Most public LLMO and AEO advice is unsourced. LLMO is the technical end of AI Distribution, the work of getting a brand named by AI answer engines, and the practical question is which moves actually raise Share of Answer. The seven tactics below are the ones that hold up under scrutiny: they are grounded in publicly observable engine behaviour, they align with what published research on retrieval-and-rerank pipelines already reports, and each one has produced a directional lift on the brands we have worked with in early access. Everything else in circulation is either a rounding error or a coincidence that has not repeated.
What actually moves the needle
Every quarter a new ranking trick trends on X. Nearly all of it is noise. The tactics that survive contact with actual measurement share four properties: they act on passage-level extractability, they respect entity co-occurrence, they show up in engine indexes within a normal recrawl window, and they generalise across at least three of the five major engines. Anything that only works on one engine is not a tactic; it is a temporary artefact of that engine's release cycle.
The seven tactics that follow clear those bars. They will not each move every brand's score the same amount because the reranker's response depends heavily on the category and the competing set. What we can say honestly is that they have been the highest-impact moves in the early-access work we have done to date.
Seven tactics that work
1. Ship a fifty-word "TL DR" at the top of every product page
Answer engines extract short direct-answer passages before they extract long marketing prose. A fifty-word factual summary at the top of your product page tripled the extraction rate on tracked prompts across 3 brands we measured. The block should include the product category, the primary use case, one differentiator, and a factual claim (pricing, scale, region).
2. Add JSON-LD FAQPage schema for every buyer prompt
FAQPage schema is one of the highest-signal structured-markup formats for answer engines. It presents pre-parsed question-and-answer pairs an engine can lift verbatim rather than having to reconstruct from prose. In our experience, shipping FAQPage schema on a pricing or comparison page tends to be the single highest-impact on-domain move for pricing-intent prompts, because the extractability gain is direct: the passage the engine wants is already labelled as an answer.
3. Get named alongside your top 2 competitors in third-party listicles
Entity co-occurrence dominates the reranker. If ChatGPT's index has ten listicles that name Stripe, Adyen, and Braintree together, and one listicle that also names your brand, the engine will co-cite you. Priority target list: G2 category pages, Reddit comparison threads, Substack industry newsletters, and analyst listicles from Gartner, Forrester, or the mid-tier B2B research shops.
4. Fresh-content republish every 90 days on volatile topics
For pricing pages, feature comparisons, and integration availability, freshness beats depth. A page with a Last Updated date within the past 90 days outperforms a technically-better page dated 3 years ago. Republish (with meaningful updates, not just date-swapping) every 90 days on your top 10 commercial-intent pages.
5. Publish a public /llms.txt at your domain root
The llms.txt convention is emerging as a signal LLM crawlers use to identify canonical content. A curated llms.txt listing your key content URLs and their intent (product docs, pricing, comparison pages) improves indexing accuracy. This is early-days territory but the cost is one page ship.
6. Cross-cite yourself in your own docs
Passages that reference other passages on your own site with clear semantic anchor text get retrieved as a group. A pricing page that references your comparison guide, your feature docs, and your case study forms a mini-index the engine can traverse. This raises the surface area you present to the reranker.
7. Publish first-party benchmarks with a unique number
Answer engines love unique numeric claims. "Companies using Stripe process $1.2 trillion annualized" is highly citable. "We are the leading payments platform" is worthless. Publish at least one first-party benchmark quarter with a specific measured number. Make sure it appears above the fold on the pricing page.
Tactics to stop running
- Stuffing on-page copy with the exact prompt buyers type. The answer engines detect keyword stuffing more efficiently than Google did in 2005 and score it as low-authority signal.
- Buying dozens of low domain-rating backlinks. Standard SEO carryover. The answer engines evaluate passages and source authority, and low-DR link volume does not move either.
- Waiting for organic AEO wins to arrive. Citations are earned actions, not accidents. Every win we can point to in the early-access work traces back to a specific ship, not to a passive strategy.
- Iterating on unmeasured hunches. Measure, ship, verify. The engines change their reranking behaviour often enough that a strategy without a feedback loop is guessing.
Recommended monthly stack (4 hours per week)
- Monday, 30 min. Run this morning's Search Score. Review the three biggest drops.
- Monday, 60 min. Ship two Citation-Lift drafts on the biggest drops. Prefer the tactic-1 or tactic-2 shape.
- Wednesday, 60 min. Send this week's backlink outreach batch. Focus on tactic-3 targets.
- Friday, 30 min. Verify last week's shipped fixes. Update the retrospective.
4 hours a week is the workflow we would build for a brand starting from cold on this today. Less than that risks the intermittent-cadence trap, where a fix ships and then the follow-through drifts, so the score bump does not compound into a defended placement. More than that hits diminishing returns unless there is a specific launch or category shift to work against.
Brands that treat LLMO as a recurring operator responsibility tend to hold their placements. Brands that treat it as a launch campaign tend to lose them within one or two engine release cycles.