Introduction

Every performance marketer knows the pain of keyword research. It’s the backbone of search marketing, but it’s also the most tedious part of campaign setup. Hours are lost cleaning exports, removing competitor terms, applying intent filters, and reordering columns to make the file upload-ready.

When Custom GPTs arrived, we saw an opportunity to finally automate this. We took two approaches while building our Custom GPTs - and then, realizing their limitations, went further and built a dedicated keyword research tool. What we learned along the way is what we want to share here: why Custom GPTs are a good prototype but a not so apt production solution, and why the future lies in purpose-built, domain-specific AI systems.

The Current State of Keyword Research

Keyword research is still a very manual, time-heavy process. Marketers start by brainstorming seed keywords from their knowledge of the business and customers, then use basic tools like Google Keyword Planner or even Google Search autocomplete to expand the list. They manually filter the keywords based on search volumes, competition, and trends - often exporting spreadsheets and comparing data line by line. Finally, they group keywords into themes and prioritize them through human judgment. Overall, there is enough scope for automation and here is what we did to achieve the results.

Experimenting with GPTs for Keyword Cleaning

First Attempt: The Straightforward Custom GPT

A straightforward starting point is to codify the existing keyword-research workflow into a Custom GPT via step-by-step instructions.

The GPT would:

• Ingest seed keywords from exports
• Validate columns for completeness
• Apply exclusions and filters
• Group by ad groups and intent
• Output a structured keyword plan

On paper, it looked like everything that is needed. In practice, it was fragile. The system assumed too much from the user. If someone uploaded a file missing a column, or skipped a step, or asked an unrelated question mid-process, the GPT would continue anyway - and the output would be garbage.

This was our first realization: GPTs are too polite. They try to help even when they don’t have the right data. That’s dangerous in a workflow where accuracy is everything.

Second Attempt: The Role-Reversal GPT

To fix this, we flipped the model. Instead of the human driving the process, here the GPT acted as the orchestrator.

This “role-reversal GPT” acted like a senior strategist training a junior executive. It dictated the flow, validated inputs, and refused to move forward until each gate was cleared.

For example, it would stop and say:

• “Your file is missing the ‘Competition (indexed)’ column. Please upload a corrected export"
• “Are you targeting lead generation or branding? Please choose one before we proceed”

This made the workflow far more disciplined. The GPT enforced rigor, and the quality of outputs improved dramatically.

But it still wasn’t perfect. Because it lived inside a conversational framework, it was vulnerable to “process drift.” If a user asked a side question or changed the context mid-way, the GPT could lose its place. And while better than the first version, it still had moments where formatting slipped or hygiene checks were inconsistent.

This implied that GPTs are great prototypes, but they’re not production-ready tools.

Where GPTs Struggle and Lessons Learned

The biggest breakthroughs came not from what worked, but from where ChatGPT failed. Each failure pointed to a missing guardrail I needed to build into the system.

File Validation Errors

GPT often accepted incomplete keyword exports - sometimes missing critical fields like “Top of page bid” or “Competition.”

• Problem: Bad data in meant bad data out
• Solution: A QA step was introduced. If required columns weren’t present, the system rejected the file outright

Missed Exclusions

Even when told, GPT occasionally left in competitor brand terms.

• Problem: These irrelevant terms could waste budgets if they slipped through
• Solution: It is better to share a hard-coded hygiene exclusion list. Competitors and irrelevant queries are always stripped out automatically

Inconsistent Outputs

Sometimes GPT shuffled the column order or produced inconsistently formatted outputs.

• Problem: Campaign managers couldn’t upload files without rework
• Solution: A better approach is to lock the output schema to a strict order like this: 
  Ad Group | Keyword | Currency | Avg. monthly searches | 3-month change | YoY change | Competition | Competition (indexed) | Top of page bid (low) | Top of page bid (high) | Competitor_Brand_Excluded | Intent | Funnel | Generic_Flag

Wrong Intent Assumptions

GPT occasionally guessed whether the campaign was for lead-gen or branding - and guessed wrong.

• Problem: Entire keyword lists were misclassified
• Solution: The system now forces a user decision at the start: “Is this for lead generation or branding?”

Process Drift

If the user asked a side question mid-process, GPT sometimes “forgot” the workflow.

• Problem: The entire keyword process could derail
• Solution: Implement a strict gated flow. The system won’t progress until each gate is passed. No shortcuts, no drifting

These fixes transformed the tool from a flexible assistant into a disciplined operator. And that’s exactly what keyword research needs.

The Case for Purpose-Built AI Tools

The real breakthrough lies not in retrofitting general-purpose AI like GPTs, but in leveraging purpose-built, AI-powered keyword research tools. These solutions combine the power of automation with PPC/SEO-specific intelligence, enabling:

• End-to-end workflows for keyword cleaning, clustering, and intent mapping
• Consistent, accurate outputs without constant manual oversight
• Scalability across thousands of keywords
• Actionable insights tied to performance and content strategy

By focusing on precision and reliability, dedicated tools bridge the gap between AI experimentation and enterprise-level PPC/SEO needs.

Where GPTs Fit in the Journey

Custom GPTs provide an accessible way for marketers to experiment with AI, validate use cases, and understand what’s possible. They lower the barrier to entry and inspire innovation. But as businesses look to scale, the need shifts toward specialized tools that deliver predictable, high-quality results every time.

Conclusion

If you’ve been doing performance marketing long enough, you know how unforgiving keyword research can be. One missed exclusion, one wrong column, one misinterpreted intent - and you waste budget, time, and client trust.

This tool solves those problems It:

• Blocks errors before they slip through
• Enforces discipline through gates and checkpoints
• Produces export-ready files that plug straight into campaigns
• Codifies expertise so even junior staff deliver senior-level outputs

This isn’t just about saving time, It’s about raising the floor - ensuring no campaign leaves the table compromised because of human error or GPT’s flexibility.

The future of AI in marketing isn’t in generic assistants. It’s in domain-specific tools that combine AI’s intelligence with industry guardrails.

As veterans in this space, we shouldn’t settle for sandboxes. We should be building power tools - AI systems that scale our expertise, enforce discipline, and raise the baseline quality of work across the board.