Time is one of the most valuable assets in any business, yet it’s often wasted on manual, repetitive tasks.
🚀 Introduction
In one of my recent freelance projects, I developed a simple but powerful tool that saved my client—a professional from the engineering domain—over 10 hours of work in a single run.
The task? Creating thousands of PDF documents from rows of data in an Excel file. Manually, this was labor-intensive, error-prone, and repetitive. With automation, the same task is now completed flawlessly in minutes—and can be reused in the future at zero additional cost.
This post breaks down the problem, the design of the solution, challenges faced, and the impact it created. If you’re working on a repetitive task, building a portfolio of meaningful work or looking to improve your Python automation skills, you’ll find something useful here.
🔧 The Problem
The client had a standardized Word form—used to document and review products in a technical setting. Along with this, they had an Excel sheet with structured data, where each row represented a different product.
The manual workflow looked like this:
- Open the form.
- Copy-paste values (like product name, tag, panel, PLC, etc.) from the Excel row.
- Save the filled form.
- Repeat hundreds or thousands of times.
It would take the client 10 to 15 hours per batch. This wasn’t a one-off process either—it was recurring.
Besides being time-consuming, this process was:
- Mind-numbing and tedious.
- Prone to human error.
- Difficult to standardize or scale.
🛠️ My Approach
We knew the pain points, So proceeded to building a Python-based automation tool that would:
- Read the Excel file and parse each row.
- Automatically select the correct template based on one of the columns.
- Fill out the PDF form fields with corresponding row values.
- Name the PDF using a logical, readable pattern.
- Ensure no file is overwritten by mistake (with auto-renaming logic).
This required minimal manual effort on the client’s part: they just had to drop in the Excel file and run the script.
💻 Code Insights (Simplified)
The key modules used:
- pandas for reading and processing Excel data.
- logging for debugging and traceability.
Here’s a part of the snippet that shows how the data is handled:
fields = {
'top': row.get('TOP:', ''),
'tag': row.get('TAG:', ''),
'loop': row.get('LOOP #:', ''),
'plc': row.get('PLC:', ''),
'panel': row.get('PANEL#:', ''),
'slot': row.get('SLOT:', ''),
'point': row.get('POINT:', ''),
'date': row.get('CONTRACTOR SIGNATURE DATE', ''),
'io': row.get('IO', '')
}
logging.debug(f"Populating PDF for row {index}: {fields}") Then based on the IO type, it chooses the appropriate PDF template and creates a uniquely named output file. I also added a function to avoid filename clashes by automatically renaming files if a similar name already exists.
The entire process finishes in minutes, even for thousands of rows.
🎯 Challenges and How I Solved Them
The biggest challenge was not in reading the data—but in aligning it properly with the PDF form structure. Fillable PDF fields have to be present and match precisely, otherwise the data won’t appear as expected.
With my prior research and some help from large language models (LLMs), I resolved the format requirements efficiently and turned the template into a reusable asset. Another subtle challenge was date formatting: Excel sometimes stores them as strings, other times as timestamps. I implemented a reliable parser that handles both.
💡 Results and Business Impact
- ✅ For just one dataset, the client saved 10+ hours of manual work.
- 🔁 The tool is fully reusable—for future sheets, it’ll keep saving time.
- 😊 The client was genuinely happy, shared a smile, and immediately handed over the next project (an extension of this one).
- 💼 I added another impactful entry to my portfolio, with clean, testable code and a real-world impact story.
📌 Lessons Learned
- Repetitive work is automation’s playground. Small scripts can produce massive impact when pointed at the right problems.
- Clients love reusable tools. Once they see the time saved, they often return with more ideas.
- Clean code = client confidence. Logging, proper structure, error handling—all of it adds to professionalism.
🧩 Closing Thoughts
This project wasn’t flashy—but it was deeply practical, and that’s what made it satisfying. If you’re working on a repetive task, investing in small automation tools like this can lead to massive long-term efficiency gains—saving time, reducing errors, and freeing up your team for higher-value tasks. If you’re building your freelance or AI developer portfolio, I encourage you to focus on impact-driven tools like this one. They not only help clients—they also showcase your ability to apply code to solve real-world bottlenecks.
If you’d like help designing automation tools for your workflow or team, feel free to reach out.