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The challenge

Sponsorscout faced the challenge of automating web crawling to find newsletters, podcasts, events, and other sponsored content from a diverse range of organizations. Turning thousands of newsletters, watching tons of videos, and keeping track ofcountless events would consume unimaginable man-hours and prove unsustainable. They sought an automated mechanism that could deliver exact results in minimal time, with reduced costs and efforts.

Process

• We initiated the content aggregation process using the Feedly API. This versatile API enabled the automatic extraction of a multitude of newsletters, podcasts, events, and digital content from various sources.
• With the content in hand, we introduced Google Vision API, a robust image analysis tool. It meticulously detected and interpreted elements within images and videos, enhancing our ability to identify sponsor mentions within visual content.
• Google OCR was employed to convert textual information from images and scanned documents into machine-readable text. This tool facilitated text-based analysis and the extraction of valuable information from visual content.
• Google Entity Recognition further enriched the extracted data. It intelligently recognized and categorized entities like names, dates, and locations within the text, enhancing the overall accuracy and structure of the information.
• To fortify the database, we integrated the Crunchbase API. This versatile API provided access to comprehensive information about companies, funding rounds, leadership teams, and more. It empowered us to incorporate accurate and up-to-date company data into the database.
• The n8n Workflow Automation platform allowed us to seamlessly connect and coordinate the various applications, services, and APIs involved in the workflow.
• The extracted and organized data found its home in Airtable, ensuring easy accessibility, storage, and collaboration on the amassed information.

Outcome

With the n8n and make.com automation, our client achieved a continuous and ever-growing list of sponsors from across the web. The data was stored in Airtable, making it universally applicable and allowing easy access and analysis

Conclusion

Using n8n combined with other powerful tools such as Feedly and Google OCR proved to be a game-changer for gosponsorscout.com. Complex and labor-intensive tasks were
effortlessly automated, providing a comprehensive and accurate database of sponsors. The capabilities of n8n and make.com are vast, empowering us to create tailored automations for
countless use cases, meeting the diverse needs of our clients. If you are looking forward to automating tasks involving an organized and structured approach to data, we can help you with our immense expertise with these tools.

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The Challenge

The client had a substantial volume of scanned financial documents from which specific data—Name, Date, and Amount—needed to be extracted accurately. The process was initially manual, proving to be time-consuming, prone to human error, and inefficient for the increasing workload. Furthermore, organizing the extracted data in a systematic manner for easy access and reference posed another major challenge.

For instance, in one month, our solution processed 10,000 documents, with an impressive data accuracy rate of 99.5%. This was a 75% reduction in processing time compared to their previous manual method.

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Conclusion

This case study demonstrates the potent efficiency and accuracy of our data scraping solution in handling large volumes of scanned financial documents. By automating data extraction and organization, we were able to significantly reduce processing time, increase data accuracy, and streamline the document retrieval process. Our solution provides a compelling answer to similar challenges faced by financial institutions and serves as a ready model for future scalability.

Solution

Our team developed and implemented a sophisticated data scraping solution tailored specifically for scanned financial documents. First, the client collected all the relevant documents and provided us with their scanned copies. We then used our solution to scrape the required data. Using advanced data recognition and extraction algorithms, our system was able to identify and extract the necessary information—Name, Date, and Amount—from the various documents.

Once the data was extracted, the solution’s next task was to sort the documents accordingly. We implemented an automated system to create specific folders based on the Date, allowing for systematic organization of the documents. Each scraped document was then saved in its designated folder.

Results

The results of implementing our data scraping and sorting solution were immediately evident and overwhelmingly positive. The client was able to process a significantly larger volume of documents within a short time, with a notable increase in the accuracy of data extraction, eliminating the possibility of human error.

Our solution’s organization feature also proved invaluable. With each document being automatically sorted and saved in a designated folder according to the Date, the client was able to easily access and reference the scraped documents, enhancing their operational efficiency.

The Challenge

NAME.com boasts an extensive set of filters, sub filters, and sub selections, making the process of reaching the final list of cars a multi-layered task. Users must navigate through a cascade of filter choices, from the basic options like make and model to complex decisions regarding annual mileage, lease length, upfront payments, and finance types. Manually extracting data from NAME.com’s intricate filter system consumed substantial time and resources for our client. They sought a custom-built tool that could scrape data swiftly, taking into account multiple sets of specific filter combinations.

About NAME.com: The platform from which data was to be scraped

NAME.com stands as a leading online platform in the United Kingdom, dedicated to transforming how consumers discover and lease vehicles. The platform’s mission revolves around simplifying the intricate world of car rental services, making it accessible and convenient for individuals across the UK. NAME.com empowers users with an array of filters, allowing them to  pinpoint their perfect vehicle. These filters include Make & Model, Monthly Budget, Lease Duration, Fuel Type, Body Type, Transmission, Features & Specifications, Colour Preferences, Lease Types, and more.

Specific Requirements

1. Streamline Data Extraction: Our client required a tool to retrieve car data without relying on external APIs or paid tools and wanted a tool that was custom coded from scratch.
2. Navigate Complex Filters: The scraper had to navigate through NAME.com’s intricate filter hierarchy and the tool to replicate the process of selecting filters as is done by normal users.
3. Speedy Results: Despite the vast data, the client needed quick scraping results.
4. User-Friendly Interface: Rather than code scripts, the client wanted a user-friendly web interface to access the tool and obtain data with a single click.

The Output & The Process

We delivered a user-friendly web page with a pre-filled table of filter values, aligning with the client’s frequently used selections. Client could simply click a button associated with each filter set to initiate data scraping. Our tool replicated the manual filter selection process in the background while swiftly presenting results in Excel format on the front end. Separate buttons allowed users to scrape data for the current date or the past 30 days. The final Excel sheet included a wealth of data about vehicles falling under the selected filter set. It encompassed details such as make, model, trim level, model derivative, finance type, pricing for the first, second, and third positions, and providers of the vehicle for the top three positions. This saved the client hours of manual scraping, streamlining the process of accessing vital data.

Conclusion

Our custom tool successfully tackled the complexities of multi-level, multi-filter data scraping, simplifying a formerly labour-intensive process. This achievement demonstrates our capacity to develop similar tools for diverse businesses, facilitating highly intricate scraping tasks within minutes. For businesses aiming to optimize data extraction, our expertise can pave the way for enhanced efficiency and productivity.

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The Challenge

The client required a targeted data extraction tool that could scrape a website listing all internet providers according to zip codes. Their focus was on three main data points: the state in which the internet providers operated, the population covered by each provider, and the maximum speed offered. In addition, they needed detailed information about the company’s size, revenue, and the number of employees. The challenge lay in accurately scraping the required information and organizing it in an accessible, clear, and useful manner.

Our Solution

To meet the client’s needs, we developed an advanced internet provider scraper tailored to their specific requirements. The tool was designed to search the targeted website, extract the relevant information as per the client’s filters, and present the data in an organized Excel sheet.

The scraper was built to capture key data points such as the state of operation, population covered, and maximum speed offered by each provider. Additionally, it was programmed to gather critical business intelligence, including the company’s size, revenue, and employee count.

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Results

The outcome of our solution was transformative for the client. Our scraper significantly reduced the time spent on manual data gathering, resulting in a 80% increase in efficiency. The scraper was able to systematically extract data for over 1,000 internet providers within a short period, presenting accurate, insightful data in an easy-to-analyze format.

By using the scraper, the client could now perform a comparative analysis of various internet providers. This detailed comparison allowed them to make informed business decisions based on data such as population coverage, maximum speed, company size, revenue, and employee count.

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Conclusion

This case study stands as a testament to our expertise in developing tailored data scraping solutions. Our tool empowered the client with data-driven insights, enhancing their operational efficiency and strategic planning. It is our commitment to continuously deliver innovative digital solutions that drive business growth and success. Let us help you unlock new opportunities and propel your business forward.

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The Challenge

• Diverse NGO Services: NGOs offer a myriad of services ranging from medical assessments, legal aid, language instruction, to programs related to gender-based violence. Understanding the breadth and specificity of these services was a challenge.
• Language Barriers: With programs offered in multiple languages like English,French, and Russian, it was essential to ensure the tool could cater to various linguistic groups.
• Effective Matching: Individuals seeking support often struggle to find the right NGO program, particularly if they lack resources. It was crucial to develop a tool that could accurately match a person’s needs with the right service.
• Data Compilation: With vast amounts of data scattered across different NGO websites, the client faced the challenge of extracting, compiling, and presenting this information in a user-friendly manner.

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The Process

• Data Extraction: The client’s tool was designed to crawl various NGO websites and extract pertinent information about the diverse programs they offer.
• Algorithm Development: An advanced matching algorithm was developed to efficiently pair individuals with suitable NGO programs based on their profiles.
• Feedback Loop: The tool incorporated a feedback mechanism to continually refine its matching process, ensuring greater accuracy over time.

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The Output

• Comprehensive Database: The tool successfully compiled a vast database of NGO programs, categorized by service type, language, eligibility criteria, and more.
• Efficient Matching: Individuals in need could now find the most suitable NGO programs in mere seconds, ensuring they receive the assistance they require.
• Community Benefits: By connecting individuals to free or low-cost programs, the tool ensured that more people could access essential services, leading to stronger, more resilient communities.
• Lead Generation: The tool also served as a lead generation platform, offering the compiled data at affordable rates for various stakeholders in the NGO sector.

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Conclusion

Our client’s innovative tool successfully addressed a significant gap in the NGO sector by efficiently connecting individuals in need with the right resources. By leveraging technology, the tool not only streamlined the process of finding appropriate NGO programs but also created a platform that could evolve and adapt based on feedback and changing societal needs. This case study underscores the immense potential of digital solutions in addressing complex societal challenges and paves the way for more such innovations in the future.

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The Challenge

Our client faced a significant hurdle in extracting valuable leads from vast amounts of multilingual data that they generate regularly. To overcome this challenge, they approached us with the need for a tool that could efficiently translate content from different languages, identify key entities, and then re-translate them into their original language for verification.

The Process

Our solution involved a comprehensive process that  seamlessly integrated with the client’s workflow:

1. Translation and Entity Extraction: The tool efficiently translated content from various languages into English, preserving the original meaning. It also systematically identified key entities from the data, making it highly
   adaptable.
2. Noun Extraction in English: Following translation, the tool systematically identified nouns in the English data. This step was crucial in extracting names and company information from the content.
3. Translation back to original language for Verification: The extracted
   names and company details were then translated back into it’s original language. This step served to verify the accuracy of the information in the original context.
4. Customization for Multilingual and Varied Data: The versatility of the
   tool was a key feature. It could be customized to function with any language, allowing the client to adapt it to various markets. Furthermore, the tool seamlessly processed data in different formats, providing flexibility in its application.
5. Information Extraction: Once verified, the tool efficiently extracted valuable information, including leads, from the processed data. This step ensured that the client could gather meaningful insights and potential business opportunities.

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Output

The output of our tool was twofold. Firstly, it successfully addressed the client’s immediate need by providing an efficient means of lead generation from multilingual data. Secondly, the tool’s customization feature opened up possibilities for its application in diverse linguistic and data environments, offering the client a scalable and adaptable solution for future challenges.

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Conclusion

In conclusion, our tailored tool not only met the client’s specific requirement for lead generation from multilingual data but also demonstrated its potential for roader applications. By leveraging systematic entity extraction and versatile language translation, we created a powerful tool that empowers our client to unlock valuable insights from a wide range of multilingual and varied data sources. This case study serves as a testament to our commitment to providing innovative solutions that align with our client’s evolving needs.