Automating Large-Scale Data Entry with AI and n8n for Buzdrinks

Buzdrinks came to webdew to update their huge amount of data into Hubspot CRM for better sales strategy and market consultation to their clients.

The Goal

Buzdrinks came with specific goals, they wanted the correct data into their CRM for accurate analysis.

• Automate Property Updates – The client wanted a system where all the important details (custom properties) for their companies inside HubSpot would get updated automatically. Instead of someone manually adding or changing these details every time, the automation would handle it in the background, saving time and reducing mistakes.

• Competitor Tracking – They wanted to keep an eye on competitors by regularly collecting information about them from different websites. This way, they could know what others in the industry are doing, like new products, services, and respond quickly.

• Ongoing Batch Processing – The client didn’t just want to check one or two companies at a time. They needed a process that could handle many websites in one go, starting with 10 at first, then scaling up to 20, and eventually even 100 or more. This meant the system had to be built for growth without slowing down.

• Data Accuracy Checks – It wasn’t enough to just collect the data, the client wanted to be sure the information was correct and up-to-date. The automation would include built-in checks to verify that the collected details were accurate before updating them in HubSpot.

The Approach

Going through rounds of understanding, webdew found the approach best suited for the problem. This is how we did it.

1. Starting with Two Excel Sheets

 The project began with two separate Excel files: Company List: A file containing all the company names and core details. Record Details: A file with additional data points for each company, such as contact information, performance metrics, or other relevant attributes. The challenge was to merge and upload this information into HubSpot in a structured way. Given the large size of the datasets, this task would have been extremely time-consuming and prone to mistakes if done manually.

2. Need for Automation

We knew that this wasn’t going to be a one-time job, more data would keep coming in regularly. This meant building a process that could run on its own, without someone having to repeat the same manual steps every week or month. An automated system would: Save a huge amount of time, reduce the chances of human error, and ensure that updates happen consistently without delays.

3. Creating a Prompt in Claude

To handle the data entry intelligently, we designed a custom prompt for Claude (an AI model). This prompt wasn’t just about typing data into fields, it had specific instructions to: Validate the data: Check if the information was complete, correctly formatted, and relevant. Filter out noise: Remove any unnecessary or redundant data that didn’t need to be stored in HubSpot. Prepare for upload: Organize the cleaned data in the right structure for HubSpot to process it smoothly.

4. Limitations of Claude for Large Data

When we tried feeding Claude the entire dataset at once, we ran into limitations. The model couldn’t handle such a massive amount of data in a single request, it either slowed down, returned incomplete results, or failed entirely. This made it clear we needed an intermediate step to make the workload manageable.

5. Introducing n8n for Batch Processing

To solve the scaling problem, we brought in n8n, an automation tool that can connect different services and break big tasks into smaller ones. n8n took the large datasets and divided them into smaller, more manageable batches. Each batch was then sent to Claude for processing, cleaning, validation, and preparation. Once processed, n8n pushed the cleaned data into HubSpot automatically. This approach allowed the system to run continuously without hitting Claude’s data limits.

6. Result

The final setup combined Claude’s intelligent data processing with n8n’s automation and batch-handling capabilities. This gave the client a scalable, future-proof process that: Automatically processes and uploads new data as it comes in. Ensures all data is accurate, relevant, and ready for HubSpot. Eliminates the need for ongoing manual data entry.

The Challenges

It wasn’t all easy, there were many challenges along the way, and solving them quickly was necessary. If the wrong data gets updated in the CRM, the entire analysis can be affected.

• Very Large Amount of Data – The client had two big Excel sheets with hundreds of entries, making manual data entry into HubSpot almost impossible.

• Ongoing Data Updates – It wasn’t a one-time job, new company records would keep coming in, so the process had to handle future updates automatically.

• Data Quality Issues – Some of the information was incomplete, incorrect, or repeated. This meant we needed a way to clean and validate the data before uploading it.

• Claude’s Processing Limitations – Claude (the AI tool) couldn’t handle such a huge amount of data in one go, causing slowdowns and incomplete results.

• Need for a Scalable Solution – The system had to work for small batches at first (10–20 websites) but also scale to much larger batches (100+ websites) without breaking.

• Risk of Errors Without Checks – Without proper validation, incorrect or irrelevant data could easily get into HubSpot and mess up reporting or decision-making.

Results

The results were positive. The client was impressed with the work that we did. The automation streamlined the entire data entry process, ensuring only clean, verified information was added to HubSpot. This saved hours of manual work and gave the client reliable, up-to-date data for better decision-making.

• Fully Automated Data Entry – The entire process of uploading company records to HubSpot became automated, removing the need for manual work.

• Scalable Batch Processing – The system could easily handle both small and large datasets, starting from 10 websites and scaling to 100+ without performance issues.

• Cleaner, More Accurate Data – Built-in validation and filtering ensured that only complete, relevant, and error-free data made it into HubSpot.

• Time and Effort Savings – What would have taken hours or even days manually was now done automatically in the background, freeing up team resources.
  
• Future-Proof Setup – The solution was designed to keep working for new incoming data, no extra setup needed every time.

• Better Competitive Insights – With accurate and regularly updated records, the client could track competitors more effectively and make faster business decisions.

Conclusion

This project showed how the right mix of AI and automation can turn a time-consuming, repetitive task into a smooth and scalable process. By combining Claude’s ability to clean and validate data with n8n’s power to handle batch processing, we created a system that not only solved the immediate challenge of uploading large datasets but also worked for future updates without extra effort. The client now has accurate, well-organized data in HubSpot, better visibility into competitors, and more time to focus on strategic work instead of manual entry.

Looking to save time, improve accuracy, and scale your processes? Let webdew help you combine AI and automation to get the job done right, every time. Get in touch with us today to start building smarter solutions for your business.