Have you ever wanted an AEO platform that feels like it’s reading your mind? That’s exactly how I felt when I started exploring Goodie 2.0. It’s not just about speed, though that’s a massive bonus. The real magic lies in its enhanced competitor tracking and those smarter recommendations that seem tailored just for me.
The AI search visibility insights are clearer than ever, giving me the edge I need to stay ahead in the game. If you’re like me and always looking for ways to get one step ahead, Goodie 2.0 is designed with you in mind.
Tracking my brand’s visibility in AI-powered searches has become an essential part of SEO. However, the available tools often come with hefty price tags, starting around $300 to $500 monthly. For those of us who need custom solutions, these costs can be prohibitive.
I encountered this challenge firsthand. I required a specific tool that wasn’t available within my budget. So, I took matters into my own hands and built one myself, despite not being a developer. With a weekend of effort and dialogue with an AI agent, I crafted an AI search visibility tracker tailored to my needs.
Sharing my experiences, I’ve compiled a guide that I wish I had at the start—a step-by-step playbook for creating a custom tool. This guide navigates through technology, processes, the hiccups I faced, and how to streamline your build.
My main goal was to automate an AI engine optimization (AEO) testing protocol. To achieve comprehensive AI-driven brand visibility, tracking across five critical AI surfaces was necessary:
ChatGPT (via API): Renowned for its conversational AI prowess.
Claude (via API): A significant competitor with a unique response style.
Gemini (via API): Google’s direct model aimed at developers.
Google AI Mode: Enhances Google’s AI search experience with advanced reasoning.
Google AI Overviews: Summaries at the top of search results, prevalent by late 2025.
On top of these, I implemented a custom 5-point rubric for scoring results based on criteria like brand name inclusion and citation quality. With no existing SaaS tools offering this particular mix, the solution was to build one.
This project leveraged vibe coding, translating natural language into functional applications with AI assistance. Amid developers increasingly adopting AI coding and the growing trend of AI-generated code, this approach offered a viable path for a non-developer like me to create an impactful internal tool.
Your tech stack: The three tools you’ll need
To replicate this project while keeping costs manageable, here are the necessary components:
Replit Agent: An online development environment costing around $20/month, enabling application building via description alone.
DataForSEO APIs: The core of this project, allowing data retrieval from various AI platforms, priced on a pay-as-you-go model.
Direct LLM APIs (optional): Establishing direct connections with OpenAI, Anthropic, and Google APIs to verify and correct any discrepancies.
The playbook: A step-by-step guide to building your tool
Building this tool involved clear communication and step-by-step progress. Here’s a structured approach to guide your process:
Step 1: Write a requirements document first
Start by outlining your needs clearly. This document acts as a blueprint covering problems, features, and necessary data. Initial conversations with your AI should revolve around this document to set a solid foundation.
Step 2: Ask the AI, ‘What am I missing?’
Once your needs are outlined, seek the AI’s help in uncovering overlooked areas. Questions like “What am I not accounting for?” can avert common pitfalls and ensure comprehensive planning.
Step 3: Build one feature at a time and test it
Avoid building everything simultaneously. Tackle one small task and test it thoroughly before moving to the next. This methodical approach aids in pinpointing and addressing issues efficiently.
Step 4: Point the agent to the documentation
When integrating APIs, guide the AI using specific documentation. Providing exact URLs ensures accurate implementation and saves time otherwise spent fixing errors.
Step 5: Save working versions
Before introducing significant changes, save copies of your project. In Replit, this is done through “forking.” It’s a precaution against potential new feature-induced disruptions.
Common problems and how to fix them
You’ll likely face technical hurdles. Here are frequent issues with solutions to help you navigate the process smoothly:
Problem
Solution
1. API authentication fails
Provide the exact authentication documentation URL to the agent.
2. Results disappear
Ensure persistent storage by requesting a database from the start.
3. API responses don’t show
Share raw JSON data with the agent to diagnose and fix parsing logic.
4. Model response cut short
Conduct parameter checks post-updates to maintain consistent results.
Evaluating the real costs
Building this tool has clear advantages over purchasing a SaaS solution, notably cost savings. Here’s a breakdown:
Expense
Custom Tool
SaaS
Subscription
$20/month
$500/month
API Usage
$60/month
Included
Total
$80/month
$500/month
Despite the initial time investment, the ability to adapt and tailor the tool outweighs the ongoing costs.
Is building your own tool right for you?
This decision largely depends on your specific needs:
Consider building if:
You require unique testing methods not supported by current tools.
Your agency needs a white-labeled solution.
You prefer cost-effective strategies and are willing to invest time.
Stick with SaaS if:
Your time is more valuable than subscription costs.
You need robust security and customer support.
You find standard features sufficient.
Ultimately, crafting a tool that aligns perfectly with your workflow can provide a distinct edge in the competitive SEO landscape. Welcome to the era of practitioner-developers; it’s time to innovate.
I recently delved into fascinating research that sheds light on how higher education data informs SEO visibility and AI search. This exploration reveals what truly enhances visibility in this AI-driven era.
Contrary to some beliefs, AI search hasn’t rendered SEO obsolete. Now, the challenge is to excel both in ranking and in earning those vital AI citations.
Every time I Google something these days, there’s a significant chance an AI Overview will appear before any organic results or ads, framing my query, shortlisting sources, and shaping which brands I consider.
According to Ahrefs, AI Overviews now feature for about 21% of keywords. This means that while search rankings remain crucial, AI summaries increasingly dictate early brand consideration.
I’ve noticed that brands aren’t losing visibility just because they slip from the third to the seventh position on search engines. They’re often losing because they’re not even mentioned in AI answers.
Research conducted by Search Influence and UPCEA, where I serve as CEO, reveals insights into AI-assisted search usage and organizational adaptation in the higher education space.
Key Takeaways
AI citations are emerging as a trust signal: Being cited by AI can enhance credibility and secure early user consideration before direct source comparison occurs.
AI visibility is collective: AI pulls from various sources like YouTube, LinkedIn, and beyond—your URL isn’t everything.
Established brands need to adapt: Even well-known brands can be overlooked if their content doesn’t align with how users ask questions.
Most organizations recognize AI’s importance but lack action plans: Awareness exists, but execution is hindered by a lack of ownership and processes.
Content structure determines inclusion: Content that is structured for easy retrieval and decision-making often gets cited over long narratives.
To grasp the evolving search landscape, we need to examine both user behavior and organizational responses.
The findings show increased AI-assisted discovery and shifts in trust signals. Meanwhile, a UPCEA member institution poll uncovers gaps in AI strategy adoption.
The question isn’t whether AI search will impact your field; it’s whether your brand will be cited, overlooked, or represented by competitors.
I had the privilege of diving deep into the world of AI visibility with Conductor experts, exploring every facet of AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). These insights reveal how we can reshape the future of search.
In today’s digital era, mastering AEO and GEO is more than essential—it’s transformative. By leveraging these strategies, I can enhance the effectiveness of my search visibility and engagement like never before.
When I first dove into the world of AI search, I quickly learned an important lesson: don’t overlook the power of SEO. In fact, the same principles that elevate Google rankings are also the cornerstone of increasing AI citation visibility.
Maintaining a strong SEO strategy is essential, not just for traditional search engines but also as AI technology evolves. It’s fascinating how the foundational elements of SEO, like keyword optimization and quality content creation, also boost your presence in AI-driven searches.
From probabilistic answers to off-site signals, AI visibility functions differently from SEO. Here are seven truths to help you understand how and why.
Fair warning: My insights might unsettle those who have excessively promoted AI visibility tools.
Having spent 18 years in the search industry, I feel compelled to share the truth over popular beliefs.
I’m not here with an agenda. Ironically, some misconceptions benefit me as the co-founder of an AI visibility tool and a GEO service provider.
Let’s address some misconceptions that have circulated over the past few months.
1. AI Search Didn’t Kill Google Search
Quite the contrary. Despite media hype, Google’s dominance prevails with significant data backing this truth.
Convincing headlines don’t change facts. What does? Data.
Consider these studies:
Semrush’s latest study shows ChatGPT increased, not reduced, Google searches, debunking biases of Google favoritism.
Datos’ report reveals Google retains a massive 95% market share in collaboration with industry experts.
Despite ChatGPT’s rise, Google search maintains its stronghold. OpenAI reports suggest ChatGPT is often used for non-search purposes. Actual ‘search’ queries form only a fraction, reflecting use diversity.
This difference highlights the continuing necessity and dominance of traditional search engines like Google.
2. No AI Tool Can Guarantee AI Answers Inclusion
History repeats itself; tools can’t do GEO for you, similar to how they couldn’t perform SEO. True optimization can’t be automated.
Real optimization relies on human decisions, supported by insights that tools can only provide partially.
Claims of automated success often omit the human efforts that drive real results. Tools assist but can’t replace expert judgment.
3. Actual Prompt Search Volumes are Elusive
No tool or provider knows true prompt volumes, relying on estimations instead of exact data, given the lack of public usage data from LLM companies.
Current volume charts are educated guesses rather than definitive statistics.
4. AI Visibility Differs from Search Rankings
LLMs provide probabilistic results, unlike deterministic search rankings. AI answers are personalized, leading to varied responses even for identical queries.
AI models are inclined to offer guesses, resulting in varied responses. This variability presents challenges for monitoring and measuring visibility.
Most monitoring tools either use averaged data or focus on specific personas to try and model this complexity.
5. Off-Site Signals Trump On-Site Efforts in GEO
Just as backlinks indicate credibility in SEO, external brand mentions are critical for AI visibility.
Off-site signals have a greater influence on whether a brand appears in AI-driven responses, much like the way trusted external recommendations bolster a brand’s reputation.
6. Key GEO KPI: Brand Mentions in AI Responses
While citation visibility is beneficial, the strategic goal of GEO should prioritize explicit brand mentions within AI-generated answers.
AI visibility alone doesn’t secure web traffic; vital is having your brand part of the response, impacting direct discovery and engagement.
7. Misaligned GEO and SEO Practices Can Hurt Performance
Beware of GEO optimizations that conflict with established SEO principles; they can detract from overall search performance.
Effective GEO requires balance, ensuring broader SEO strategies remain complementary, rather than contradictory.
When Search Evolves, Measurement Must Too
GEO thrives within the existing search framework but needs evolved measurement strategies that reflect AI’s dynamic nature.
Embrace change by rethinking metrics, challenging assumptions, and refining success benchmarks alongside evolving technology.
