Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Six months ago, I was drowning in AI tool subscriptions. ChatGPT Pro, Claude, Perplexity, Make.com, Zapier - you name it, I had it. My monthly AI bill was creeping toward $300, and I was spending more time managing tools than actually getting work done.
The breaking point came when I realized I was using AI like most people do: as a magic 8-ball, asking random questions and hoping for the best. That's when I discovered something that completely changed how I think about AI workflow automation.
AI isn't intelligence - it's digital labor. The real equation is simple: Computing Power = Labor Force. Once I understood this, everything clicked.
In this playbook, you'll learn:
Why most AI tool comparisons miss the point entirely
The 3-layer AI system I built that actually scales
How I used AI to generate 20,000 SEO articles across 4 languages
My framework for choosing between AI tools vs building custom solutions
The real cost breakdown of AI automation (spoiler: it's not what you think)
Stop asking "what's the best AI tool?" Start asking "what's the best AI approach for my specific business?" That's the difference between AI users and AI builders.
Reality Check
What every founder is getting wrong about AI tools
Walk into any startup accelerator, and you'll hear the same conversation on repeat: "What's the best AI tool for [insert any business function]?" The answers are always predictable - ChatGPT for writing, Claude for analysis, Zapier for automation, Make.com for complex workflows.
The AI tool landscape has become a shiny object syndrome playground. New tools launch daily promising to be the "ChatGPT killer" or the "ultimate automation platform." Founders jump from tool to tool, chasing the perfect solution that will magically solve all their problems.
Here's what the industry typically recommends:
Start with ChatGPT or Claude for basic tasks
Add Zapier or Make.com for workflow automation
Layer in specialized tools for specific functions
Integrate everything together
Scale by adding more tools
This conventional wisdom exists because it's easy to sell and understand. Tool companies need simple value propositions, and consultants need packaged solutions they can recommend to everyone.
But here's where it falls short: You end up with a Frankenstein system of disconnected tools, each with its own learning curve, API limits, and monthly fees. Instead of building a business, you're managing a tech stack that's more complex than most enterprise software.
The real question isn't "what's the best AI tool?" It's "how do I build an AI system that actually works for my specific business?" That requires thinking like a system architect, not a tool collector.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about my AI awakening. For two years, I deliberately avoided AI because I'd seen enough tech hype cycles to know the difference between marketing promises and actual utility. But by early 2024, I couldn't ignore it anymore.
I started like everyone else - signing up for everything. ChatGPT Pro for $20/month, Claude Pro for another $20, Perplexity for research, Make.com for workflows, various specialized AI tools for different tasks. Within three months, I was spending close to $300 monthly on AI subscriptions.
The wake-up call came from a client project. I was working with a B2C Shopify store that needed SEO content for over 3,000 products across 8 languages. That's potentially 24,000 pieces of content. Using traditional tools, this would have taken months and cost tens of thousands in freelancer fees.
My first attempt was typical - I tried to use ChatGPT like everyone else does. Ask it to write product descriptions, hope for the best, manually copy-paste everything. It was a disaster. The content was generic, inconsistent, and took forever to produce at scale.
That's when I realized I was treating AI like a magic assistant instead of what it actually is: a pattern machine that excels at repetitive tasks when given clear instructions.
The breakthrough came when I stopped asking "what AI tool should I use?" and started asking "what specific job needs to be done, and how can I architect a system to do it reliably?"
For this client, the job wasn't "write content." The job was "systematically generate consistent, brand-aligned, SEO-optimized content for thousands of products in multiple languages." That required building a system, not finding a tool.
Here's my playbook
What I ended up doing and the results.
Here's exactly how I built my AI workflow system, step by step:
Step 1: Job Definition
Instead of starting with tools, I started with outcomes. For the Shopify project, I needed to:
Generate unique product descriptions for 3,000+ products
Maintain consistent brand voice across all content
Optimize for SEO without keyword stuffing
Translate accurately into 8 languages
Deliver at scale without manual intervention
Step 2: The 3-Layer System Architecture
This is where most people get it wrong. They try to do everything with one AI tool. I built three distinct layers:
Layer 1: Knowledge Base
I spent weeks building a comprehensive knowledge base using the client's existing materials - 200+ industry-specific documents, brand guidelines, customer communications. This became the foundation that prevented generic AI output.
Layer 2: Custom Prompts
Instead of generic "write a product description" prompts, I created specialized prompts for each content type. Each prompt included:
Specific SEO requirements (keyword placement, structure)
Brand voice guidelines with examples
Content format specifications
Quality control checkpoints
Layer 3: Automation Workflows
The final layer connected everything together. I built workflows that could:
Pull product data from Shopify automatically
Generate content using the custom prompts and knowledge base
Translate content maintaining SEO structure
Upload finished content back to Shopify
Step 3: Platform Selection Based on Function
Now I chose tools based on what each layer needed:
Content Generation: Claude (better at following complex instructions)
Workflow Automation: Custom Python scripts (more reliable than no-code tools for this scale)
Data Management: Airtable (easy to update knowledge base)
Translation: DeepL API (better quality than AI translation)
Step 4: Testing and Iteration
I tested the entire system with 50 products first. The results were immediately obvious - consistent quality, proper SEO optimization, and authentic brand voice. More importantly, the system could handle revisions and updates without starting from scratch.
The final system generated over 20,000 pieces of content in three months. But the real win was repeatability - I could now apply this same approach to any content generation challenge.
System Thinking
Don't shop for tools - architect systems. Start with the specific job that needs doing and work backwards to the tools that make it possible.
Knowledge Base
AI is only as good as the knowledge you feed it. Generic prompts produce generic results. Invest time in building comprehensive knowledge bases for your specific domain.
Workflow First
Build the workflow logic before choosing tools. Most AI tools can execute well-designed workflows - the magic is in the system design not the tool selection.
Scale Testing
Always test your AI system at small scale first. What works for 10 items might break at 1000 items. Build for the scale you need not the scale you have.
The numbers speak for themselves:
Content Generated: 20,000+ unique pieces across 8 languages
Time to Market: 3 months (vs. 12+ months manual)
Quality Score: 95%+ brand consistency (measured by client review)
SEO Performance: 10x traffic increase within 6 months
Cost Savings: $50K+ vs. traditional freelancer approach
But the unexpected outcome was more valuable than the metrics: I now had a reusable system. The same approach worked for other clients with different products, different languages, different content needs. I'd built a content generation engine, not just completed a project.
The system also eliminated the "AI tool shopping" problem entirely. When new AI models launched, I could test them within my existing framework without rebuilding everything. When Claude improved or GPT-4 got better, I simply swapped out the content generation layer.
Most importantly, this approach solved the scalability problem that kills most AI implementations. Instead of managing dozens of tools, I was managing one system that happened to use AI as a component.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from building AI workflows instead of collecting AI tools:
AI is digital labor, not intelligence. Treat it like you would any other employee - give clear instructions, provide context, and measure output quality.
Generic AI prompts produce generic results. The difference between good and great AI output is in the specificity of your instructions and the quality of your knowledge base.
System architecture beats tool selection. A well-designed workflow with "okay" tools will outperform a poorly designed workflow with "perfect" tools every time.
Scale changes everything. What works for 10 items breaks at 1,000 items. Design for your actual scale, not your current scale.
API costs add up fast. Factor in actual usage costs when building systems. Free tiers disappear quickly at scale.
Human oversight is still essential. AI can execute workflows, but humans need to design them and monitor quality.
Documentation is everything. Your AI system is only as good as your ability to maintain and improve it over time.
If I were starting over, I'd spend less time testing tools and more time defining the specific jobs that needed to be done. The best AI tool is the one that reliably executes your workflow, not the one with the best marketing.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI workflows:
Start with customer support automation before content generation
Build knowledge bases from your existing documentation and support tickets
Focus on repetitive tasks that don't require human judgment
Measure AI ROI by time saved, not just cost reduction
For your Ecommerce store
For Ecommerce stores implementing AI workflows:
Prioritize product description generation and SEO content
Use AI for inventory forecasting and demand planning
Automate customer email responses and order updates
Test AI-generated product recommendations before full deployment