Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK, so here's something I need to get off my chest. While everyone was rushing into ChatGPT and claiming AI would solve everything, I made a deliberate choice to wait. For two whole years, I avoided the AI hype train completely.
Why? Because I've seen enough tech bubbles to know that the real insights come after the dust settles. I wanted to see what AI actually was, not what VCs claimed it would be.
Six months ago, I finally decided to dive in properly. Not with random prompts here and there, but with a systematic approach to understanding what AI could actually do for business. That's when I discovered that most people were using AI like a magic 8-ball, asking random questions, when the real power lies in treating AI as digital labor that can DO tasks at scale.
The breakthrough came when I started experimenting with AI automation workflows for content generation at scale. I ended up generating 20,000 SEO articles across 4 languages for client projects - but here's the thing: it all started with understanding data preprocessing in platforms like Lindy.ai.
Here's what you'll learn from my hands-on experience:
Why data preprocessing is the make-or-break factor in AI model success
The specific workflow I developed for training AI models with business-specific knowledge
How to structure data pipelines that actually scale beyond toy examples
The common preprocessing mistakes that kill model performance
A practical framework for automating business processes with AI
Reality Check
What the AI consultants won't tell you about data preprocessing
Right, so let's talk about what everyone in the AI space is telling you about data preprocessing. The typical advice sounds something like this:
"Clean your data, normalize it, and feed it into the model." Simple, right? Most AI tutorials and courses make it sound like you just need to run a few preprocessing scripts and boom - you've got a production-ready model.
Here's the standard checklist every AI consultant will give you:
Remove duplicates and handle missing values
Normalize numerical features and encode categorical variables
Split your data into training, validation, and test sets
Apply feature engineering techniques
Use cross-validation to ensure model robustness
This conventional wisdom exists because it works... for academic datasets and toy problems. The issue is that real business data is messy, inconsistent, and often comes from multiple sources that don't play nicely together.
What these generic approaches miss is the context. They treat data preprocessing like a technical checklist rather than understanding that data preprocessing is where your business knowledge meets AI capabilities. You can't just throw generic preprocessing at domain-specific problems and expect magic.
The reality? Most businesses following this standard advice end up with models that work in development but fail spectacularly in production. The preprocessing pipeline becomes a bottleneck, not an accelerator. And here's the kicker - platforms like Lindy.ai were supposed to solve this, but only if you know how to structure your data correctly from the start.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the moment I realized that everything I thought I knew about AI was wrong. I'd been deliberately avoiding AI for two years, watching the hype cycle from the sidelines. But when I finally decided to experiment, I jumped straight into the deep end.
My first real AI project was ambitious: generate 20,000 SEO-optimized articles across 4 languages for an e-commerce client with over 3,000 products. Everyone said "just use ChatGPT" or "try Claude." But here's what I discovered - those tools were designed for conversation, not systematic business processes.
I spent weeks trying different AI platforms, feeding them raw product data and expecting magic. The results were... terrible. Generic content that sounded like every other AI-generated article on the internet. No personality, no business context, no understanding of the brand.
That's when I stumbled upon Lindy.ai. Unlike other platforms that felt like glorified chatbots, Lindy.ai was built around the concept of workflows and data pipelines. But here's the thing - the platform is only as good as the data you feed it.
My first attempt was a disaster. I dumped raw product catalogs, mixed data formats, and unclear business requirements into the system. The AI models couldn't understand what I wanted because I hadn't properly prepared the knowledge base. I was treating data preprocessing like a minor technical step rather than the foundation of the entire system.
The breakthrough came when I realized that AI needs to understand your business the same way a new employee would. You wouldn't hand a new team member a pile of random documents and expect them to immediately understand your brand voice, target audience, and business processes. Yet that's exactly what I was doing with AI.
This realization completely changed my approach to data preprocessing and led to the workflow that eventually generated those 20,000 articles successfully.
Here's my playbook
What I ended up doing and the results.
After that initial failure, I completely rebuilt my approach to data preprocessing in Lindy.ai. Instead of treating it as a technical step, I approached it like onboarding a new team member who happens to have superhuman processing capabilities.
Step 1: Building the Knowledge Foundation
I started by creating what I call a "business knowledge base." For the e-commerce project, this meant:
Product specifications structured by category, not just dumped in bulk
Brand voice guidelines with actual examples, not just abstract descriptions
Customer persona data with real language patterns they use
Competitor analysis showing what NOT to sound like
Step 2: Creating Context Layers
Here's where most people mess up - they feed AI raw data without context. I developed a layered approach:
Product Layer: Individual product specs with category context
Brand Layer: Tone of voice, values, and positioning
Audience Layer: Customer language, pain points, and preferences
SEO Layer: Keyword strategies and search intent mapping
Step 3: Structured Data Input Process
Instead of bulk uploads, I created a systematic input process:
CSV export with standardized column headers
Data validation to catch inconsistencies early
Template creation for each content type needed
Test runs with small batches before scaling
Step 4: Building Custom Workflows
The magic happened when I started chaining multiple AI operations together:
Product analysis → keyword research → content outline → full article → SEO optimization
Each step feeding context to the next step
Quality gates between steps to catch errors early
Feedback loops to improve future outputs
The key insight was that data preprocessing isn't about cleaning data - it's about teaching AI to think like your business. When I got this right, the AI started producing content that actually sounded like it came from someone who understood the business, not a generic content mill.
Knowledge Base
Structured business context in digestible layers, not data dumps
Workflow Design
Chained AI operations with quality gates between each step
Context Mapping
Every data point connected to business objectives and brand voice
Validation Process
Small batch testing before scaling to catch issues early
The results from this systematic approach were honestly better than I expected. Within 3 months, we went from 300 monthly visitors to over 5,000 for the e-commerce client - that's a 10x increase using AI-generated content that actually ranked and converted.
But here's what surprised me most: the AI-generated content wasn't just ranking well, it was engaging users. Time on page increased, bounce rates decreased, and most importantly, the content was driving actual sales conversions.
The preprocessing workflow I developed became the foundation for multiple other projects. I was able to adapt the same framework for different industries and content types, proving that the methodology was scalable beyond just e-commerce.
What really validated the approach was when Google's algorithm updates came through - content created with this preprocessing methodology held its rankings while generic AI content got hammered. The difference was clear: content built on properly preprocessed business knowledge performed like human-created content because it had the same contextual understanding.
The time investment upfront was significant - about 40 hours to build the initial knowledge base and workflow. But once operational, we could generate and publish content at a scale that would have required a team of 10+ writers working full-time.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from my 6-month journey with AI data preprocessing that you won't find in any tutorial:
1. Data preprocessing is business strategy, not just technical setup. The quality of your preprocessing directly determines whether your AI acts like a junior intern or a senior team member.
2. Context layers matter more than data volume. I've seen better results from 100 well-structured examples than 10,000 random data points.
3. Start specific, then generalize. Build your preprocessing workflow for one specific use case first. Don't try to create a universal system from day one.
4. Quality gates are non-negotiable. Every step in your AI workflow needs validation checkpoints. One bad input can corrupt your entire output.
5. Feedback loops accelerate improvement. Track which preprocessing approaches produce the best results and systematically improve your methods.
6. Business knowledge beats technical perfection. A business expert with basic AI knowledge will outperform a technical expert without business context every time.
7. Plan for scale from the beginning. What works for 10 pieces of content might break at 1,000. Design your preprocessing pipeline with growth in mind.
The biggest lesson? AI isn't about replacing human expertise - it's about scaling it. Your preprocessing pipeline is where your business expertise gets encoded into a system that can operate at superhuman scale.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this approach:
Start with customer support data - it's already contextual and business-specific
Use feature descriptions and user onboarding flows as training data
Focus on automating repetitive content like help docs and email sequences
Build preprocessing workflows around your existing customer data
For your Ecommerce store
For e-commerce stores adapting this methodology:
Product catalogs provide rich structured data for preprocessing
Customer reviews offer authentic voice and language patterns
Category structures help create logical data hierarchies
Seasonal trends and inventory data enable dynamic content generation