Start building your own AEO/GEO playbook (without hiring an agency)
Use this five-step framework you can start on today.
I’ve been putting this one off for weeks now. Every time “AEO” showed up in a LinkedIn post or a summit deck, I’d nod, save the link, and go back to whatever tool review was due that week. It felt like the marketing equivalent of “I should really start flossing.”
I don’t think I get to do that anymore. “AEO” — answer engine optimisation, or GEO if you’re on that side of the acronym war — has been showing up in every AI marketing recap for months, including a recent talk I attended where marketers from the likes of HubSpot, Estee Lauder, and DoorDash threw around numbers like “4-6x higher conversion from AI-chat journeys versus classic search” and claims like “early signs of organic traffic eroding as AI overviews take over clicks.”
Those are figures from large US brands, several timezones away from us in APAC, but the mechanic underneath is real: AI models build answers from third-party sources — Reddit, review sites, publishers, forums — and if you're not in that pool, you don't exist to an entire discovery channel that's only getting bigger.
Here’s the step-by-step, without the consultant markup.
Step 1: Find out where you already stand
Before you build anything, check your current visibility. Pick 5-10 questions your buyers are asking — the ones that would normally send them into a Google search and toward you. Not “what does [your company] do,” but the actual problem language: “best project management tool for remote teams,” “how to reduce SDR outreach time with AI.”
Run each one cold through Claude, ChatGPT, Perplexity, and Gemini, logged out or without custom memory if you can manage it, so you’re seeing what a stranger sees. For each query, note three things: do you show up, does a competitor show up instead, and is whatever gets said about you actually accurate.
This takes under 30 minutes and costs nothing. It’s also the single most-skipped step, because it’s more satisfying to jump straight to “fixing” things than to find out if there’s anything to fix yet.
Step 2: Treat it as two separate jobs, not one
AEO isn’t SEO with a new coat of paint. SEO rewards ranking. AEO rewards being the source a model decides to cite or paraphrase — a different game with different inputs.
Job one is earning third-party authority in the places models actually crawl: Reddit threads, niche communities, comparison sites, tier-two publishers. This turns PR and community work into answer-coverage work, not just brand-awareness work. If nobody’s talking about you anywhere a model can see, no amount of on-site optimisation fixes that.
Job two is restructuring your own content to be citable. Less “ultimate guide to X,” more a direct, specific answer to a specific question, near the top of the page, with the kind of stats, quotes, and clear structure a model can lift cleanly. FAQs and schema markup help here, but they’re the finishing touch, not the strategy.
Step 3: Fix the citation pool problem first
Most teams jump to Step 2’s second job — rewriting content — because it’s the part they control. But if nobody’s referencing you anywhere, a perfectly restructured page still has nothing to be cited from. Start with one or two genuinely relevant places you could realistically get mentioned: a comparison post, a community thread, another practitioner’s newsletter or blog. Not favour trades — actual relevance. This is slower and less controllable than rewriting a page, which is exactly why most people skip it and why it matters more.
Step 4: Don’t copy the playbook wholesale if you’re building for APAC
A lot of AEO advice assumes Reddit is where your audience’s conversations live, that Google sits underneath every AI overview the way it does in the US, and that everything worth optimising is in English.
None of that holds evenly across Southeast Asia. A meaningful share of real discovery happens inside WhatsApp groups, Telegram, Line, and closed communities that no model is crawling, which means “get mentioned on Reddit” might optimise for a citation pool your actual audience never touches.
If that’s your situation, Step 3 needs a genuinely regional answer: figure out where your buyers’ real conversations happen, and treat that as your citation target, even if no AI model indexes it yet. Building visibility there still compounds, just not necessarily inside the systems these AI tools are trained on today.
Step 5: Re-test and go deep on one thing at a time
Don’t try to run all four jobs — audit, third-party mentions, content rebuild, regional adaptation — simultaneously. Pick the one query or page where you’re weakest, fix it, and re-run your Step 1 audit in 60 days to see if anything moved. If it did, you’ve got a repeatable process. If it didn’t, you’ve learned something cheap instead of something expensive.
That’s the whole playbook: audit manually first, split the two jobs, fix the citation pool before the content, adapt for where your audience actually is, only bring in a tool once manual tracking gets tedious, and re-test before you scale it. No agency retainer required.
Bonus: Once you outgrow manual checks, here’s what to track with.
The Step 1 audit works fine by hand for a while, five to ten queries, a spreadsheet, 30 minutes a month. But once you’re tracking more prompts than you can reasonably copy-paste, a few tools have emerged specifically for this:
Otterly.AI: the cheapest entry point, starting around $29/month for 15 prompts across ChatGPT, Perplexity, Google AI Overviews, and Copilot, with citation analysis. Good first tool if you just want ongoing visibility tracking without committing to a platform.
Peec AI: starts around $95/month, built specifically for AI search analytics, tracks prompt-level visibility across models. Pricing scales with how many prompts and engines you track — Claude sits behind an enterprise tier, and adding extra engines like Gemini costs more per month, so check what’s actually included before you commit.
Profound and Scrunch: more enterprise-grade, pairing visibility monitoring with auditing and optimisation workflows. Worth a look once AEO becomes a standing line item rather than a quarterly experiment, less so for a first test.
The AI;DR
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