Use Filters and Alerts: Set up automated filters to avoid information overload and get only the insights you need.
• Scale as You Grow: Once you’re comfortable, you can add more integrations and data sources to your toolkit.

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Overview

This project focused on developing an end-to-end automated image processing pipeline to support AI model training in agriculture. The system was designed to collect and validate high-resolution images of crop diseases, nutrient deficiencies, pests, and fungi.

By automating data acquisition and applying filtering aligned with research practices, the client assembled a structured, consistent crop disease dataset for use in machine learning agriculture models aimed at plant health diagnostics.

Client Background

The client operates in the agritech AI sector and specializes in developing tools for AI crop diagnostics. They required a scalable and repeatable method to build a high-quality agricultural image dataset for machine learning.

Previously, image collection was handled manually, which introduced inconsistencies and slowed down dataset development. The new system needed to support image curation at scale while adhering to standards common in academic and applied AI contexts.

Challenges & Objectives

• Manual sourcing of agricultural images was inefficient, inconsistent, and unscalable
• Ensuring image quality and relevance without automated validation was time-intensive
• Lacking a consistent framework for data labeling, categorization, and audit trails
• Preparing a dataset that meets the quality standards expected in supervised learning workflows

Objectives

• Build an image scraping tool for agricultural AI training datasets
• Organize images into standardized categories (deficiencies, pests, diseases, fungi)
• Implement automated validation and deduplication using a research-aligned image filtering tool
• Provide metadata tracking and transparency through structured logging
• Enable scalable, continuous data collection via server-based deployment

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Conclusion

Relu Consultancy delivered a scalable, research-informed solution that transformed fragmented image sourcing into a reliable and automated process. The final crop disease dataset enabled the client to accelerate AI model development with cleaner, labeled data that met training standards. By integrating scraping, relevance filtering, audit logging, and storage into a seamless workflow, the system laid a strong foundation for future work in AI crop diagnostics and machine learning agriculture.

Approach & Implementation

The following are the details of the approach that was taken:

Search Query Construction

The system began by defining detailed search queries mapped to common crop issues. These included terms like “rice leaf potassium deficiency” or “soybean rust symptoms.” Filters for image resolution (minimum 480p), location, and recent timeframes were applied to reflect the freshness and quality standards typical of AI training datasets.

SERP Scraping Module

Selenium and search engine APIs (Google, Bing, Yahoo) were used to retrieve image URLs, page sources, and metadata. A retry mechanism handled rate limits to ensure uninterrupted extraction. This module served as the core of the image scraping tool for agricultural AI training datasets and supported robust, high-volume collection.

Image Download and Storage

Image files were downloaded using blob and base64 methods, then stored in cloud repositories like Google Drive and AWS S3. A hierarchical folder structure categorized the images into deficiencies, pests, diseases, and fungi. This structure was built to support downstream tasks such as annotation, model validation, and class balancing, which are critical steps in ensuring model generalizability.

Quality and Relevance Filtering

An AI-based content validation layer, supported by perceptual hashing (phash), was used to detect and eliminate duplicates. Content relevance was assessed using predefined visual cues. Only images meeting clarity and context standards were retained. Filtered-out samples were logged for audit, promoting dataset transparency and adherence to data sanitization best practices. These steps helped preserve the consistency and usability of the plant health AI training data.

Metadata Logging with Google Sheets

A synchronized Google Sheets interface was used to log filenames, sources, categories, and filtering status. This created a live audit trail and helped align data science workflows with agronomic review processes. The traceability also simplified quality checks, dataset updates, and collaboration.

Server Deployment and End-to-End Testing

The entire system was deployed to a cloud server, enabling scheduled runs and continuous data collection. Each pipeline component, from search query execution to dataset export, was tested to ensure reliability and alignment with training data requirements in machine learning agriculture projects.

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Results & Outcomes

The following were the outcomes of the project:

• The platform enabled automated, scalable collection of thousands of high-resolution crop images
• Images were consistently categorized, validated, and prepared for use in AI development
• Manual image vetting was eliminated, saving time and improving dataset preparation speed
• Images were organized into usable classes for model training
• Substandard and redundant images were filtered out before ingestion
• The system remained operational with minimal manual intervention
• Metadata logging improved dataset management and accountability

Key Takeaways

Given the direction of the project, the following key takeaways were identified:

• A structured pipeline combining automated image processing and content validation is essential for building reproducible AI datasets
• Using perceptual hashing and relevance scoring ensured dataset quality and reduced noise
• Metadata tracking supported review, debugging, and retraining workflows
• Aligning the pipeline with academic dataset practices supported long-term research and commercial deployment goals

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Overview

This project focused on building an automated deal discovery platform for online marketplaces that helped users track listings, evaluate pricing trends, and identify promising deals. The system automates listing analysis, pricing checks, and real-time alerts. It gave users faster access to undervalued items while reducing the manual effort involved in tracking and verifying marketplace listings.

Client Background

The client operates in the e-commerce and resale intelligence space. Their operations depend on timely access to online listings and the ability to identify profitable deals based on historical pricing. Their business model involves reselling items sourced from various online platforms, where spotting underpriced listings early can directly impact margins.

Previously, this process relied heavily on manual tracking and limited third-party tools, which led to inconsistent data capture, slower decision-making, and missed opportunities. They needed a solution that could automate data collection, adapt to different platforms, deliver actionable insights, and support long-term growth.

Challenges & Objectives

The objectives and identified challenges of the project were as follows:

Challenges

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• Many platforms require session handling, login credentials, and browser simulation to access listing data.
• The volume of data scraped across platforms was too large for manual processing or flat-file storage.
• Identifying underpriced listings required developing logic based on historic data rather than arbitrary thresholds.
• Alerts and insights needed to be easily accessible through a visual, filterable dashboard.

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Objectives

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• Build a web scraping tool for e-commerce price tracking across authenticated and public platforms.
• Store listing data, images, and metadata in a long-term, structured format using PostgreSQL
• Develop logic to flag deals using basic price analytics and machine learning for detecting underpriced marketplace listings.
• Integrate real-time deal alerts for resale and flipping platforms via Telegram.
• Deliver a dashboard for monitoring item listings and price trends

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Conclusion

Relu Consultancy developed a system that helped the client automate their listing analysis and improve the accuracy and speed of deal detection across online marketplaces. With a combination of scraping logic, database structuring, alert systems, and frontend visualization, the automated deal discovery platform for online marketplaces turned raw data into actionable insight. It allowed the client to respond faster, track trends more consistently, and reduce manual oversight. The project demonstrated how well-planned platform monitoring tools can support resale workflows and scale alongside marketplace changes.

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Approach & Implementation

The following were the details of the approach and subsequent implementation of solutions:

Web Scraping and Automation

To support both public and login-protected marketplaces, the system was built using Playwright and Puppeteer. It simulated human browsing behaviour, handled logins, and used proxy rotation through tools like Multilogin. These measures reduced the risk of detection while maintaining long-term scraping stability. Custom scraping routines were written per platform, allowing for flexible selectors and routine maintenance.

Data Storage and Processing

Listing information was stored in a PostgreSQL database with proper indexing for fast retrieval. Metadata, images, timestamps, and item descriptions were organised for long-term access and trend analysis. This backend design allowed the platform to serve as a scalable online monitoring platform for pricing intelligence.

Deal Detection and Analysis

Using Python libraries like Pandas and NumPy, the team created logic to track historical pricing trends and detect anomalies. Listings that significantly deviated from baseline values were flagged. A lightweight machine learning model built with scikit-learn was added to improve deal prediction accuracy over time, helping refine what was considered a "good deal."

Alerts and Integrations

A Telegram bot was developed to send real-time deal alerts. Alerts could be filtered by item type, location, price range, or custom parameters. This helped the client reduce the lag between listing appearance and response time.

Dashboard Interface

The frontend was built using HTML, JavaScript, and minimal UI components for clarity and responsiveness. The dashboard for monitoring item listings and price trends allowed users to view historical and live data, inspect flagged deals, and analyze pricing movements across platforms. Simple charts and filters gave structure to the raw data and improved decision-making.

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Results & Outcomes

The following were some notable outcomes of the project:

• The platform monitoring tools automated listing collection across several marketplaces, including complex ones like Facebook Marketplace
• Real-time alerts improved the client's ability to act quickly on profitable listings
• Historical data and live insights were centralized in one easy-to-use dashboard
• The scraping routines continued to operate even as site structures changed, thanks to the modular selector design

Key Takeaways

Here are the conclusions we can draw from the project:

• A modular marketplace monitoring system, supported by browser emulation and scraping logic, can reliably track high-volume listing data
• Combining structured databases with basic ML techniques provides a scalable way to detect pricing anomalies
• Real-time notifications reduce manual monitoring and help users act quickly on undervalued listings
• Simple, focused dashboards make large datasets easier to work with, especially in fast-paced resale environments

1. Executive Summary

Online sports betting is a world of high stakes with high speed. Here, every second counts, and that’s why every decision needs high precision. That’s what the management at toto.bg thought when they realised that repetitive manual betting selections aren’t just inefficient but risky as well.

This case study uncovers how toto.bg designed and implemented a custom-built Robot Automation solution to replace imperfect workflows with a swift and fully automated system. The system involves integrating real-time data via API, allowing seamless CSV uploads and executing betting logic with algorithmic accuracy. The enhancement helped toto.bg reduce human error significantly and boost operational speed. Lastly, it helped unlock scalable efficiency, which is a bonus.

2. Introduction

What seems manageable in the betting world, got complicated after expansion of the betting world, which demanded more stringent rules, putting a pressure on the management to set everything right. It became tough for the toto.bg also to track fast-moving games, loads of data and endless choices happening simultaneously.

Imaging sorting through all the information manually: to track live matches, read user strategies, check the odds and input everything precisely- repeatedly. It not only slowed down the process, but came with more stress, tiresome and messy.

toto.bg was seeking a suitable solution to address this issue.

Enter: Robot Automation.

It enabled coupling user logic with real-time sports data, transforming the arduous manual betting tasks into a sleek automated process.

3. Project Overview

Project Name: Robot Automation

Objective: Automate betting selections to optimise speed, precision, and scalability

Key Features:

• Real-Time API Integration: Pulls live match data from toto.bg
• CSV-Based User Interface: Allows users to upload their betting strategies with drag-and-drop ease
• Automated Decision Engine: Executes betting choices with zero manual input

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10. Conclusion & Future Outlook

Robot automation began as a tool; however, it has ultimately evolved into a transformative technology. It became more than an optimiser. It showcased how intelligent workflows can pinpoint accuracy, scale effortlessly and empower business users to take control.

We are now exploring more possibilities with Robot Automation. These include:

• Predictive Betting Inputs via historical match data
• Multi-Language Interfaces for international rollout
• Cloud Deployment for real-time sync across devices.

Let’s conclude this case study with a statement: The future of betting automation isn’t just possible; it’s programmable, too.

4. Workflow & Process

Step 1: Plugging into the Pulse

To enable smart betting decision, the system was fed with what’s happening in the sports world. That’s where the toto.bg API came into the picture. It exactly works like a digital scoreboard, continuously streaming real-time information about matches, teams and betting options.

A system was integrated that connected to this stream. It collects all the live match details, and organized them neatly into options:

• 1 (home win)
• X (draw) or
• 2 (away win)

It was like giving the robot a front-row seat to watch the match, enabling it to understand what’s exactly is going on, capturing every moment. This step eliminated the need to track the results manually or type in odds. Instead, the system reads the data instantly, precisely and doesn’t miss a beat, similar to a top sports analyst who never takes a break.

Step 2: Designed for Humans

The front end is developed using CustomTkinter, which even non-techies find easy to operate. Users can upload a CSV file with their choices without needing to use Excel. Behind the curtain, the inputs are validated by the system, mapped to live data and set into the queue for automated action.

Step 3: Logic Comes to Life

In this step, the CSV meets real-time data, prompting the robot to take on the task, process logic, confirm eligibility and execute bets with algorithmic quietness. It’s like having a personal assistant who works relentlessly and yet never makes a mistake.

5. Explanation of Project

Let’s simplify the explanation by imagining the betting process as a relay race. Initially, in the manual-run process, every step, right from data review to logic matching to placing bets, was prone to errors. Robot automation replaced the entire relay time with a single ultra-efficient sprinter. As simple as that.

The automation helps harmonise the static strategy from CSVs uploaded by the users with dynamic conditions, which are pulled from live match data. Users benefit from no misclicks and no missed matches. All they get is a zero-latency logic execution.

6. Purpose & Benefits

The purpose was to enable maximum bets in the shortest possible time. That’s what management envisioned to make robot automation happen.

Here’s what made the difference:

1. A faster workflow: Repetitive tasks are now completely automated, transforming tedious steps into one-click execution.
2. Simplicity in uploading CSV: Allows users to upload better logic in batches.
3. Live Data Smarts: Merging real-time match data with rule-based inputs helped unmatched precision.
4. No Human Error: No more blunders due to manual data copy or mismatches.
5. Scalability: The modular system design enables easy scalability across new games, markets or regions.

7. Marketing & Sales Objectives

Robot Automation isn’t just a tech project. It’s a living case study for intelligent automation. Here are a few outcomes:

1. Exhibit Real Automation: The project demonstrates that automation is feasible for complex, rule-based tasks.
2. CSV + API Versatility: This demonstrates that the system can integrate with any structured data workflow.
3. Speed and Accuracy: Performance through automation was quicker, more accurate, and faster than the manual process.
4. Users' Empowerment: Users benefit from the betting logic without any technical know-how.
5. Positioning as a Tech Partner: The automation process positions the development team as problem-solvers, not merely coders.

Imagine Robot Automation serving as a lighthouse to guide other industries out of the ocean of manual tasks.

8. Technical Stack & Use Cases

Outstanding technology is invisible to the user; however, it is powerful under the hood. Here’s some brief information about the technical stack and use cases.

1. Python: It serves as the logic engine, handling API parsing, data mapping, and selection algorithms.
2. Custom Tkinter: The actual front-end interface, the UI layer, provides a sleek and user-friendly desktop interface, making it easy for users to interact with.
3. PyInstaller: It packages everything into a plug-and-play .exe file.

This model can also be adapted for:

• Automated invoice validation
• Real-time pricing bots
• Inventory restocking automation
• Rule-based email or alert generation

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9. Results & Impact

The results were immediate and measurable once the automation was launched. Here are the results:

• Manual efforts were reduced to 75% during betting events.
• Input-to-output accuracy leapt to 99.2%, as compared to 89% in previous manual workflows.
• Execution time was reduced from minutes to milliseconds.
• Users requested the integration of the system for new sports and games within the first month.

Introduction

Businesses are turning the tables into data-driven models, yet they often overlook one of the richest sources of untapped lead data- email archives. Buried within old Outlook backups are potential goldmines of sales intelligence, contact information, and engagement patterns. This project focuses on unlocking potential by automating the extraction of valuable lead data from PST files using Python and artificial intelligence (AI) technologies. The results include a scalable system that not just parsed emails but enriched, validated, and prepared the data for smooth CRM integration.

Brief Description

The solution was built to mine the data, specifically the large ones from historical emails stored in PST format —Outlook’s native archive format. These archives contained years of business communication that hold valuable lead information if mined smartly.

We developed a Python-based automation tool to handle this task completely. It parsed PST files to extract email metadata and content, used artificial intelligence to interpret unstructured text, and generated organized CSV files ready for the CRM platform.

The key processes include deduplication, validation via external APIs, and filtering irrelevant or internal communication. This tool revived both forgotten email threads and active sales opportunities.

Objectives

Client-specific goals

The tool was engineered to offer several targeted objectives:

• Automatic extraction of lead data from old Outlook emails- The system eliminates the need for any data mining by parsing PST backups automatically. These backups contain thousands of emails from previous years, which, when correctly parsed, reveal valuable insights and contracts.
• Structured dataset generation for the sales team- Rather than presenting raw data, the tool structures extracted information into clearly defined fields, including names, email addresses, job titles, company names, phone numbers, and more. This allowed for a dataset that was actionable for the sales team,, providing them with the option to filter, sort, and analyze the data as needed.
• Cleaning, deduplication, and validation of extracted contacts- To ensure high data quality, duplicate contacts were removed using a session-wide comparison. Additionally, you can utilize syntax checks and blacklist filters to validate emails, enabling teams to focus solely on usable and high-value leads.

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Conclusion

The project illustrates the powerful combination of AI, automation, and data validation in transforming legacy email archives into actionable lead intelligence. The use of Google Gemini for intelligent parsing and NeverBounce for email validation ensured accuracy and relevance while incorporating features such as deduplication, logging, and domain filtering.

This case is a clear example of how old communications when combined with modern technology, can fuel new opportunities and streamline lead generation workflows.  

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Wider Business Purposes

Beyond immediate use cases, the solution came together with broader business development goals:

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1. Lead generation: Identification of High-Quality, Engaged contacts

The automation helps identify contacts who have previously interacted with the business or with individuals already familiar with the company. It also focuses on leads that are likely to engage again and bring out high-value targets for outreach.

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2. Data enrichment: Converting unstructured email data into information

By using AI, the system adds structure and intelligence to unstructured email content. Information, such as job titles and inferred company types, turns basic emails into strategic sales leads.

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3. CRM Readiness: Generating importable CSVs for HubSpot/Salesforce

The organized output of data is prepared for compatibility with some of the frequently used CRM platforms. This ensures the importing process is smooth, allowing the sales team to start engagement activities without delay.

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4. Personalization for Outreach: Using Roles and Industry to Tailor Campaigns

Detailed job titles and company information make hyper-targeted messaging easy. For example, marketing executives can receive campaign pitches, while IT heads can get product specifications relevant to their business.

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5. Email validation: Improving overall deliverables via External APIs

Email validation via APIs reduces bounce rates. This improves campaign efficiency and ensures a higher sender reputation is put forward for future outreach.

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6. Competitor insights: Finding for company engagement

By analyzing sender domains and relevant content, the tool identifies which competitor companies are involved in previous conversations. This information informs competitive strategies and reveals better possibilities for partnership opportunities.

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Technical Base

The entire system was built using modular and scalable technologies:

1. Programming language: Python 3.x

The solution was developed via Python. It is chosen for its versatility, wide range of libraries, and robustness in data manipulation and automation.

2. AI API: Google Gemini

The tool was integrated with Google Gemini to perform natural language parsing. This could extract names, job roles, companies, and phone numbers and infer organizational structure from contextual clues.

3. Email validation: NeverBounce API

To ensure data accuracy, emails are validated through the route of NeverBounce API. This checks for deliverability, syntax correctness, and domain reputation.  

4. Data Storage: CSV via Pandas

Structured data was stored using Pandas DataFrames and exported as CSV files. This format facilitates universal compatibility and ease of use in CRMs.

5. Logging: Custom module

A dedicated logging module was used to track every step of the extraction process- starting from successful parses to debugging.

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Key features

PST Email Extraction

At the core of the tool is its ability to extract information from PST files:

• Parses .pst Outlook Backups

This tool uses a PST parser to read and iterate over each item in the backup file. This helps in navigating folders, subfolders, and threads.

• Extracts email bodies and metadata

Each email subject, body, sender and receiver metadata, and timestamp information are captured.

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Filters out Internal or Irrelevant Domains

Domains like internal company emails or spam-like sources are filtered using verified and configured blacklists (failed.json).

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AI-based parsing

Once raw emails are extracted, Google Gemini powered the intelligent interpretation:

• Contact names- Names are pulled from both email metadata and content, accounting for signatures and context within threads.
• Job titles- The AI reads email signatures and introductory lines to deduce any professional roles.
• Company names- It detects company names with the help of domain references, email signatures, and mentions in the content.
• Phone numbers and addresses - Contact details embedded in signatures or within emails are extracted.
• Company type inference- Based on domain names and context, the AI attempts to list information about the industry or function of the organization.

Validation and deduplication

To bring out the best output, the following process is undertaken:

• Removal of duplicate entries across various sessions - This tool maintains a cache of processed entries to prevent redundancy, even during multiple runs.
• Validate email syntax- Regex patterns check the organized level and validity of each email before moving into further processing.
• Skips blacklisted domains - This process is for internal domains that can be excluded using a configurable list. This helps focus on external leads.

Data Output

The final dataset is a highly planned and organized CSV file having-

• Cleaned output
• Fields with the rows, namely Name, Company, Job Title, Email, Website, Phone number, and other valuable attributes that assist with segmentation and targeting.

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Customization and notes

• Domain filtering via failed.json

A JSON file allows dynamic updates to the domain exclusion list without changing code.

• Rate limiting with time.sleep(1)

To comply with API usage quotas, delays were added between requests to Google Gemini.

• Logging errors and duplicates

Detailed logs were used to enable traceability and help troubleshoot any skipped or failed entries,

• Future extensibility

While the current version's output CSVs are available, the architecture was designed to support direct integration with CRM APIs, such as HubSpot and Salesforce, in future iterations.

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Outcome

Achievements

• Massive Time savings by processing thousands of emails in an hour.
• High-quality leads with enriched metadata to ensure data isn’t just complete but meaningful and ready for outreach.
• Focused sales efforts to ensure relevant leads were prioritized as per the high-intent contacts.

Potential ROI

• An 80-90% reduction in lead research time, resulting in a decrease in manual labor required to identify qualified leads.
• Real-time validation and domain filtering further reduced the bounce rates.
• Historical emails once considered digital clutter, are now an active resource in the business development arsenal.

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Overview

The Toto-TKT project involved developing an automated ticket creation system to replace manual formatting and production processes for branded lottery and raffle tickets. This project was created to read Excel data and generate structured, print-ready tickets with precise layouts, essentially functioning like an automated ticket machine for high-volume output.

The goal was to turn digital inputs into high-quality physical materials, making it easier to produce tickets for campaigns, sweepstakes, and other large-scale events.

Client Background

The client works in promotional events, where ticket-based systems are an important part of operations. 

Whether running a sweepstake, community giveaway, or corporate campaign, the client frequently has to produce a large volume of tickets with custom layouts and branding elements.

Previously, these tickets were created manually by editing data in Excel and mapping it onto physical templates. This was not only labour-intensive but also inconsistent, especially at higher volumes. Small layout mistakes, printing issues, and the risk of duplication led to operational setbacks. They needed a solution that would eliminate these problems while saving time. We endeavoured to set up an automated ticket creation system.

‍Challenges & Objectives

The following were some challenges that the auto lottery processor faced and the resultant objectives that were formulated:

Challenges

• The manual formatting process was error-prone, especially for high-ticket volumes.
• Inconsistencies in layout often led to misalignment during printing.
• Physical ticket templates required precise mark placement to remain legible and usable.
• The ticket generation process had to accommodate varied ticket lengths and structures, depending on the campaign.

Objectives

• Develop a tool that could convert structured Excel rows into printable tickets.
• Maintain strict grid alignment for marks and numbers to match physical layouts.
• Output professional-quality PDFs that require no additional editing.
• Build a process that could be reused for future campaigns with minimal setup.

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Conclusion

Relu Consultancy built an auto lottery processor that solved a clear operational pain point for the client. The Toto-TKT Project turned spreadsheet data into clean, event-ready tickets with speed and accuracy.

The system’s modular design made it easy to reuse across different types of events, including raffles, lotteries, and entry passes. The combination of precision formatting, batch processing, and user-friendly documentation made it easy for the client to adopt and scale.

Overall, the project made high-volume ticket creation faster and more consistent, helping the client deliver professional, branded materials for their campaigns.

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Approach & Implementation

Relu Consultancy created a Python-based automated ticket generation system capable of reading Excel files and converting them into layout-specific, high-resolution PDFs. The system was built to handle variations in ticket structure, support branding requirements, and generate outputs ready for printing on standard A4 paper. To support adoption, the final solution included walkthrough documentation and a screen recording for internal training.

Key Features

• The script reads six-number combinations from a designated "Combination" column in Excel.
• It supports multiple formats: 10, 12, or 13 values per combination mapped precisely on a grid.
• Each ticket displays either dots or crosses based on client preference.
• Ticket numbers are rendered upside-down to suit the physical layout's readability.
• Outputs are saved as A4-sized PDFs at 300 DPI for high-quality print compatibility.
• Error handling, validation checks, and auto-foldered output directories help maintain order during large batch generation.

Common Use Cases

The Toto-TKT Project’s functionality made it suitable for a variety of event and campaign needs. Some of the most frequent applications included:

• Lottery Tickets: Generates unique combinations for official lottery draws, laid out for immediate printing.
• Raffle Entries: Creates hundreds or thousands of entries for community or commercial raffles, all formatted consistently.
• Event Entry Passes: Custom tickets with individual identifiers and formatting tailored to specific event themes.
• Survey or Exam Sheets: Marked layouts aligned with answer sheets or feedback forms, where precise placement is crucial for scanning or review.

Results & Outcomes

The automated ticket generation system delivered significant improvements across multiple areas:

• Faster Turnaround: The manual formatting process that once took hours was reduced to just minutes.
• Accuracy at Scale: Mark placements were accurate to the millimetre, helping avoid printing misalignment and formatting problems.
• Consistent Branding: Branded tickets followed a standard design across batches, improving presentation at events.
• Scalable Outputs: The client could generate large quantities of tickets in one run without worrying about duplication or formatting breakdowns.

By delivering consistent, professional tickets that could be printed and distributed immediately, the client gained greater control over promotional materials. The tool also opened opportunities for new use cases, such as interactive surveys and educational events.

Key Takeaways

Several important lessons emerged from the Toto -TKT Project:

• Automating layout-based tasks significantly reduced the risk of human mistakes and the time spent on repetitive formatting.
• Using a grid-based logic system helped maintain precise alignment across ticket designs and batches.
• Including a screen recording and walkthrough documentation made onboarding internal users simpler and more effective.
• The solution bridged digital inputs (Excel) and physical outputs (printed tickets), giving the client a reliable way to manage ticketing for events and campaigns of any size.

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