Can you guarantee assistants will recommend us?

No — no one controls model output, and anyone claiming to is guessing. What is controllable is the input: entity consistency, corroboration, extractable content. The monthly panel measures whether the input is moving the output.

Which assistants does this cover?

ChatGPT, Gemini, Copilot and Perplexity, tested on the buying questions your customers actually ask. The panel is category-specific — generic test questions produce generic, useless data.

How does this relate to our existing SEO work?

It is the same foundation plus two additions: entity consistency across the web, and the recommendation panel as a measurement instrument. Teams already doing strong technical and content SEO start with a real head start.

[13]AI Search

AI Assistant Optimisation

Become the brand AI assistants recommend when buyers ask — through entity clarity, credibility and monitoring.

An increasing number of buying decisions now start with a question to an assistant: "Who should we use for X?" "Compare these three vendors." "Is this company any good?" The assistant's answer is built from what the web collectively says about you — and if that picture is thin, stale or wrong, the recommendation goes to someone else. AI assistant optimisation is the work of making sure your brand is understood, accurately and favourably, by the systems buyers now ask.

Written by Rahul Sengupta, independent SEO & AI-search consultant in Melbourne.

About the author

Being the recommended answer

When someone asks ChatGPT, Claude or Gemini for a vendor or category recommendation, the assistant assembles an answer from its picture of the market: which brands exist, what they're known for, and how credible they appear. That picture is built from your own site, structured data, knowledge panels, reviews, media coverage, directories and third-party mentions. Recommendation follows clarity — brands that are consistently and specifically described, everywhere, are the ones assistants can confidently name.

[S1]Comparison

Ranking vs being recommended

Ranking vs being recommended	Ranking in search	Being recommended by assistants
Win condition	Top position for a query	Named when a buyer asks what to buy
Basis	Page relevance and authority	Brand entity strength and consistent third-party corroboration
Buyer stage	Researching options	Final shortlist — the closest stage to purchase
Who decides	An algorithm scoring pages	A model weighing what the whole web says about you
Measurement	Rank trackers	Recommendation testing, sentiment and share of answer

Brand-entity building

Assistants don't rank pages; they reason about entities. To be recommended, your brand needs to be a well-defined entity:

Consistent entity data — the same name, description, category and facts across your site, structured data and profiles
Knowledge panel presence, so assistants can anchor the brand to verified facts
Credible third-party mentions — reviews, industry press, comparison content and listings that corroborate what you say about yourself
Content that states clearly what you do, who it's for and how you're different, in plain language an assistant can extract

Self-description alone isn't enough. What independent, credible sources say about you carries more weight.

[S2]Process

How entity building runs

Entity audit
Weeks 1–2
What the web currently says your brand is — and where it contradicts itself.
Knowledge panels, wikis, directories, reviews and structured data are compared. Contradictions are why assistants hedge or omit you.
Consistency programme
Weeks 2–6
One brand story, written everywhere the models read.
Structured data, profiles, listings and third-party references aligned to a single canonical description.
Corroboration building
Ongoing
The third-party mentions that make the brand safe to recommend.
Reviews, comparisons, directories, press and community presence — the web of corroboration models weigh before naming a brand.

Monitoring how assistants talk about you

You should know what buyers hear when they ask about your category. That means regularly testing the questions your customers actually ask — vendor shortlists, category overviews, comparisons with named competitors — and recording what comes back: whether you're mentioned, how you're characterised, and which sources shaped the answer. Errors compound silently; a misdescription left alone becomes the assistant's default position.

[S3]Deliverables

What monitoring delivers each month

Recommendation test results across a fixed buying-question panel
Share of answer: how often you are named versus competitors
Sentiment analysis: what assistants say when they mention you
Corrections applied: inaccurate claims surfaced and fixed at the source
New corroboration opportunities identified and prioritised
Trend reporting against the baseline, quarterly deep-dive

Overlap with classic SEO foundations

None of this replaces SEO — it depends on it. A crawlable, well-structured site, strong technical health, credible content and genuine authority are the foundations assistants build on. The difference is emphasis: alongside ranking for queries, we now also manage how the brand is represented inside generated answers, where there is no page two and no second impression.

[S4]Capabilities

The foundations that carry over

Technical accessibility
Assistants and answer engines cannot recommend what they cannot read. Crawlable, fast, well-marked-up pages remain the floor.
Structured data
Organisation, product and person markup is how machines resolve who you are. It is the passport photo of entity building.
Extractable content
Clear, self-contained answers to the questions buyers ask — the passages models lift into responses.
Authority and mentions
Links still matter; mentions matter more. The corroboration web is built with the same outreach muscles as link building.

What this looks like

Engagements start with a brand-entity audit: how assistants currently describe you, where the facts come from, and where the picture is wrong or missing. Then the fix programme — entity and structured data cleanup, third-party mention and citation building, and a monitoring cadence that flags when assistant narratives drift. For enterprise brands whose buyers now start their research in an AI tool, this is becoming as basic as having a website that ranks.

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[Q]Questions