DEV Community: Riya Amir

The Smoke Detector and the Fire Brigade: Picking AEO/GEO Tools Without Fooling Yourself

Riya Amir — Tue, 27 Oct 2026 17:28:08 +0000

Here's an analogy that saves SaaS teams a lot of money.
A smoke detector tells you there's a fire. A fire brigade puts it out. You need both, but nobody thinks buying a better smoke detector will stop the kitchen burning.
Most AI visibility tools are smoke detectors. That's not an insult; detection matters. But if you're a Brisbane startup deciding between the best aeo geo tools for startups vs enterprise options, it helps to know what you're actually buying.
This post covers what to detect, how to build a cheap detector yourself, and when it's worth paying for one.
What you're detecting: four layers
Kiyado's article on GEO vs SEO vs AEO vs AIO frames these as four layers of one job:
Layer
Question
Signal
SEO
Are we in the list?
Rankings, clicks, indexation
AEO
Are we the lifted answer?
Featured snippet or "People Also Ask" ownership
AIO
Are we in Google's AI Overview?
Citation in the overview
GEO
Do AI assistants recommend us?
Named, cited, accurate in ChatGPT, Gemini, Perplexity, Copilot

They stack. SEO is the foundation; AEO shapes answers; AIO is Google's AI layer; GEO is the full picture. If the foundation is cracked, alarms higher up are mostly noise.
The startup detector: build it in an afternoon
You don't need a platform to start. A spreadsheet and a script will do.

Define a small prompt set. Ten to twenty prompts, mixing category, brand and comparison questions: prompts:
- id: cat-01 text: "best rostering software for Australian hospitality"
- id: brand-01 text: "what is Example Roster?"
- id: comp-01 text: "Example Roster vs Competitor X"
Log results in a flat table. date,engine,prompt_id,named,cited,accurate,notes 2026-10-01,chatgpt,cat-01,0,0,,competitors A and B named 2026-10-01,perplexity,brand-01,1,1,0,old pricing quoted
Score with a tiny helper. def score(answer: str, aliases: list[str], domain: str) -> dict: text = answer.lower() return { "named": any(a.lower() in text for a in aliases), "cited": domain.lower() in text, } Collect answers through official APIs where available, or manually. Respect each provider's terms of service and rate limits, and remember API answers can differ from the consumer apps.
Check SEO and AEO in Search Console. Track indexation and positions for the queries that matter. For snippets and AI Overviews, a monthly manual check of 20 queries is honest and cheap. That's a working smoke detector for a seed-stage team. When to buy an enterprise detector Paid platforms earn their keep when the problem gets bigger than a spreadsheet: Many markets or languages. Tracking prompts across Australia, New Zealand, the UK and the US by hand gets painful fast. Many products or business units. You need reporting per product line. Stakeholder reporting. Leadership wants trend charts, not CSVs. Historical depth. Answers change; you need months of stored answers to see real trends. The key question to ask any vendor: does it show me the raw answer text? A score without the words behind it is a smoke detector with no indication of which room is burning. The fire brigade problem Here's where most teams get stuck. The detector goes off. Now what? The Kiyado article explains why GEO in particular resists tool-only fixes. GEO goes further than AEO in three ways:
Many sources, one answer. Engines combine sources; you need to be one of the voices.
Recommendations, not just definitions. "Who should I use" questions are where buying decisions happen.
Off-site influence. Directories, review platforms, press and partner listings shape the answer. It also cites G2's 2026 research: buyers trust AI recommendations but still check them against review sites. For SaaS, that means G2 and Capterra profiles aren't optional decoration. None of that is fixed by a dashboard. It's fixed by people rewriting pages, aligning listings and earning coverage. In-house or agency, someone has to be the fire brigade. A decision rule that sticks The article's diagnostic doubles as a tooling guide: Pages not indexed or past page two? You don't need a GEO tool yet. Use Search Console and fix SEO. Ranking, but missing from snippets and AI Overviews? A lightweight tracker plus content work on question-style headings and clear answers. (The often-quoted 40–60-word answer length is a practitioner guideline, not a published rule.) Competitors named by ChatGPT, you aren't? Now a GEO tracker earns its place, alongside off-site work. All three? One plan, in that order. Don't let the tool budget eat the fix budget. Make the alarms specific A vague alarm gets ignored. "AI visibility down 4%" tells nobody what to do. "Perplexity quotes our 2024 pricing for brand-01" tells the docs team exactly which page and listing to fix. When you log results, always record which layer failed and what the engine said. Startups vs enterprise, in one line each Startup: build a cheap detector, spend the money on fixes. Enterprise: buy a good detector, and make sure a named team owns every alarm. Don't split the brigade One last warning from the article: don't buy four separate retainers for four acronyms. The layers share the same pages, technical base and content. Separate vendors can work against each other, and in a SaaS company those shared pages are your pricing, docs and comparison pages: the most valuable URLs you own. If you'd like an outside audit of all four layers for your Brisbane product, Kiyado offers one free: SEO and GEO for Brisbane businesses →

Build Your Own AI Visibility Tracker (Before You Buy a GEO Tool)

Riya Amir — Tue, 27 Oct 2026 10:59:08 +0000

Look, there are a lot of GEO tools appearing right now. Some are great. But before you spend money, you can build a basic AI visibility tracker in an afternoon and learn what you actually need.
This post walks through it. The concepts come from Kiyado's explainer on GEO, which is also useful context if you're weighing a generative engine optimization tools comparison.
What we're measuring
The Kiyado post suggests a simple, practical test. For each AI answer, record three things:
Named: is your product mentioned?
Cited: does the answer link to your site?
Accurate: is the description correct?
That's it. Three booleans per prompt, per engine, per month. Simple data you can trend.
Step 1: Define your prompt set
Start with 15–25 prompts that mirror real buyer questions:
prompts:
category:
- "What are the best {category} tools for {audience}?"
- "Which {category} software should a {buyer} choose for {need}?"
brand:
- "What is {brand} known for?"
- "Is {brand} good for {use_case}?"
comparison:
- "{brand} vs {competitor}"
- "Alternatives to {competitor}"
variables:
category: "customer onboarding"
audience: "B2B SaaS companies in Australia"
buyer: "head of customer success"
need: "reducing time-to-value"
brand: "Example Onboard"
competitor: "Competitor X"
use_case: "product tours"
Keep the set stable. Changing prompts every month destroys your trend line.
Step 2: Collect answers
You can do this manually (honestly fine for 20 prompts × 3 engines) or via official APIs where available. Respect each provider's terms of service and rate limits, and note that API answers may differ from what users see in consumer apps.
import csv, datetime

def record(engine: str, prompt: str, answer: str, brand: str, domain: str):
row = {
"date": datetime.date.today().isoformat(),
"engine": engine,
"prompt": prompt,
"named": brand.lower() in answer.lower(),
"cited": domain.lower() in answer.lower(),
"accurate": "", # fill in manually against your fact sheet
"answer": answer.replace("\n", " ")[:2000],
}
with open("ai_visibility_log.csv", "a", newline="") as f:
w = csv.DictWriter(f, fieldnames=row.keys())
if f.tell() == 0:
w.writeheader()
w.writerow(row)
Step 3: Score accuracy with a fact sheet
Accuracy is the metric most teams skip, and it's often the most revealing. Create a fact sheet: pricing tiers, integrations, hosting regions, target customers. When an answer contradicts it, mark it inaccurate and note why.
Inaccurate answers usually point to a specific outdated source: an old review profile, a stale partner page or a forgotten blog post.
Step 4: Visualise monthly trends
A simple pivot is enough:
Month
Engine
Named %
Cited %
Accurate %
Aug
ChatGPT

Aug
Gemini

Aug
Perplexity

Here's the deal: don't react to single answers. AI output is noisy. React to trends over two or three months.
Step 5: Connect it to actions
Every inaccuracy or absence should map to one of the three GEO layers:
Content: missing or vague information on your own site
Identity: inconsistent descriptions across properties
Corroboration: weak or outdated third-party evidence
Fix the layer, then watch the trend.
What this won't tell you
Exact traffic or revenue from AI answers (attribution is still messy)
Why an engine chose a specific source
Anything guaranteed. The 2024 research that named GEO reported gains of up to 40%, but they varied by topic, and engines change often.
When to buy a tool
Once you've run this for two or three months, you'll know which features you actually need: more prompts, more engines, automated accuracy checks or competitor tracking. Then compare tools against real requirements, not demos.
Example monthly report
Here's what a one-page report from this pipeline might look like for a Melbourne SaaS team. The figures are illustrative.
Metric
Month 1
Month 2
Month 3
Named rate (all engines)
20%
27%
33%
Cited rate
7%
13%
20%
Accuracy rate
60%
73%
80%
Open issues
9
6
4

Underneath the table, list the top three issues and their owners:

Gemini describes the product as "project management software" (it's resource planning). Layer: identity. Fix: align G2, Capterra and the LinkedIn company page.
Perplexity cites a 2023 review with outdated pricing. Layer: corroboration. Fix: request an update from the publisher.
ChatGPT doesn't name us for "resource planning software for agencies in Australia". Layer: content. Fix: publish a dedicated use-case page. The value isn't the percentages. It's the list: specific, owned and fixable. That's what turns measurement into progress. Keep it lightweight You don't need a data warehouse to start. A shared spreadsheet, a fixed prompt set and a calendar reminder are enough for the first quarter. Automate only when the manual version becomes the bottleneck. Choosing your prompts The prompt set decides what the pipeline can see, so pick it carefully. Mix three types: Category prompts ("best resource planning software in Australia"): do engines know you belong in the category? Brand prompts ("what is Example Plan?"): do they describe you correctly? Comparison prompts ("Example Plan vs Competitor X"): do they represent your differences fairly? Ask your sales team for the exact phrases prospects use on calls. Those phrases make better prompts than anything brainstormed in a marketing meeting. Aim for about 20 prompts, review them each quarter and keep a changelog of any edits. Add a few prompts in the words of Melbourne buyers specifically, such as "resource planning software for agencies in Melbourne", so you can see whether local relevance improves alongside national visibility. The ROI angle This tracker costs a few hours a month. It tells you whether your content, identity and corroboration work is moving the needle, which is exactly what you need before spending more. Searches for a geo agency melbourne have grown sharply this year. The teams measuring now will have the data advantage later. If you'd like a second opinion on your setup, Kiyado's Melbourne team offers a free audit: SEO and GEO for Melbourne businesses →

Build Your Own AI Visibility Tracker (Before You Buy a GEO Tool)

Riya Amir — Tue, 27 Oct 2026 10:19:03 +0000

Aug
Gemini
Aug
Perplexity
Here's the deal: don't react to single answers. AI output is noisy. React to trends over two or three months.
Step 5: Connect it to actions
Every inaccuracy or absence should map to one of the three GEO layers:
Content: missing or vague information on your own site
Identity: inconsistent descriptions across properties
Corroboration: weak or outdated third-party evidence
Fix the layer, then watch the trend.
What this won't tell you
Exact traffic or revenue from AI answers (attribution is still messy)
Why an engine chose a specific source
Anything guaranteed. The 2024 research that named GEO reported gains of up to 40%, but they varied by topic, and engines change often.
When to buy a tool
Once you've run this for two or three months, you'll know which features you actually need: more prompts, more engines, automated accuracy checks or competitor tracking. Then compare tools against real requirements, not demos.
Example monthly report
Here's what a one-page report from this pipeline might look like for a Melbourne SaaS team. The figures are illustrative.
Metric
Month 1
Month 2
Month 3
Named rate (all engines)
20%
27%
33%
Cited rate
7%
13%
20%
Accuracy rate
60%
73%
80%
Open issues
9
6
4

Underneath the table, list the top three issues and their owners:

Gemini describes the product as "project management software" (it's resource planning). Layer: identity. Fix: align G2, Capterra and the LinkedIn company page.
Perplexity cites a 2023 review with outdated pricing. Layer: corroboration. Fix: request an update from the publisher.
ChatGPT doesn't name us for "resource planning software for agencies in Australia". Layer: content. Fix: publish a dedicated use-case page. The value isn't the percentages. It's the list: specific, owned and fixable. That's what turns measurement into progress. Keep it lightweight You don't need a data warehouse to start. A shared spreadsheet, a fixed prompt set and a calendar reminder are enough for the first quarter. Automate only when the manual version becomes the bottleneck. Choosing your prompts The prompt set decides what the pipeline can see, so pick it carefully. Mix three types: Category prompts ("best resource planning software in Australia"): do engines know you belong in the category? Brand prompts ("what is Example Plan?"): do they describe you correctly? Comparison prompts ("Example Plan vs Competitor X"): do they represent your differences fairly? Ask your sales team for the exact phrases prospects use on calls. Those phrases make better prompts than anything brainstormed in a marketing meeting. Aim for about 20 prompts, review them each quarter and keep a changelog of any edits. Add a few prompts in the words of Melbourne buyers specifically, such as "resource planning software for agencies in Melbourne", so you can see whether local relevance improves alongside national visibility. The ROI angle This tracker costs a few hours a month. It tells you whether your content, identity and corroboration work is moving the needle, which is exactly what you need before spending more. Searches for a geo agency melbourne have grown sharply this year. The teams measuring now will have the data advantage later. If you'd like a second opinion on your setup, Kiyado's Melbourne team offers a free audit: SEO and GEO for Melbourne businesses →