AI & Automation
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I was staring at another $300 SEMrush invoice for a B2B startup project when something clicked. I'd been paying for multiple SEO subscriptions - Ahrefs, SEMrush, sometimes even Ubersuggest - and honestly? The keyword research process was becoming a time sink.
I'd spend hours clicking through interfaces, cross-referencing data, and exporting massive CSV files just to find a handful of keywords that actually made sense for the client. The worst part? Half the volume data was wrong anyway.
Then I remembered I had a dormant Perplexity Pro account. What started as a quick experiment completely changed how I approach keyword research for clients. Spoiler alert: it's not about replacing traditional tools entirely, but about working smarter.
Here's what you'll learn from my real-world testing:
Why traditional SEO tools are overkill for most keyword research
The exact AI workflow I use to find better keywords faster
When AI keyword research fails (and how to avoid the pitfalls)
How to combine AI insights with traditional validation
Real results from switching to this hybrid approach
If you're tired of expensive SEO subscriptions and want a more intuitive approach to keyword research, this playbook will show you exactly how I've made the switch for multiple client projects.
Industry Reality
What every marketer has been told about keyword research
Walk into any SEO conference or read any "ultimate guide to keyword research" and you'll hear the same advice: invest in premium tools like Ahrefs or SEMrush, export thousands of keywords, analyze search volumes, check keyword difficulty scores, and build massive spreadsheets.
The traditional approach looks like this:
Tool-heavy research: Subscribe to multiple expensive platforms
Volume obsession: Focus on search volume numbers as the primary metric
Keyword difficulty scores: Let algorithms decide what you can rank for
Competitor analysis: Reverse-engineer what others are ranking for
Mass keyword lists: Generate hundreds of variations and long-tail keywords
This conventional wisdom exists because SEO tools companies have convinced us that more data equals better decisions. The industry has built an entire ecosystem around the idea that you need proprietary databases and complex metrics to find good keywords.
But here's where this approach falls short: it optimizes for quantity over quality and prioritizes tool features over actual user intent understanding. Most businesses end up with keyword lists they never fully use, targeting search terms that don't actually convert, and spending more time in tools than creating content.
The truth? For most businesses, especially startups and smaller companies, this approach is overkill and often counterproductive.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I took on this B2B startup SEO project, I started exactly where every SEO professional begins - firing up SEMrush and diving into Ahrefs. The client needed a complete keyword strategy overhaul, and I was following the standard playbook.
After hours of clicking through expensive subscription interfaces and drowning in overwhelming data exports, I had what looked like a decent keyword list. But something felt off. The process was:
Expensive - Multiple tool subscriptions adding up to serious monthly costs
Time-consuming - Endless manual filtering and cross-referencing
Overkill - Thousands of irrelevant keywords to sort through
The final straw came when I realized I was spending more time navigating tool interfaces than actually understanding the client's market and customer intent. These tools were giving me data, but they weren't giving me insights.
Then I had a moment of frustration-induced clarity. I remembered I had a Perplexity Pro account sitting unused. On a whim, I decided to test whether AI could handle keyword research differently.
What I discovered wasn't just surprising - it was a complete game-changer for how I approach SEO strategy. Instead of starting with tools and working backward to intent, I could start with understanding the market and let AI help me find the keywords that actually mattered.
The difference was immediate and shocking. Perplexity didn't just spit out generic keyword lists - it understood context, search intent, and competitive landscape in ways that traditional tools simply couldn't match.
Here's my playbook
What I ended up doing and the results.
Here's the exact workflow I developed that replaced my expensive SEO tool stack:
Step 1: Market Context Research
Instead of starting with seed keywords, I begin with Perplexity research about the client's industry, competitors, and customer problems. I ask questions like "What are the main challenges in [industry]?" and "How do [target customers] typically search for solutions to [specific problem]?"
Step 2: Intent-Based Keyword Discovery
Using Perplexity's research capabilities, I map out the customer journey and identify keywords at each stage. The AI naturally understands semantic relationships and suggests keywords I would never have found through traditional keyword tools.
Step 3: Competitive Intelligence
I ask Perplexity to analyze what types of content are ranking for target keywords and why. This gives me insights into search intent that keyword difficulty scores never could.
Step 4: Validation with Traditional Tools
Here's the key - I don't abandon traditional tools entirely. I use them for validation, not discovery. Once I have my AI-generated keyword list, I spot-check volumes and trends using free tools or limited paid access.
Step 5: Iterative Refinement
I use follow-up prompts to dig deeper into specific keyword clusters, asking Perplexity to explore related terms, seasonal variations, and emerging trends in the space.
The result? In a fraction of the time it used to take, I had a comprehensive keyword list that wasn't just accurate - it was strategic. Each keyword came with context about why it mattered and how it fit into the overall content strategy.
Research Speed
Reduced keyword research time from days to hours using AI's natural language processing
Context Understanding
AI grasps search intent and semantic relationships better than traditional keyword matching
Cost Efficiency
Replaced multiple expensive SEO subscriptions with strategic AI tool usage
Strategic Focus
Shifted from data collection to strategic thinking about customer search behavior
The transformation in my keyword research process delivered measurable improvements across multiple dimensions:
Time Savings: What used to take 2-3 days of research now takes 3-4 hours. I'm spending less time in tools and more time on strategy and content creation.
Quality Over Quantity: Instead of generating 500+ keyword variations, I'm focusing on 50-100 high-intent keywords that actually align with business goals and customer needs.
Better Client Outcomes: The strategic approach has led to more focused content calendars and better-targeted pages that convert visitors instead of just driving traffic.
Cost Reduction: I've cut my SEO tool expenses by 70% while improving research quality. Most clients now get better keyword strategies for less investment.
The most unexpected outcome? Clients understand and can execute on these keyword strategies themselves. Because the research is intent-based rather than data-heavy, it's easier to explain and implement.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this AI-first approach across multiple client projects, here are the key lessons that changed my entire perspective on keyword research:
Context beats volume data: Understanding why people search is more valuable than knowing exactly how many people search
AI excels at semantic understanding: It naturally finds keyword relationships that traditional tools miss
Less can be more strategic: Smaller, focused keyword lists outperform massive spreadsheets
Intent mapping is crucial: Starting with customer problems leads to better keyword discovery
Validation still matters: AI is great for discovery, but verify trends with traditional data
Speed enables iteration: Faster research means more time to test and refine strategies
Human insight remains essential: AI provides suggestions, but strategic decisions require human judgment
The biggest shift in mindset: stop treating keyword research as a data collection exercise and start treating it as a customer research exercise. AI makes this transition possible and practical.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies implementing this approach:
Focus on problem-based keywords that align with your solution
Use AI to understand how prospects describe their pain points
Map keywords to customer journey stages for better content planning
Validate commercial intent before investing in content creation
For your Ecommerce store
For ecommerce stores using this method:
Research product-specific search behaviors and seasonal trends
Identify long-tail keywords that indicate purchase intent
Use AI to find related product categories and cross-sell opportunities
Focus on local and mobile search patterns for better targeting