The sales productivity platform SMBs and startups actually love.

Project Overview

Sales is a game of chance, and more than luck, it is skills that can convert a lead into a potential client. In this age of automation, businesses should play smarter rather than forcing sales reps to work hard on repetitive and monotonous tasks. Introducing an automated solution can increase the productivity of every sales operation, like capturing leads.  

For instance, this client thought of improving the workflow of its sales team by streamlining the real-estate leads capturing process and reducing the time wasted in completing the manual obligations.  

As a leading automated data extraction service provider, we specialize in building solutions that reduce manual efforts, save time, and enhance operational efficiency. Let’s understand how we tackled the challenges in capturing leads and streamlined the process with an automated lead-capturing solution.  

Challenges

The client deals with the Procore platform to extract information about the leads. However, manually extracting data from a platform like Procore is challenging because of the availability of complex and voluminous data. Here are the challenges that the client was dealing with:

1. Procore handles large-scale project data with numerous stakeholders and timelines, making manual real-estate company data extraction time-consuming.  

2. The platform’s data is organized into several layers, such as project categories, budgets, and team information, and it requires significant effort to navigate, extract, and process it.  

3. The real estate industry is dynamic, and real-time updates are necessary to take quick action. However, the absence of real-time features often led to delayed updates, which ultimately came at the cost of lost leads or prompt acting on new opportunities.  

4. Manually captured data often lacked standardization, which made the analysis much more difficult. Also, manual data extraction was prone to inaccuracies and issues like types, missing fields, and incorrect categorization when entering the data into CRM and lead management solutions.  

5. Extracting, organizing, and validating data manually required significant time and effort, and this time could have been utilized for more productive activities.  

6. The access level of projects on the Procore platform varied, and extracting data for some projects was cumbersome.  

Leverage the Benefits of Automated Sales Lead Generation Solution in Your Operations

According to reports, marketers who have implemented automation tools and processes in their workflow have experienced an increase of 451% in getting qualified leads. An automated lead generation solution can introduce the following advantages in any organization’s marketing operations:

• An automated workflow of extracting lead data, cleaning and processing the dataset, and feeding it to the lead management tool helps save time by curbing the need to complete tasks manually. Sales reps can use this saved time to focus on building solid conversion strategies and fetching more clients.  

• Strategic and robust API integrations help data flow seamlessly from one system to another. Hence, it reduces the chances of errors that might happen when manually entering data about leads from one platform to another.  

• Automation frees the sales team from completing monotonous, repetitive tasks that are essential to ensuring continuity of operations. It allows the sales reps to focus on important tasks, like closing deals and strengthening the relationship with clients.  

• The automated data extraction solution is scalable and flexible, and it grows with your company's operations. Regardless of the increasing volume of datasets, the solution adapts seamlessly to your growing needs while reducing manual efforts and operational costs.  

Conclusion

It is essential to remember that capturing leads is only half the battle; your sales team also needs to focus on converting them. That’s why introducing an automation tool simplifies the capturing part, and your team can focus on building innovative strategies and tactics to convert potential leads into promising customers.  

Our Strategy to Extract High-Quality Lead Data From Growing Database

Here’s what our experts did to automate the entire lead-capturing process:

1. Data Scraping: Firstly, using Python and Procore’s API, relevant datasets to the real estate companies were extracted. We ensured that all the extracted data was structured in nature while complying with industry regulations and ensuring data accuracy.  

2. Data Storage: The extracted data was stored in AWS, and it was processed to undergo a transformation using an advanced tool. Given the growing data needs, it was essential to choose a storage platform that could easily scale up without affecting performance.  

3. Lead Management System Integration: The processed data was fed to Apollo.io, an all-in-one lead management platform, using Python-based API integrations. This enabled automated lead generation and ensured accurate data mapping.

4. Salesforce CRM Synchronization: Leads generated from Apollo were synced with the Salesforce CRM platform to enable centralized management of customer data and reduce the sales team's manual efforts.  

Results and Impact

Within months of deployment of the automated lead-capturing solution, the client witnessed an increase in the productivity of the sales team and revenue growth. Let’s understand the impact:  

• The volume of high-quality sales leads increased by 35%.  

• By automating the workflows, the efficiency of the sales team was boosted by 40%.

• 30% decreased the time invested by sales reps in capturing leads.  

• The average time to convert leads reduced by 25% and the conversion rate with this solution jumped to 20%.  

Project Overview

Podcasts have become a lucrative marketing tool. There are over 464 million podcast listeners worldwide, and the number is still growing. These listeners spend an average of 7 hours per week listening to their favorite podcasts.  

Thanks to modern technology and podcast tools, it is easy to produce and distribute podcasts, and businesses can reach out to their audience who prefer to listen. Brands can create episodes around their products that can subtly drive engagements to meet the KPIs.  

By repurposing visual content, the process of podcast generation can be automated end-to-end. One of the businesses aimed to repurpose YouTube content into podcasts, and with the help of advanced data web scraping tools and AI, our team created a streamlined workflow that converted the YouTube scripts into podcasts with natural, human-like voices.  

By extracting and refining scripts from YouTube, our automated system reduced manual efforts, accelerated production, and repurposed the content while ensuring consistent quality. Let’s understand the challenges faced by the client and how we found a solution to create a podcast using YouTube scripts.

Challenges Faced by the Client in Podcast Generation

When it comes to podcast generation using YouTube transcripts, it becomes challenging to create high-quality and polished podcasts if one relies on manual methods. Traditional workflows make it difficult to achieve a steady production pace.

Here are some challenges that the client faced that led them to opt for a solution that automated podcast generation from video transcripts:

1. Time-Consuming Process: The traditional method of podcast generation includes comprehensive steps, like extracting the data from YouTube scripts, editing the content to refine and make the script more engaging for audio platforms, and recording voiceovers. This manual method slows down content delivery and scalability.
2. Inconsistent Quality: Manual transcription and content editing can lead to inconsistencies in tone, structure, and overall quality, which can affect the overall listener’s experience.
3. High Production Costs: Hiring professionals for editing and voiceover adds substantial cost to the podcast generation process. Besides, there is a cost associated with investing in high-quality equipment and software tools to record and edit the audio content, and maintenance costs increase the operational costs as well.
4. Limited Scalability: The manual process makes simultaneously producing highly polished products unfeasible. However, with the help of automated solutions, businesses can achieve that pace and meet the audience's increasing demands.
5. Repurpose the Content: Extracting insights from YouTube transcripts and adapting them to audio-only format is a tedious process. The absence of an automated solution reduces the ability to maximize the value of existing video content.

How Automated Podcast Generation Solution Can Benefit Your Organization?

According to Notta, it takes around 10 hours to transcribe an hour-long transcript. This is where an automated solution comes into play, as it replaces manual intervention with automated workflow. The system works in parallel to process all the YouTube links and generate transcripts in a formatted manner to be fed to an AI tool to improve them.  

Besides, AI tools can easily modify the transcript to make it more conversational-friendly and impactful. This ensures that listeners listen to the podcast until the end and that the content is repurposed efficiently without any plagiarism issues. Besides, the system is easy to scale, so it can handle a large volume of links without any issues in output quality or turnaround time.  

So, if you are a business that’s interested in re-purposing its video content and utilizing the opportunity that audio content has to offer, an advanced data scraping and extraction solution with automated podcast generation should be in your arsenal. This will help you achieve your marketing KPIs without increasing your operational costs or hiring a specialized workforce.

Technology Used For Building Automated Podcast Generation Solution

YouTube API | NotebookLM | Python | Google Drive API | Open AI

Our Strategy to Convert YouTube Scripts into Engaging Podcasts

As an expert in providing data extraction and automation services, we design workflows that can navigate the most complex websites for podcast generation, like YouTube to generate audio content. Here’s how we created podcasts using YouTube scripts:

1. Input Data Collection and Video Transcription Extraction
   Firstly, we gathered all the YouTube links from the client that they needed to convert into podcasts. All the links were given in the Google Sheet with YouTube links and additional data, like the video’s title and channel’s name.
   Then, we worked on a system that read the links and utilized YouTube API with other web data scraping tools to scrap the video transcriptions. It is based on batching logic, so all the video links were efficiently processed in batches and easily handled the large volume.
2. Saving Transcriptions as .docx Files
   After the transcriptions were extracted, the system formatted the texts with titles, paragraphs, and timestamps, and all this data was saved in an individual .docx file. Every transcription was saved in the .docx format, and these files were stored in folders in an organized manner, either locally or on a server.
3. Uploading Files to Google Drive
   Now, the system uploads the .docx files to a specific Google Drive folder using the Google Drive API. The folders are organized either by title or channel name to make it easy to access the files. The transcripted files are processed using AI to refine and enhance the conversation and generate high-quality podcasts.
   Once the conversations were improved using AI tools, they were fed to NotebookLM, which converted the transcripts into highly polished podcasts with a human-like feel.
4. Automated Organization and Generation of Podcasts
   This entire process was automated end-to-end, and as the new video links were uploaded to the Google Sheet, our system initiated the transcription and data extraction process. The transcripts were extracted, data was formatted, and it was then stored in .docx files, which were enhanced using AI. Then, all the improved conversations were translated into audio content using NotebookLM, converting text into speech.

Results and Impact

This automated solution makes it possible to speed up the podcast generation process, reducing the turnaround times and giving the ability to pace up the production output. All this while ensuring consistent quality. Here’s how our advanced solution helped the client:

• Reduced the production cost by 50% as the entire process of podcast production was automated.  

• The number of podcasts generated in a month increased by 60% without at the expense of increasing the workforce or resources.  

• Saved around 80+ hours of manually extracting the data, editing it, improving the content, and converting it into audio files.  

• The listener retention rate was boosted by 35%, and the overall completion rate of podcasts was 70%.  

• The average time to market to convert the transcript into highly polished text was reduced by 75%, allowing the client to meet audience demands.  

Real-time tender monitoring is essential for all organizations to stay competitive in the fast-paced market. Our dear client faced challenges in identifying and responding effectively to Brazil government tenders.

To address this issue, we delivered a real-time tender monitoring solution with automation and web scraping. The system we used seamlessly captured, categorized, and analyzed relevant tenders from multiple sources, including government portals while sending on-time tender alerts and notifications.

This approach has helped our clients act promptly on critical opportunities. Let’s examine the problem statement and how Relu enabled real-time tender monitoring and data extraction.

Challenges faced by the Client

Lack of real-time updates—Without an efficient real-time tender monitoring system, the client was dependent on a manual process. This caused a delay in identifying and responding to tenders. Tight deadlines or missed submissions have become common scenarios.

Inefficient tender discovery

Existing tools were insufficient for tracking Brazil's government tenders. The reason was the usage of limited keyword searches. This caused the overlooking of opportunities in niche and regional markets.

Complex tender data extraction

Tender information came from different sources in different formats. Manual tender data extraction and preparation became tedious and error-prone. This process lacked scalability causing inefficiencies.

Lack of competitive insights

The client’s inability to monitor competitors' activities and strategies prevented them from optimizing their bids for competitive tenders.

Resource intensive workflow

Manual methods for web scraping for tenders require time and effort, which reduces the team’s ability to focus on strategic tasks.

Recognizing these challenges, we developed a solution that includes automation, tender alerts and notifications, and data-driven insights.

Our solution to the problems

We developed a tailored real-time tender monitoring system to address all the client's challenges. It was designed to simplify tender discovery and management. A deeper look into the solution:

Real-time tender monitoring

We developed a system capable of monitoring live updates for Brazil government tenders. It ensured that the client had access to the latest opportunities as soon as they were published. This removed delays caused by manual tracking and helped the client stay competitive.

Automated tracking

The manual tracking of tenders was replaced with an automated tender tracking process. This automated tender tracking identified, captured, and categorized tenders from different sources. This automation reduced dependency on human intervention, saved time, and reduced errors.

Web Scraping

Advanced web scraping techniques were employed to extract tender data directly from government portals and other relevant websites. This system was built to handle diverse formats and data sources. It ensures comprehensive coverage and accuracy. Scalability was incorporated to accommodate an increased number of volumes.

Tender data extraction

A robust framework for tender data extraction was implemented. This framework organized the information into a centralized repository. Tenders were automatically categorized based on factors like keywords, industry, geographic location, and submission deadlines. This helped the client prioritize the opportunities effectively.

Tender Alert and Notification

A notification system was implemented to deliver instant tender alerts via email or SMS. This ensured that the client was immediately informed about new opportunities, submission deadlines, and status changes.

User-friendly interface

We created an impactful dashboard that gave a consolidated view of all the tenders, analytics, and filters for better decision-making; this helped clients search, sort, track tenders, and gain insights about market trends by staying competitive.

Results and Impact

Our solution transformed the client’s tender management. With real-time tender monitoring and automated tender tracking, they could identify and respond to tenders faster, increasing their win rate significantly.

This dashboard prepared by the Relu team is not limited to providing a solution for this client. Any business or individual seeking an efficient tender management solution in any sector can benefit significantly from this platform.  

Project Overview

In today's digital era, our first instinct is to turn to the web to look up movies and shows. It offers endless information - from ratings and cast details to reviews and more. With this valuable data, writers can easily brainstorm new creative ideas and write content for stories that could become the next blockbuster.

Recently, a prominent author and film enthusiast approached us with a specific goal: building a comprehensive database of films featuring disability themes.

Our team developed an AI-powered data extraction solution that systematically collected and organized data from different authoritative sources, including Wikipedia, IMDB, and Google search results.

About the Client

As a writer in film and media research, our client wanted to study how disability is portrayed in movies. They needed to gather detailed film data - from cast information and accessibility features to audience reviews across different platforms - to support their creative process.

However, the manual collection of data was taking time away from their core work of writing. The client sought our services to automate this time-consuming process, allowing them to conduct more thorough research and come up with excellent content.

The Challenges

The client wanted to build a comprehensive database of films by gathering information from multiple platforms like IMDB, Wikipedia, and other Google search results pages. However, manual data collection from these various websites presented several challenges:

• Film platforms like IMDB and Rotten Tomatoes structured their data differently, making it time-consuming to find and extract relevant details from each place.

• The large volume of global film releases, including those focusing on disability, required constant monitoring for comprehensive coverage.

• Platform-specific search limitations like CAPTCHAs prevented third-party sources from scrapping data from websites.

• Differences in what qualified as a "film for disabled people" varied (e.g., films about disabilities, featuring disabled actors, or with accessibility features), creating complexity in data categorization.

• The platforms and the accompanying descriptions didn't specifically indicate if films included disability representation, making it difficult to identify and verify relevant content.

‍

Summing Up

Smart data collection can make any job easier - no matter how specific or complex. We helped turn endless hours of manual research into a smooth, automated process that actually worked.

That's what we do best at Relu: figure out clever ways to gather and organize data, whether you're studying movies, tracking market trends, or doing something completely different.

Got an interesting data problem? Let's solve it together!

How Did We Fix This?

At Relu, we developed an AI data scraping solution to collect film data from Google search results, Wikipedia, and IMDB. The solution was specifically designed to identify and collect information about films focusing on disability, ensuring accurate results.

Here's exactly how the process went:

• Used SERP modules to extract relevant film information from search engine results

• Applied AI-based validation to verify each film's relevance and accuracy before extracting information

• Implemented data formatting algorithms to convert information into a standardized and consistent structure

• Created an automated system to fill data gaps with appropriate placeholders, ensuring uniformity across the dataset

Key Features of Our Solution

Our automated data scraping system enhanced the client's research capabilities with these core features:

1. Advanced Metadata Scraping: Our system uses advanced SERP modules to gather film insights from multiple platforms, including Google search results, IMDB, and Wikipedia.

2. AI-Based Validation System: The solution employs AI to ensure the database only includes films that genuinely represent disability themes while automatically detecting and correcting inconsistencies.

3. Automated Data Structuring: Our system organizes the information into a standardized format, automatically structuring details like titles, release years, and filmmaker information while maintaining consistency.

4. Customization: The solution is specifically designed to focus on films with disability representation. It captures detailed insights about character portrayals, accessibility features, and more, providing valuable context for research and analysis.

Results

Our AI-driven automated data scraping solution helped the client with an in-depth analysis of disability representation in cinema. They could now easily access details about movie names, cast, release dates, accessibility features, and more.

Its AI-powered validation system enabled them to collect vital data from multiple platforms. Our advanced algorithms and automated filing ensured uniformity in how films were structured and represented.

Through automated updates, the client could efficiently track new film releases and update existing entries, keeping their research database fresh and updated.

The AI scraper transformed a time-consuming manual process into a streamlined system, providing our client with an effortless and reliable way to get insights in the film and media industry.

Project Overview

Filmmaker interviews serve as valuable resources for writers to understand cinematic history and filmmaking.  

Recently, a researcher came to us with an interesting challenge: they needed to gather every interview ever done with the legendary filmmaker Jacques Tati. The interviews were everywhere - scattered across websites, in different languages, some in text, others in video and audio. Manually collecting all this would have taken months of tedious work.

That's where we stepped in. We built a smart web scraping tool that could automatically find, collect, and organize all these interviews into one easy-to-use digital collection. Instead of endless hours of copying and pasting, our client could now focus on what really mattered - understanding Tati's artistic vision through his own words.

The Challenges

Our client wanted to gather all interviews of filmmaker Jacques Tati from across the internet. These came in different formats - text, audio, and video - and were spread out across many websites and languages. This made it hard to collect and arrange them in one place.

The client faced several major challenges:‍

• Websites used security tools like CAPTCHAs, which required advanced methods to overcome and extract data.

• Most interviews were protected by copyright and cannot be scraped or accessed without permission.

• Websites using JavaScript frameworks (e.g., React, Angular) dynamically load content, making it challenging to locate data in the HTML source.

• Different websites or pages structured interviews in various ways, requiring unique data collection methods for each platform.

• The quality of data wasn't consistent, as some platforms had incomplete or inconsistent information.

To Sum Up

Building this research archive showed that complex data challenges often need custom-built answers. At Relu, we specialize in crafting targeted solutions to collect, sort, and deliver information that fits each project's specific needs.

From handling multi-language content to processing different data formats, we adapt our approach to solve the problem at hand.

Ready to streamline your data collection process? We'd love to explore solutions together!

How Our Solution Helped

At Relu, we developed an automated web scraper script to locate and gather Jacques Tati's interviews across multiple platforms. Our solution worked around common data scraping obstacles and ensured the client could collect all available interview content.

The solution included: 

• A specialized web script that searched through Google results to identify interviews across text, video, and audio formats.

• Advanced AI validation systems to verify each interview's authenticity and relevance.

• Integrated translation and transcription capabilities to convert all content into English.

• Standardization protocols to organize all interviews into a consistent, unified format.

‍What Made Our Web Scraping Tool Different

Here's what our custom solution brought to the table:

1. Smart Search Functionality: A unique script that searched through Google search results to find Jacques Tati's interviews in text, video, and audio formats.

2. AI-Based Content Validation: It made use of AI to scrape website and checked each piece of content before extracting them to ensure that the collected data is of quality and relevance to the client.

3. Language Processing: The system comes with built-in translation and transcription tools that convert all gathered interviews into English, making the content easily accessible.

4. Standardization Protocols: The ability to standardize all collected interviews into a unified format, creating a well-structured and easy-to-navigate database.

Results

Our web scraping solution transformed how our client handled their research. We built them a comprehensive digital archive containing all of Jacques Tati's interviews, properly translated to English and verified for accuracy.

What used to take hours of manual searching and organizing now happened automatically!

The result was simple but powerful: our client could stop worrying about data collection and focus entirely on their research and writing. With a single, organized source for all of Tati's interviews, they could work more efficiently and be confident they weren't missing any important content.

Project Overview

From property listings to tenant preferences, the internet is home to valuable data. By accessing this information, real estate agencies gain comprehensive market insights. They can spot market opportunities faster, price properties more accurately, and better understand buyer preferences.

However, extracting and organizing this data from multiple platforms requires significant time and resources. Our company partnered with a leading Australian real estate agency to solve this challenge by developing an automated data scraping bot.  

The custom-built solution efficiently aggregated real estate data from three platforms: RealEstate.com.au, Domain.com.au, and Property.com.au. This tool automatically extracted various types of data, including property listings, agent details, suburb insights, and pricing trends.  

Additionally, it structured all extracted information directly into Google Sheets under proper sub-heads. By continuously updating data, our bot offered the client with a real time view of the real estate market to make strategic decisions on time.  

About the Client

In Australia's real estate market, staying ahead means being smart about how you work. The biggest names in property have grown by focusing on new ideas and sustainable buildings, using modern technology to attract both buyers and investors.

Data is what drives success in this competitive field. Having up-to-date information about properties, prices, and market trends helps real estate agencies understand what buyers want and stay competitive.

This is why one of our clients, a real estate agency, came to us for help with web scraping. They wanted to better understand their city's property market by collecting data about listings, prices, what types of homes people prefer, and which neighbourhoods are popular.

With these insights, they could improve their marketing and make sure they're offering the right properties to the right people.

The Challenges

The client wanted to obtain a 360-degree market view by combining property data from multiple real estate platforms.  

The issue was each platform had distinct search parameters and data structures which required advanced solutions to merge and clean listings while keeping valuable information from each source. During this process, they encountered several challenges. ‍

Technical Complexity

• Targeted real estate platforms employed sophisticated structures with dynamic, interactive elements that made data extraction harder using simple tools  

• Frequent updates in the website structures caused scrapers to break often and demanded constant maintenance  

• Scraping excessively led to IP bans or blacklisting from property platforms, damaging relationships with these sites ‍

Data Management Issues

• Identifying and merging duplicate listings across platforms while preserving unique data points in excel was hard manually  

• Organizing extracted data into multiple sheets based on varying parameters was complicated because of mismatched formats and fields  

• Continuously monitoring and regularly updating data in real-time was time-consuming without advanced scrap setups  

These challenges needed a sophisticated solution that could manage complex web structures, automated data extraction, and real-time organization - all while navigating the technical restrictions of multiple platforms.

Wrapping Up

The success of this project proved the transformative power to automate form submission. By using tools like Airtable and browser automation, businesses can reduce manual effort, improve accuracy, and scale their operations effortlessly.

If your organization needs a break from inefficient manual form submission, let us help you build a customized solution to automate your workflow.  

Partner with Relu Consultancy to transform your business challenges into opportunities. Our team specializes in building custom automated solutions that save time, reduce manual work, and deliver actionable insights.  

Whether you need data extraction, processing, or analysis, we'll create a tailored solution that drives your business forward.

How Did We Fix This?

Our team developed an automated data scraping bot to help the client with real-time data collection and analysis.  

The solution combined web scraping technology with intelligent data processing abilities that made it easy to access listings, prices, and market insights in one place from different sources.  

Here's what our team did:

• Employed a Selenium-based automation framework that naturally simulates human browsing patterns  

• Implemented enterprise-grade security features, including IP rotation, to ensure reliable and automated data collection  

• Built a Python-powered data pipeline for cleaning and structuring extracted information directly into Google sheets

• Developed smart duplicate detection algorithms to consolidate listings across multiple platforms while preserving unique details from each source

• Created a comprehensive Google sheet with dedicated sections to categorize data based on parameters like listing price, active property listings, suburb profile data, and more  ‍

‍Key Features of Our Solution

Relu’s custom-built automated data scraping bot transformed the client’s real estate data extraction through four powerful capabilities

1. Automated Web Scraping: Our powerful bot solution efficiently collects essential property data across multiple platforms simultaneously. The solution effortlessly handles dynamic elements and successfully bypasses anti-scraping mechanisms like CAPTCHA, ensuring effective data collection.;  

2. Data Processing and Transformation: The solution implements a robust ETL process that automatically cleans, merges, and organizes data within Google sheets. There are dedicated tabs in the sheets to track historical pricing trends, offering valuable market insights.

3. Hosting and Scheduling: Hosted on AWS infrastructure, the system operates continuously without manual intervention. This cloud-based approach ensures data remains fresh and relevant through automated updates.

4. Anti-Detection Measures: The solution employs residential rotating proxies and randomized browsing patterns to avoid IP blocks in data extraction. These strong measures maintain uninterrupted data scraping operations, ensuring consistent data flow.

Results

By partnering with Relu Consultancy, the client transformed their approach to market intelligence. Our custom-built real estate scraper bot delivered immediate, measurable improvements to their operations.

Through our solution, the client eliminated hours of manual data gathering, replacing it with automated, real-time data collection across multiple platforms. The scalable architecture we designed meant they could easily expand their data collection as their needs grew, while our intelligent processing system ensured the information remained accurate and actionable.

The impact was clear - with Relu's automated scraping technology, the client can now:

• Track property listings and price changes in real-time

• Spot neighborhood trends before their competitors

• Make faster, data-driven decisions about their property portfolio

• Adapt quickly to market shifts with reliable, up-to-date information

This strategic advantage helped them move from reactive to proactive market positioning, enabling them to capitalize on emerging real estate opportunities as soon as they arose.