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Introduction

Reading the email and extracting the crucial data is a tedious and time-consuming task. This is especially true for IT teams that spend their valuable time going through the text-heavy backup emails received from backup and disaster recovery service providers.

The emails from the service providers usually contain critical information about the status and health of data backups. These emails help the IT team monitor the backup processes and take necessary actions if any problem is detected.

However, if any critical detail is missed during manual reading, it can lead to delayed responses, unnoticed backup failures, and potential data loss. A simple negligence can disrupt business continuity and affect infrastructure security.

With automation enhancing efficiency in traditionally time-consuming processes, Relu expert helped build a system that automated the data extraction from backup emails from service providers like Acronis and Datto.

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Project Scope

The manual approach to reading emails, especially the ones that deal with critical data associated with data security and backup. However, the manual approach slows down the response time and also increases the risk of overlooking the essential details. This can lead to missed alerts and potential data protection failures.

The client faced the same issues and required an automated system to parse backup alert emails from Acronis and Datto. Acronis and Datto are well-known providers of backup, disaster recovery, and cybersecurity solutions.

Email parsing for IT management is the automatic extraction of structured data from emails and helps the teams collect specific data accurately. In this case, the business wanted an intelligent automated email extraction solution for fetching details, like backup alerts, timestamps, and backup size.

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Objectives

The objective of this Acronis and Datto backup alert monitoring process is to solve common problems, as:

• Eliminate Manual Tracking of Backup Alert Emails

With the backup email parsing automation solution, the need for IT teams to sift through numerous emails is completely eliminated. The team can focus on resolving the issues rather than searching for them.

• Accurate Parsing of Critical Data

Automated solutions can parse the key details, like backup status, timestamps, and affected systems, with precision. So, it reduces the risk of human errors that happen during natural extraction.

• Filter Out Irrelevant Details From Email

The intelligent Datto backup monitoring solution smartly filters out non-essential emails, like promotional emails and routine confirmations. This allows the team to focus on important alerts that indicate potential failures or security threats.

• Filter Out Irrelevant Details From Email

The extracted backup data is stored in a centralized and structured format such that the team can easily access the data and go through it. The structured data can be used for further processing, providing actionable insights.

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The Bottom Line

Manual data entry is prone to errors, and a small error in security and data backup aspects can lead to severe consequences. That’s why Relu’s automated email parsing for IT monitoring ensures that all the critical details are extracted accurately and stored in a structured manner. The stored data can be exported in other formats, like CSV, JSON, or Excel format, for further analysis. It can exported using API, like Flask or Fast API, to fetch MySQL data dynamically and present it in JSON format.

The automated email extraction solution for backup alerts provides a scalable and error-resilient framework for backup monitoring. With automated error handling, logging mechanisms, and server deployment, IT teams can keep track of everything easily with minimal intervention.

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Solution

To build the automated email parsing solution for IT monitoring, Relu experts designed a platform that automates email processing, parsing, and tracking of backup alert emails from Acronis and Datto. The platform is designed using Python’s libraries and frameworks for email processing, parsing, and monitoring.

Here’s how our solution works:

1. Email Processing and Parsing:

The backup email parsing automation solution fetches the backup email alerts from Arconis and Datto using Microsoft Emails API. The implemented parsing rules extract the data systematically to get the relevant details, like:

• Backup Status
• Timestamp
• Device/Server Name
• Backup Size
• Error Messages (if any)
• Backup Location
• Next Scheduled Backup

Once the processing of a set of emails is done, the email status is updated to prevent duplicate parsing.

        2.  Acronis Parsing

The Datto and Acronis backup alert processing solution extracted the backup job details from both the subject and the body. The key data points included the start time and end time of the backup, the duration of the backup process, and backup size.

         3.  Datto Parsing

For Datto emails, the Arconis and Datto backup monitoring solution extracted the essential details for accurate status tracking. The applied filters remove the irrelevant emails which do not contain the data related to backup alerts.

        4.  Error Handling and Logging

The custom error messages were implemented to detect and log issues, like missing or malformed backup data, email parsing failures, and connectivity or API issues. The logging mechanism implemented was designed to track the errors and debug the insights for better system maintenance.

         5.  Data Storage and Integration

The parsed data was stored in MySQL database, which helps the client in quick retrieval of backup history and efficient monitoring and reporting. The email records are updated with their processing status to ensure transparency.

         6.  Deployment and Automation

The backup email parsing automation solution is deployed on a server to ensure it remains up and running at all times. The automated scripts monitor and parse the incoming emails from Arconis and Datto in real time. It reduces the need for manual intervention.

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Results & Impact

Theimplementation of backup email parsing automation transformed backupmonitoring. The system improved the team’s efficiency and productivity, whichwas affected by manually checking and reading the emails from Acronis andDatto. Manually checking each email from the stack, extracting the details, andcopying them into a database is an error-prone process.

However, this automated solution substantially reduced the manual efforts, allowing the IT team to focus more on proactive issue resolution and less on email checking.

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Client Background

The client operates in the overseas sports betting space. Their operations involved monitoring and placing bets across several sports betting platforms, includingPS3838 and CoolBet. Previously, this process was handled manually, which was time-consuming and introduced room for human error. The client sought a sports betting software solution that could automate these actions while adapting to platform changes and maintaining data reliability.

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Challenges & Objectives

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

Challenges

•  Each sports betting platform used a different site structure, which made creating a consistent automation logic difficult.
• Layouts, CSS selectors, and API structures changed frequently, often breaking existing scripts.
• Many platforms employed anti-bot systems, such as CAPTCHAs, behavioral detection, and IP restrictions.
•  The client needed up-to-date and accurate data at all times to make informed decisions, which required validation and error-handling mechanisms.

Objectives

• Build a flexible Chrome extension capable of sports betting automation across multiple platforms.
• Design it to adapt quickly to frontend changes and different site architectures.
• Implement basic bot avoidance features such as proxy rotation and request timing.
• Maintain and update the system regularly to support long-term use.

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Conclusion

The browser-based automation system developed by Relu Consultancy gave the client a reliable way to manage betting tasks across platforms like PS3838 and CoolBet. Each platform had its quirks, so custom scripts were built to handle the different site structures. To keep things running smoothly, the system dealt with common issues like CAPTCHAs, changing layouts, and bot detection using proxy rotation, timed requests, and fallback strategies.

The sports betting software continued to perform well even as betting platforms evolved. With steady updates and bug fixes, it reduced the amount of manual work involved and helped create a more consistent, automated process. Overall, the project showed how important it is to build adaptable tools that can grow with changing online environments.

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Approach & Implementation

Custom browser scripts were developed for each supported website, allowing the extension to interact with the site’s elements as a user would. The code was structured in a modular fashion, making it easier to isolate and update individual components when a platform changed. This modularity also simplified testing and future feature integration.

A lightweight design was prioritized to ensure the extension ran smoothly on standard user systems without needing significant resources or complex setup.

The AI betting software incorporated dynamic learning mechanisms to adapt to platform changes efficiently.

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Maintenance & Updates

Frequent platform updates often caused selector breakage. To address this, regular bug-fixing cycles were introduced to inspect and update affected scripts. Code refactoring accompanied these updates to maintain a clean codebase.

The selector logic was improved with strategies to handle minor layout shifts, reducing the need for constant manual changes. The automated betting strategies integrated into the system ensured that users could adjust betting logic without overhauling the software.

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Anti-Bot Considerations

Several strategies were used to reduce the risk of bot detection:

• Proxy rotation was implemented to distribute traffic and avoid IP bans.
• Request timing and user-like behaviors were randomized to mimic human actions.
• Fallback mechanisms were added to maintain functionality during temporary access issues or data gaps.

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Monitoring & Support

Basic logging captured session data, including timestamps, responses, and errors, enabling faster issue identification. Retry logic helped the system recover from failed or timed-out requests.

Ongoing support involved regular performance reviews, updates, and the rollout of new features in response to evolving needs. The integration of web scraping for betting sites helped ensure that real-time odds and data were always accessible.

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Results & Outcomes

The Chrome extension reduced the need for manual interaction in betting tasks. Processes such as monitoring odds, placing bets, and navigating between platforms became partially or fully automated.

Response times improved across multiple platforms, and the system remained stable even during frequent front-end changes. The automated sports betting system continued to perform well despite evolving platform restrictions.

The solution also scaled over time. As new sports and platforms were introduced into the client’s workflow, the extension continued to deliver reliable performance thanks to its maintainable design and structured update process.

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Key Takeaways

Upon completing the project, we identified the following key takeaways:

•  In fast-moving environments like online sports betting, sports betting software significantly improved efficiency and accuracy.
• Planning for constant change early on helped the system stay one step ahead of platform updates and anti-bot measures.
• Regular maintenance, whether updating broken selectors or fixing subtle bugs, was key to keeping the extension stable over time.
• Having a modular code structure allowed new platforms and features to be added without reworking the entire system.
• Even small improvements in betting bot development free up time and allow the client to focus on decision-making rather than manual data gathering.

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Project Background& Objectives

• The web dashboard serves as an all-in-one hub for watch collectors, sellers, and resellers, offering smarter search, reliable verification, and time-saving tools for better outcomes.
• Manual tracking across platforms—Users had to jump between websites like eBay,Chrono24, and niche watch forums to track listings. This process was time-consuming and tedious, especially when trying to monitor changes in availability or price.
• Lack of verified information—Most listings clearly show whether the watch is genuine, including original packaging and proper documentation. This creates uncertainty and increases the risk of buying counterfeits and overpriced items.
• Slow, time-consuming decision—Without consolidated data and easy comparison tools, buyers and sellers spend hours searching for watches, which delays the purchase, sales, and overall market opportunities.

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Target Users

• Watch collectors- They are individuals who are looking for limited edition, rare, and vintage watches and have a strong preference for authenticity and originality.‍
• Vintage watch lover- People deeply interested in heritage timepieces and historical models seek complete sets, including boxes and papers.
• ‍Resellers- Professionals identifying price differences between platforms to profit from quick buys and resales. ‍
• Online sellers- Sellers manage inventory on multiple platforms and require monitoring of competition and market trends. ‍
• Luxury buyers- Individuals purchasing high-value watches need assurance of legitimacy and pricing accuracy.

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Project Goals

Automate watch data collection eliminates the need for manual browsing by automatically pulling listings from multiple sources in real-time.

• Verify listing from AI- Use AI to confirm listing authenticity based on photos, description quality, and metadata.
• Provide actionable market information—Present users with pricing history, demand signals, and comparable listings through a clear, interactive dashboard.
• Enable proactive tracking- Let users set alerts and automated searches to be notified when a desirable listing becomes available.

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Conclusion

Relu's web dashboard solved the coreproblem of fragmented, unverified watch listings by delivering an AI-poweredplatform and comprehensive solution. This solution unified search, alertcreation, and analysis. It empowered users to act faster, smarter, and withbetter confidence in the high-stakes luxury watch marketplace.

By reducing manual effort and boosting trust through intelligent automation, this web dashboard refined timepiece tracking.

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Solution Overview

The web dashboard delivers an intelligent, end-to-end solution that transforms fragmented online listings into usable insights:

User-focused design: A dashboard with filters to compare and track watches easily.

AI-powered validation: It processes images and descriptions to assess the authenticity and completeness of each listing.

Data aggregation engine- Uses web scraping to pull watch listings from key platforms and forums.

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Phases of Development

Phase1 – Data Aggregation: Automated script crawl and extraction of watch listing from marketplaces like eBay, Chrono24, and others:

• Rotation proxies: It uses rotating proxies and user agents to avoid detection and IP bans.
• Multiple process in line: Adapts to different site structures, ensuring consistent, clean data extraction from multiple sources

Phase2 – AI Verification: Every listing is analyzed to assess its credibility:

• NLP: It parses the product title and description to extract key details such as brand, model, reference number, and packaging information.
• Image recognition: AI models review uploaded images to identify box presence, documents, and counterfeiting indicators.
• Confidence score: Assign a confidence score to check authenticity and listing quality.

Phase3 – Dashboard & UX: User interaction can be done when the dashboard and UX are built on an attractive interface:

• Saved search: Personalized setting sallow for quick reassessing of commonly used questions.
• Price trend graphs: Historical data is displayed visually, helping users see how prices have changed over time.
• Advanced filters: Users can search for it by price, brand, model, location, documentation, etc.

Phase4 – Smart Alerts: These alerts help users stay on top of the market:

• Email notification: Alerts for specific keywords, price drops, and newly listed items
• Search automation: To continuously monitor user-defined parameters and trigger notifications instantly.

Phase5 – Deployment: Scalable deployment is put into place to ensure long-term reliability and growth readiness:

• Cloud-native architecture: It is hosted on cloud-native architecture that dynamically helps allocate resources for performance under load.
• Job queues: These include background job queues for the timely processing of alerts and scraping
• Automated backup: Backup and monitoring tools safeguard data integrity and platform availability.

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Challenges &Solutions

1. IP bans during scraping -  We have used rotating proxy servers and diversified user-agent strings to mimic natural user behavior.
2. Duplicate listings - Employed AI that matches images and description to detect duplicates across platforms even with slight changes.
3. Authenticity validation - Developed custom-trained machine learning models that analyze product images and meta data for improvising trust in listing.
4. Multi-currency confusion - Developed live currency conversion APIs so that listings reflect current exchange rates.
5. Timely updates - Implemented asynchronous job queues that allow for frequent email alerts and listing updates without overloading the system.

Business Impact

General Impact

• Unified marketplace view—This web dashboard combines data from scattered platforms into one centralized place, allowing users to monitor the entire market from a single dashboard.
• Massive time savings—Thanks to the automation and verification tools, Users can report reducing their research and comparison time by nearly 80%.
• Improved buyer confidence- AI-backed authenticity scoring adds a trusted layer, especially for high-ticket purchases.
• Better pricing strategy- With access to historical data, users can negotiate better and spot undervalued listings.

User-Specific Impact

For Watch Collectors:

• Identify rate and vintage models matching specific criteria like original boxes, paper, and regional availability.
• Reduces the risk of buying incomplete sets and counterfeiting items by relying on AI-authenticated insights.
• Saves personalized search and receives real-time alerts.

For Resellers:

• Gain competitive edge by spotting underpriced listings across platforms.
• Use historical data and trend graphs to determine ideal buy and sell windows.
• Maximizes resale margin by understanding and targeting high-demand watches

For Watch Sellers:

• It uses the platform to benchmark prices against similar listings across different marketplaces.
• Understand clearly what features increase desirability, such as specific model references and included packaging.
• Gauges real-time market demand and helps in inventory planning.

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User Feedback & Early Metrics

• Simplicity- Users supported the platform’s attractive UI and how much faster it made their decision process.
• High engagement- The average sessiontime was over 12 minutes. This indicated deep user interaction.
• Effective alerts- Email open rates were 63%. This confirms that notifications were timely and relevant.
• Reliable data- Marketplace data scraping accuracy stood at 94%. This is verified through sampling and user validation.

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Automating Part Number Data Extraction and Processing with AI: Case Study

Automating the data extraction process has become a critical part of the business dealing with extensive product inventories or part numbers. The need to streamline data collection while maintaining high accuracy is the challenge that most industries face.

In this blog, we will dig deeper into the case study of a project aimed at automating the process of part number scraping and data processing. It will also cover how leveraging AI-driven techniques and modern web scraping solutions solves the problem.

Companies are aiming to make data-driven decisions today, covering the requirement for accurate and organized data essentials. Manual data collection, though reliable in certain contexts, cannot match the efficiency, precision, and speed of automation. This project case study exemplifies the role of automation in transforming traditional data-gathering methods.

Overview of the project

The client’s primary objective is to automate the extraction of part number details from various online sources. Previously, this process involved manual data extraction, resulting in errors, time consumption, and inconsistent data formatting. The project focused on creating an automated, scalable, and accurate solution that collects and processes part number data.

 Manual data collection for part numbers often results in data discrepancies and incomplete information. This project aimed to remove such errors by introducing AI-based automation. It captures, processes, and stores necessary data. By integrating web scraping techniques and AI-powered data processing, the system improved operational efficiency and minimized manual effort.

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Step by Step Process of Part Number Scraping and Data Processing

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1. SERP Queries for Part Numbers

The process starts with leveraging a SERP API to retrieve 15-20 URLs relevant to a specific part number. This automated method ensures a broad yet focused selection of data sources.

• Input: Specific part number details   
• Output: Curated list of website URLs for data extraction

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2. Data Scraping from URLs

Web scraping tools such as Selenium and Markdownify are employed to extract content from the collected URLs. These tools efficiently handle dynamic site structures while overcoming challenges like CAPTCHAs, login requirements, and restricted pages.

• Input: List of URLs
• Output: Raw website content for further processing

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3. AI-Powered Data Processing

The scraped content is processed using Gemini1.5 Pro AI, which specializes in content analysis. It extracts specific details, such as alternative or equivalent part numbers, categorizes the data, and filters out irrelevant information to ensure accuracy.

• Input: Website content
• Output: Structured data fields ready for export

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4. Data Storage and Accessibility

The processed data is stored in CSV format or Google Sheets, ensuring easy access for analysis and integration into existing systems. This structured format supports seamless business operations.

• Output: A CSV file or Google Sheet

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5. Testing and Validation

A quality control team performs manual spot checks on randomly selected part numbers to check data accuracy against original sources. Any differences in the result are documented and addressed to maintain data integrity.

This streamlined approach improves overall process efficiency, accuracy, and scalability in part number data extraction and processing.

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Conclusion

This project demonstrates the power of combining AI-driven content processing with automation to smooth the data extraction process. The solution fulfilled the client’s requirement and set a foundation for scaling the process to accommodate more complex data extraction needs.

 Businesses looking for a solution for their data extraction process can draw inspiration from this approach. AI and automation can help businesses stay competitive in data-driven industries.

 By integrating these techniques, any organization can harness the power of accuracy and actionable insights, leading to better decision-making, better productivity, and a solid competitive edge in the evolving market.

Steps taken for Efficient Execution

1. URL Collection- Automated URL collection with SERP API ultimately saved time and effort required to gather reliable data sources.
2. Comprehensive approach- Selenium and Markdownify enabled a comprehensive approach to web scraping, bypassing site-specific limitations.
3. Content analysis- Advanced content analysis via Gemini 1.5 Pro further allowed for precision extraction, which is crucial for details on the parts.
4. Data storage- Data was stored in accessible, user-friendly format to enable quick decision-making and data utilization.
5. Quality validation- A rigorous quality validation process also ensures that extracted data have high accuracy and relevance.

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Purpose of the project

Streamline and automate the entire extraction process to reduce dependency on manual efforts. Some of the specific purpose of the project includes:

•  Accelerating data retrieval for part numbers, reducing time and labor.
•  Ensuring highest level of accuracy in extracted data, minimizing errors.
• Deliver structured data output that integrates well into the client's existing system.

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Tech stack and use cases

•  Python: The core programming language for automation. It uses libraries like Selenium for scraping and Pandas for data processing.
• Flask: A lightweight framework used to create APIs that handle data requests and integrations.
• Serp API: Real-time search engine scraping to prepare accurate URLs based on part numbers.
• Gemini AI API: For advanced text analysis and content extraction to prepare alternate part numbers and equivalent products.

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AI driven Operational and Cost Benefit

• Operational efficiency: The entire automation process reduced the involvement of manual hands, allowing employees to focus on strategic tasks.

• Data accuracy: AI-powered data validation process that minimizes errors and ensure reliability.
• Scalability: The system that can adapt to larger data volumes without significant reconfiguration.
• Competitive advantage: Access to structured, organized data improves overall decision-making and market intelligence.
• Cost savings: Automating the data scraping process to reduce the need for a large workforce dedicated to manual data extraction.

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Project overview

The future of business operations is automation, with companies seeking ways to eliminate repetitive, time-consuming tasks. By automating monotonous processes, businesses can redirect their valuable resources toward strategic activities that help with growth.

That's why our client reached out to us seeking an automated solution for ordering SIM cards from Hot Mobile, Israel's leading telecommunications provider. The manual process of placing an order through this platform was time-consuming and required significant staff hours for data entry and follow-up.  

We developed a specialized automated SIM ordering system that handles the entire SIM card ordering process from initial form submission to invoice collection. Our system runs on a scheduled basis to check for SIM availability and triggers ordering without manual intervention.

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The challenges

Before automation, the client's SIM card ordering process from Hot Mobile relied heavily on manual work. Staff members had to individually process each order through Hot Mobile's website, making bulk ordering time-consuming and prone to errors.

The manual process created several operational hurdles, like:

• Hand-filled forms led to frequent errors, like incorrect SIM details and wrong shipping information.

• The client ended up submitting a few duplicate orders due to a lack of proper tracking.

• Without a proper system in place, the team struggled to monitor inventory, place orders, and handle invoices.  

• Their team had to manually check Hot Mobile's system for SIM card availability, leading to frequent stockouts or delayed orders.

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Final words

Automated order processing systems eliminate human errors, boost operational efficiency and simplify the entire order management process. With a customized solution designed for your specific needs, you can turn a demanding manual task into a reliable, automated workflow that allows your team to focus on more strategic activities.

At Relu Consultancy, we specialize in designing automated workflow management solutions that work with your required platforms to streamline operations and deliver faster turnaround times.  

Get in touch with us today to support your growth with smart, scalable automation.

How did we help

We designed an automated workflow that interacts with the Hot Mobile website to streamline the process of ordering new SIM cards. It handles the complete form-filling process across multiple web pages, ensuring accurate data entry and compliance with Hot Mobile's ordering requirements.  

Our solution also automatically collects invoices upon order completion and distributes them to relevant stakeholders for documentation. Built with scheduling capabilities, the system regularly checks SIM availability and places orders independently, eliminating the need for human oversight.

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Key features of our solution

Our automated tool comes with the following core capabilities to simplify the process of order handling:

• Web interface automation: Built with Selenium and Python, our tool creates a robust automation framework that seamlessly integrates with Hot Mobile's website.

• Smart form completion: The system automatically populates data across Hot Mobile's five-page order system, maintaining data accuracy and adhering to the platform's requirements.

• Scheduled monitoring & ordering: Using cron jobs, the system performs regular checks on SIM card availability. When needed stock is detected, it automatically initiates the ordering sequence.

• Invoice retrieval & sharing: After order completion, the system automatically collects invoices and shares them with team members for record keeping.

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The Results

With our automated solution, the client enhanced efficiency and accuracy in order management. By eliminating manual processes, the team reduced the time spent on order research and placement. They also minimized errors in order as the system maintained consistent accuracy in form filing.  

Our system even prevented supply delays and improved inventory management. With orders being placed automatically as soon as stock became available, our system ensured timely procurement of SIM cards. The automated data retrieval and sharing of invoices made record-keeping more efficient, making sure all key authorities had access to invoices.

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Project Overview

Social media has changed how businesses interact with customers - it gives them a chance to offer their customers services round the clock.

At Relu, we developed a social media DM marketing tool to help our client with their outreach efforts across platforms like Instagram, Pinterest, Reddit, YouTube, and Twitter.  

Our solution automatically identifies users with genuine interests in clients' products and engages them through personalized messages created with AI. The tool also handles engagement activities like following accounts, liking relevant posts, and leaving thoughtful comments.

With our automated tool, the client now saves time on manual outreach and can focus on core business tasks. The system works continuously in the background, building connections with potential customers 24/7.

The Challenges

Managing social media conversations demands significant time and effort from businesses. Finding potential customers, sending messages, and maintaining engagement across platforms can be challenging to handle individually.

When our client approached us, they were facing challenges like:  

• Keeping up with regular social media activities (liking, commenting, and following) across multiple platforms was time-consuming.

• Their generic messages resulted in low response rates and few conversions.

• Teams spent excessive time managing DMs and engagement tasks manually.

• Poor targeting meant they often reached users with no interest in their products.

• Manual limitations prevented them from expanding their outreach to reach more social media users.

To Sum Up

Our client's success with the social media DM tool shows how automation can help businesses effectively manage their social media presence. The solution helped them identify and reach the right audience with personalized messages, while its data-driven insights allowed them to adjust their strategy for better conversions.

At Relu, our purpose is to create solutions that address your business challenges and drive growth. Our expertise in automation and data scraping helps us develop solutions that scale and adapt to your specific business requirements, whether it's social engagement or data collection.

Looking to solve a specific business challenge? Let's talk about how we can help.

How Our Solution Helped

We created an automated AI-driven DM solution that assisted our client's social media outreach across different social media platforms. The tool easily finds and connects them with users who will be a good fit for their products. It crafts unique and relevant messages tailored to each user to foster relations with them.

What makes our solution particularly valuable to clients is its ability to build real connections. Beyond sending personalized messages, it follows users, likes their posts, and adds relevant comments on their content, much like a human social media manager would do. This helped our client build a more authentic social media presence.

Their marketing team also benefited from the tool's flexibility. They could quickly adjust their outreach levels and choose which platforms to focus on, making it simple to adapt their strategy based on what worked best.

What Made Our Social Media DM Tool Different

Our AI-powered DM automation tool comes with the following key features:

• Advanced AI-Powered Personalization: The tool uses NLP to write personalized messages that match each user's interests and how they interact on social media.

• Multi-Platform Automation: Seamlessly works across multiple social media platforms, automating outreach and engagement across different channels.

• Smart Targeting & Segmentation: Finds and groups users based on their genuine interest in your product, helping reach people who are more likely to become customers.

• Automated Engagement: Automatically manages following, liking, and commenting to build a more organic and authentic brand presence.

• Easy-to-Use Controls: Gives marketing teams simple ways to adjust their outreach settings on different social platforms based on their objectives and performance.

The Results We Achieved

With our social media DM tool, our client significantly improved their marketing and outreach campaigns. They were able to identify and connect with potential customers with less effort. Instead of spending hours manually searching for and messaging potential customers, their team could focus on converting the interested leads that the tool brought in.  

The AI-generated messages, along with automated engagement actions like following and liking posts, helped create a more authentic brand presence across all platforms.

By automating these outreach tasks, our tool also helped the marketing team save significant time, so that they could redirect toward creating strategies and building deeper customer relationships. With its user-friendly interface, they were easily able to adjust their approach across different platforms, directing efforts where they saw the best results.