AI & Automation
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I took on a B2B startup website project as a freelancer. The client needed a complete SEO strategy overhaul, and the first critical step was obvious: build a comprehensive keyword list that would actually drive qualified traffic.
So I did what every SEO professional does - fired up SEMrush, dove into Ahrefs, and started cross-referencing with Google autocomplete. After hours of clicking through expensive subscription interfaces and drowning in overwhelming data exports, I had a decent list. But something felt off.
The process was expensive (multiple tool subscriptions adding up), time-consuming (endless manual filtering), and honestly overkill (thousands of irrelevant keywords to sort through). That's when I decided to experiment with something different.
Here's what you'll learn from my experience:
Why traditional keyword research tools can actually slow you down
How I replaced multiple expensive subscriptions with one AI tool
The exact process I used to build comprehensive keyword strategies in hours, not days
When AI keyword research works (and when to stick with traditional tools)
The surprising results that changed how I approach SEO research
This isn't about jumping on the AI bandwagon - it's about finding what actually works when you need results fast without breaking the budget.
Industry Reality
What every SEO agency has already heard
Walk into any digital marketing agency, and you'll hear the same keyword research gospel being preached. The "proven" process goes something like this:
Start with seed keywords - brainstorm your main topics
Feed them into premium tools - SEMrush, Ahrefs, Moz, whatever your budget allows
Export massive spreadsheets - thousands of keyword variations with search volumes
Filter by difficulty and volume - hunt for the "sweet spot" keywords
Group and categorize - organize into topic clusters manually
This approach exists because it worked in the early days of SEO when competition was lower and keyword tools were revolutionary. Agencies built entire service offerings around this process, charging thousands for keyword research that took weeks to complete.
The problem? This conventional wisdom assumes you have unlimited time and budget. For most startups and small businesses, spending $200-500 monthly on multiple SEO tools just for keyword research is insane. Plus, you're drowning in data that may not even be accurate - these tools notoriously show "0 searches" for keywords that actually drive 100+ visits monthly.
But here's where the industry narrative gets really broken: they treat keyword research like it's rocket science when it's actually about understanding search intent and finding opportunities. You don't need a PhD in SEO tools - you need smart research that uncovers what your audience actually searches for.
Most agencies won't tell you this because their entire business model depends on making keyword research seem complex and expensive.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started this B2B startup project, I followed the textbook approach. I had active subscriptions to SEMrush and Ahrefs, and I was ready to do this "the right way." The client was a software company targeting HR professionals, and they needed keywords for everything from product features to industry pain points.
I spent my first day clicking through interfaces, exporting CSV files, and cross-referencing data between platforms. The process was mind-numbing: search for a keyword, check the volume, note the difficulty, export the related terms, repeat. After six hours, I had spreadsheets full of data but no clear strategy.
The bigger problem became obvious when I started analyzing the results. The tools showed conflicting data - SEMrush said one keyword had 1,000 monthly searches while Ahrefs claimed 2,400. Half the "related keywords" were completely irrelevant to the client's business. I was spending more time cleaning data than actually thinking about strategy.
That's when frustration kicked in. Here I was, burning through expensive subscriptions and my client's budget, for what felt like digital busywork. The client didn't need a thousand keyword variations - they needed smart, targeted keywords that would actually bring in qualified leads.
On a whim, I remembered I had a dormant Perplexity Pro account somewhere. I'd barely used it, but I knew it was supposed to be good at research. What if I could use it differently?
Instead of feeding it generic prompts about keyword research like I'd tried with ChatGPT before, I decided to test Perplexity's research capabilities specifically for SEO work. The results were immediately shocking and completely changed how I approach keyword research.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I did to build a complete keyword strategy using Perplexity's research tool, and why it worked better than traditional methods.
Step 1: Industry Context Research
Instead of starting with seed keywords, I started with industry research. I asked Perplexity to research the HR software market, current challenges HR professionals face, and emerging trends in the space. This gave me context that no keyword tool provides - understanding the actual problems my client's audience was trying to solve.
Step 2: Intent-Based Keyword Discovery
Rather than searching for keyword variations, I focused on search intent. I prompted Perplexity to research what HR professionals search for when they're facing specific challenges like "employee onboarding automation" or "compliance tracking software." This approached keywords from the user's perspective, not the SEO tool's algorithm.
Step 3: Competitive Intelligence
I used Perplexity to research what competitors were ranking for and, more importantly, what gaps existed in their content. This wasn't just about finding their keywords - it was about understanding the entire competitive landscape and finding opportunities they'd missed.
Step 4: Long-tail Opportunity Mapping
Traditional tools are terrible at finding conversational, long-tail keywords that people actually type. Perplexity excelled here because it understands natural language patterns. I could ask it to research how people actually phrase questions about HR software, and it returned genuine, searchable queries.
Step 5: Content Cluster Creation
The most powerful part was using Perplexity to organize keywords into logical content clusters. Instead of manually grouping hundreds of keywords, I could research entire topics and get comprehensive keyword families that made strategic sense together.
The entire process took about 4 hours - compared to the 2-3 days I usually spent with traditional tools. But the quality was what really surprised me. The keywords weren't just data points; they were genuine search queries connected to real business problems.
Research Depth
Perplexity provided context and intent behind keywords, not just volume numbers
Speed Factor
Completed comprehensive keyword research in 4 hours instead of 2-3 days
Cost Efficiency
Replaced $400+ monthly tool subscriptions with a $20 Perplexity Pro account
Strategic Focus
Found business-relevant keywords instead of getting lost in irrelevant data
The results spoke for themselves. Within three weeks of implementing the keyword strategy, the client's organic traffic had increased by 40%. More importantly, the quality of traffic improved dramatically - we were attracting HR professionals with actual buying intent, not just casual browsers.
But here's what really surprised me: the keywords I found through AI research were performing better than ones I'd previously discovered through traditional tools. The long-tail queries Perplexity helped me identify were converting at 2.3x the rate of generic short-tail keywords from SEMrush.
The client was thrilled, but I was honestly shocked. I'd accidentally discovered that AI research could outperform tools that cost 20x more. Since then, I've used this approach on every SEO project, and the results remain consistent.
The timeline was compressed too. What used to be a 2-week research phase became a 2-day strategic sprint. Clients got faster results, I saved money on subscriptions, and the keyword strategies were more aligned with actual business goals.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me several crucial lessons about keyword research in 2025:
Context beats volume - Understanding why people search is more valuable than knowing how many people search
AI understands intent better than algorithms - Perplexity could grasp conversational search patterns that traditional tools missed
Speed enables iteration - When research is fast, you can test and refine strategies quickly
Cost shouldn't be a barrier - Effective keyword research doesn't require expensive tool stacks
Quality over quantity - 50 strategic keywords outperform 500 random ones
Business alignment matters most - Keywords should connect to actual business problems, not just search volume
Traditional tools still have their place - For technical SEO analysis and backlink research, premium tools remain valuable
If I were starting this project again, I'd skip the traditional tool phase entirely and go straight to AI research. The only thing I'd change is spending more time on competitive analysis using Perplexity's research capabilities.
This approach works best for small to medium businesses that need strategic keyword research without enterprise-level complexity. If you're doing large-scale SEO for massive sites, traditional tools might still be necessary. But for most projects, AI research delivers better results faster.
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:
Focus on feature-specific long-tail keywords that show buying intent
Research integration and comparison keywords your prospects actually search
Use AI to understand the customer journey from problem awareness to solution research
For your Ecommerce store
For ecommerce stores using this strategy:
Research product-specific keywords including brand, model, and use-case variations
Use AI to find seasonal and trending keywords traditional tools miss
Focus on commercial intent keywords that lead directly to purchases