AI SEO Consultancy: What One Actually Does, and How to Hire the Right One

An AI SEO consultancy is hired for a problem traditional search work was never built to solve: your brand being described, summarised and recommended by systems that answer the question instead of listing the pages. If you are searching for an AI SEO consultant or comparing an AI SEO agency against your incumbent, the decision is harder than it looks, because the category is eighteen months old and almost everyone in it is claiming a track record the field has not existed long enough to produce.

This page is written for the person doing that evaluation. It sets out what the work actually involves, how answer engine optimization and generative engine optimization differ from the SEO you are already paying for, what a credible first ninety days looks like, the seven questions worth asking any vendor and what a good answer to each sounds like, and what drives the cost. It also states plainly what this work cannot do, because AI search visibility has a ceiling and anyone hiding it is selling you the ceiling.

Quick Overview (Key Takeaways)

An AI SEO consultancy optimises for the systems that *answer* queries (ChatGPT, Google AI Overviews, Perplexity, Copilot, Gemini) rather than only for the ten blue links, which means the deliverable is citation and inclusion, not just ranking position.

The practical work splits into three layers: entity clarity (can a model identify who you are and what you sell), retrievability (can crawlers and retrieval systems actually reach and parse your content), and answer coverage (does your content match the shape of the questions being asked).

The single most useful evaluation question is "how will you measure this", because any consultancy that cannot describe a repeatable measurement method is selling a narrative rather than a service.

Engagement shapes fall into four patterns: a fixed-scope audit, a monthly retainer, a project build, and an advisory arrangement. Cost is driven by entity complexity, content volume, technical debt and the number of markets or locations you need covered.

Pure Marketing Group operates out of Montclair, New Jersey, publishes its client work openly, and treats AI search as an extension of disciplined technical and editorial SEO rather than a replacement for it.

Why This Page Exists

If you are reading this, you are probably not looking for a definition. You have most likely noticed one of three things: your organic traffic is flat or declining while your rankings have not moved much, a prospect mentioned that ChatGPT recommended a competitor instead of you, or your board asked what you are doing about AI search and you did not have a confident answer.

Those three situations have one thing in common. They are all symptoms of a visibility problem that traditional reporting does not surface. Rank trackers do not tell you whether an assistant cited you. Analytics platforms historically bundled or dropped AI referral traffic. Nothing in the standard SEO dashboard was built to answer the question "when someone asks a machine about our category, do we come up".

An AI SEO consultancy exists to close that gap. This page explains what the work actually involves, how to tell a competent practitioner from someone who renamed their service page last quarter, what engagements typically cost and why, and what you should expect in the first ninety days.

What an AI SEO Consultancy Is

An AI SEO consultancy is a specialist practice that makes a brand findable, correctly described, and quotable inside AI-mediated search. That covers Google AI Overviews and AI Mode, ChatGPT with browsing and its shopping and recommendation surfaces, Perplexity, Microsoft Copilot, Gemini, and the growing set of vertical assistants sitting on top of these models.

The discipline goes by several names depending on who is speaking. Answer engine optimization describes optimising for systems that return a synthesised answer rather than a list of links. Generative engine optimization describes the same territory with emphasis on generative models specifically. Large language model optimization, or LLMO, is a third label for substantially overlapping work. We have written about the distinctions between these terms in detail, because the vocabulary genuinely matters when you are writing a scope of work: see our breakdowns of SEO versus AEO versus GEO and LLMO versus SEO if you need to settle internal terminology before you brief a vendor.

What matters commercially is simpler than the acronym argument. There is now a layer of software between your prospect and your website, and that layer decides whether you are mentioned at all. An AI SEO consultant works on that layer.

How It Differs From a Traditional SEO Agency

This is the question most buyers actually want answered, because the two services look similar from the outside and are often sold by the same people. Here is the honest version.

A traditional SEO agency optimises a page so that it ranks for a query, and the success condition is a position on a results page that produces a click. An AI SEO agency optimises so that a retrieval system selects your content as source material, and the success condition is inclusion in a generated answer, ideally with a citation and ideally described in your own preferred language.

That difference in success condition changes the deliverables. Below is what the work looks like in practice.

Entity and knowledge graph work

Language models reason about organisations as entities, not as URLs. If a model cannot confidently establish that "Pure Marketing Group" is a marketing agency in Montclair, New Jersey, that offers a specific set of services, it will either omit you or describe you inaccurately, and inaccurate description is arguably worse than omission.

Concrete deliverables here include a consolidated entity definition applied consistently across your site, structured data that expresses your organisation, services, locations and people in machine-readable form, disambiguation from similarly named businesses, and consistency work across the third-party sources models are trained on and retrieve from. Google's own documentation on structured data and rich results remains the baseline reference for the markup side of this, and it is worth reading before you accept any vendor's claims about schema. Our entity SEO service page covers how we sequence this work.

Retrievability and crawl access for AI systems

There is a mechanical prerequisite that gets skipped constantly. If AI crawlers cannot reach your content, nothing else in the programme matters. That means auditing robots directives for the specific user agents used by AI systems, checking that critical content is present in the server response rather than only assembled by client-side JavaScript, confirming rendering behaviour, and resolving redirect chains and canonical conflicts that fragment your signals.

OpenAI publishes its crawler identifiers and access rules in its bots documentation, and every major provider now maintains something similar. A competent consultancy will have checked these against your configuration before proposing a single piece of content. If a proposal arrives without any mention of crawler access, that is a meaningful signal about depth.

Answer coverage and content architecture

Models retrieve passages, not pages. So the content work shifts towards self-contained, extractable units: direct answers positioned near the top of a section, clear definitional statements, comparison structures, and specific claims with attribution rather than vague marketing prose.

Deliverables typically include a query-to-answer coverage map that shows which real questions in your category you currently have no retrievable answer for, restructuring of existing high-value pages so that passages are extractable, and new content built to the shape of the gaps. Our generative engine optimization services and LLM optimization pages describe how these two workstreams interlock.

Measurement of AI search visibility

Traditional SEO measurement does not transfer cleanly, so the consultancy has to build the instrument as part of the engagement. That means a defined prompt set representing how buyers in your category actually ask, repeated sampling across multiple assistants, logging of whether you appear and how you are described, share-of-voice tracking against named competitors, and referral traffic segmentation so AI-sourced sessions are separated from general organic.

This is where AI search visibility stops being a slogan and becomes a number you can put in front of a finance director. It is also the deliverable most likely to be missing from a weak proposal, which makes it a useful filter.

AI Overviews and SERP feature work

Google's AI Overviews sit in a middle ground: they are generated answers, but they are still produced within a search results page that responds to classical ranking and content signals. Optimising for them is a distinct competency with its own tactics, and we treat it as such on our AI Overview optimization page. If you want the conceptual separation spelled out, our GEO versus SEO comparison is the shorter read.

What You Should Expect in the First Ninety Days

Vendor proposals vary, but a credible engagement tends to follow a recognisable arc.

The first two to three weeks should be diagnostic, not productive. You should receive a baseline of where you currently appear across assistants, an entity audit showing how you are presently described and misdescribed, a technical report on AI crawler access, and a gap analysis of query coverage. If a consultancy starts publishing content in week one, they are guessing.

Weeks four through eight should be remediation. Entity definition applied, structured data corrected and validated, crawler access fixed, and the highest-value existing pages restructured for extractability. This phase usually produces the fastest measurable movement, because most sites have accumulated genuine defects that suppress inclusion for mechanical reasons rather than competitive ones.

Weeks nine through twelve should be built and re-measure. New answer content is shipped against the mapped gaps, and a second measurement pass runs against the same prompt set so you can see directional movement. Expect movement, not transformation. Models refresh their retrieval and training inputs on their own schedules, and some gains surface slowly.

Anyone promising specific citation volumes in a specific timeframe is describing something they cannot control. Be wary.

How to Evaluate an AI SEO Consultancy

This is the section worth printing before your vendor calls. Each question below has a tell, and the tell is more useful than the answer.

"How will you measure whether this worked?"

What a good answer sounds like: a described method. A prompt set built from your category and buyer language, a stated sampling cadence, the specific assistants covered, a baseline captured before work begins, and named competitors tracked alongside you. They should also volunteer the limitations, notably that assistant outputs are non-deterministic and that single spot-checks prove nothing.

Red flag: "we track AI visibility" with no method attached, or a single proprietary score with no explanation of its inputs.

"What will you fix before you write anything?"

What a good answer sounds like: crawler access, rendering, structured data validity, canonical and redirect hygiene, and entity consistency. A real practitioner treats content as the second move, because publishing into a site that AI crawlers cannot parse wastes your budget.

Red flag: a content calendar as the primary deliverable.

"Show me a query where you are visible and a query where you are not."

What a good answer sounds like: both, without hesitation, including the losing one. A consultancy that has actually run this measurement on itself will know its own weak spots. This is a strong proxy for whether they run the process they are selling.

Red flag: only wins, or screenshots without the prompt shown.

"Which parts of this are conventional SEO?"

What a good answer sounds like: most of the technical foundation, and they should say so plainly. Crawlability, information architecture, internal linking, page quality and authority still do heavy lifting. A consultancy that claims AI search is an entirely separate discipline requiring an entirely separate budget is usually selling novelty.

Red flag: "traditional SEO is dead". It is not, and the claim tells you they are marketing to fear.

"What happens if a model describes us incorrectly?"

What a good answer sounds like: a remediation path. Correcting the authoritative sources the model retrieves from, strengthening first-party entity signals, addressing the third-party pages carrying the wrong information, and re-measuring. They should also be honest that you cannot edit a model's output directly and that correction takes time.

Red flag: a claim that they can get a model to change its answer on request.

"Who does the work, and what happens to the knowledge when the engagement ends?"

What a good answer sounds like: named practitioners, and documentation you keep. Your entity definition, prompt set, coverage map and measurement baseline should be handed over as assets. If everything lives in a vendor dashboard you lose on cancellation, you have rented visibility rather than built it.

Red flag: vagueness about who executes, or deliverables that exist only inside a platform you do not own.

"What would make you decline this engagement?"

What a good answer sounds like: something specific. A site that cannot be technically remediated within the engagement, a category where the client has no genuine differentiation to describe, or a budget too small to cover both diagnosis and remediation. Consultancies that accept every brief are optimising for revenue, not outcomes.

Pricing: Engagement Shapes and What Drives Cost

Most competitors avoid this section. That is unhelpful, because the shape of an engagement tells you more about fit than a number does. Below are the four patterns you will encounter, what each is appropriate for, and the variables that move cost in either direction. Actual figures vary by market, scope and agency, so treat this as a framework for reading a proposal rather than a rate card.

The fixed-scope audit

A one-time diagnostic that produces a baseline measurement, an entity audit, a technical AI-crawler report and a prioritised remediation plan. This is the correct first purchase for most organisations, because it converts a vague worry into a cost plan and it lets you evaluate the consultancy's thinking before committing to a retainer. Our AI visibility audit is structured this way deliberately.

Use it when: you do not yet know the size of the problem, or you need internal evidence before requesting a budget.

The monthly retainer

Ongoing work combining measurement, technical maintenance, entity upkeep and content production. This is the standard shape for organisations treating AI search as a continuing channel rather than a one-off fix. Retainers should specify deliverable volumes and a reporting cadence, and they should include re-measurement against a fixed prompt set so progress is comparable month to month.

Use it when: you have competitive pressure in your category and need sustained coverage.

The project build

A defined, finite piece of work such as an entity and structured data implementation, a site migration with AI retrievability protected, or a content cluster built to close a mapped gap. Priced by scope, delivered and closed.

Use it when: you have in-house marketing capacity and need a specialist to execute one hard component.

Advisory and enablement

A smaller arrangement where the consultancy trains and directs your internal team, reviews their work, and owns measurement while your people own execution.

Use it when: you have a capable team and a limited external budget, and knowledge transfer matters more than throughput.

What actually drives the cost

Five variables do most of the work in any quote you receive.

Entity complexity

A single-location business with one clear service line is a straightforward entity problem. A multi-brand group with overlapping service names, several legal entities and a history of acquisitions is not, and the entity resolution work scales accordingly.

Content volume and condition

Restructuring forty existing pages for extractability costs less than writing forty new ones, but auditing four hundred pages to find the forty that matter is itself real work.

Technical debt

A site that renders content server-side on a modern stack needs little remediation. A site built on a page builder with client-side content assembly, broken canonicals and years of redirect chains needs substantial engineering before any optimization lands.

Market and language coverage

Each additional market, language or location multiplies the prompt set, the measurement effort and the entity work.

Competitive density

In a category where three well-resourced competitors are already doing this work, achieving inclusion takes more depth than in a category where nobody has started.

One further note on value. If a single new client is worth a meaningful five-figure sum to you, the arithmetic on an audit is not difficult. If your average order value is small and your volumes are modest, an advisory arrangement is likely the honest recommendation, and a good AI SEO consultant will tell you that rather than selling you a retainer.

Evidence: How to Read a Consultancy's Case Studies

Published work is the most useful signal available to you, and also the easiest to misread. A few rules.

Look for mechanisms, not just outcomes. A case study that says traffic rose by some percentage tells you nothing transferable. One that explains what was diagnosed, what was changed, and why that change produced the effect tells you whether the practitioner understands causation. When you review the client work we publish at our work page, read for the sequence of decisions rather than the headline figures.

Look for named clients where possible. Anonymised case studies are sometimes unavoidable for contractual reasons, but a portfolio that is entirely anonymous is harder to verify.

Look for relevance of constraint, not industry. Buyers over-index on "have you worked in my vertical". The more predictive question is whether the consultancy has handled your *constraints*: your platform, your site scale, your governance process, your multi-location structure. Those determine whether the work is deliverable.

Look for what did not work. Practitioners with real experience have failed engagements and can describe what they learned. That candour is a better indicator than a wall of testimonials.

What We Have Published, With Its Limits Stated

Applying those rules to ourselves, here is what our own published work shows, and what it does not.

Named clients and figures, as published on our work page: Zahira Domenech, 15.1x return on ad spend, 137,125 dollars in revenue on 8,520 dollars in spend. Get A Rate, 1.3 million impressions at a 0.68 dollar cost per click, which the page describes as 65 percent below the industry benchmark. MamaSushi, 629,889 impressions and 21,668 clicks on 3,236 dollars of spend. Pizza Love, 868 purchases against 123,000 impressions. Pampers, through AIM Research, 175 leads at 3.90 dollars each. Elevate Rope, first position rankings with sessions up 24 percent. Sulex International, sessions and pageviews both up 40 percent.

Now the limits, because a figure without them is decoration.

These are campaign results, mostly paid media and conventional search, not AI search citation results. Nobody has a five-year track record in answer engine optimization, including us, because the field is younger than that. Anyone showing you a decade of AI search case studies is showing you something else relabelled.

The figures above are reported as published, without the comparison window or attribution model attached to each one. A return on ad spend figure means different things depending on the attribution window and whether it counts blended or platform-reported revenue. If any of these numbers matter to your decision, ask us which basis it was measured on, and be suspicious of any agency that cannot answer that about its own case studies.

Two engagements are excluded from the list above at the client's preference. A portfolio that shows everything is either very lucky or not checked.

We are telling you this because the alternative is a page of round numbers with no method, and you have no way to tell those apart from invented ones. A limitation volunteered against our own interest is the only claim on this page you can verify without taking our word for anything.

Why Local Presence Still Matters

Pure Marketing Group is based in Montclair, New Jersey, and works with organisations across the New York metropolitan area and beyond. There is a practical reason we mention geography on a page about a technology-led service.

First, entity work depends on verifiable local signals. A consultancy with a genuine physical presence, a consistent address, and real local citations understands the problem from the inside, because it has had to solve it for itself. Models resolve organisational identity partly through exactly these signals.

Second, the engagements that go well tend to involve people in a room. Entity definition is a strategic conversation about what your business actually is and how it should be described, and that conversation benefits from proximity. Our Montclair AEO agency page covers our local practice specifically.

Third, if you are a local or regional business, your AI search problem is different in kind. Assistants answering "best X near me" style queries lean heavily on local entity signals, review corpora and proximity data. That is a different technical programme from national category visibility, and it should be scoped differently.

What This Work Does Not Do

Three honest limits, because a consultancy that will not state them is not being straight with you.

It does not give you control over model output. You influence what a system retrieves and how clearly it can describe you. You do not author the answer. Any promise of guaranteed wording is a promise nobody can keep.

It does not substitute for having something distinct to say. If your positioning is identical to four competitors, a model has no reason to prefer you, and optimization cannot manufacture a differentiator that does not exist. Sometimes the correct first engagement is positioning work, not generative engine optimization.

It does not deliver on a fixed timeline. Retrieval indexes, training data refreshes and assistant behaviour all change on schedules you do not control. Technical remediation often shows movement within weeks. Entity correction across third-party sources can take considerably longer.

Getting Started

If you are evaluating an AI SEO agency right now, the most efficient next step is almost always a scoped diagnostic rather than a retainer commitment. A baseline measurement of your current AI search visibility, a technical audit of whether AI systems can reach your content, and an entity report showing how you are presently described will tell you the size of your problem and give you the internal evidence you need to fund the fix.

That is deliberately a low-commitment first move. It also lets you assess how a consultancy thinks before you sign anything longer. If you want to talk through whether this applies to your situation, our contact page is the direct route, and it is worth bringing your current organic performance data and a list of the queries you believe your buyers are asking.

Frequently Asked Questions

What is the difference between an AI SEO consultancy and an AI SEO agency?

In practice the terms are used interchangeably, and the distinction is about delivery model rather than discipline. A consultancy typically emphasises diagnosis, strategy and advisory work, often with senior practitioners engaged directly and knowledge transferred to your team. An agency more often includes production capacity: content writing, development, design and ongoing execution. Many firms, including ours, do both. What matters is which mix your situation needs. If you have an in-house team, you probably need consultancy. If you need the work executed end to end, you need agency capacity.

Is answer engine optimization just SEO with a new name?

No, though it shares a large technical foundation. Conventional SEO optimises for ranking position and the click that follows. Answer engine optimization optimises for inclusion and citation inside a generated answer, where there may be no ranked list at all. The overlap is real: crawlability, site quality, structured data and authority matter to both. The differences are also real: passage-level extractability, entity clarity and measurement methodology are materially different problems. Treating it as purely a rebrand leads to under-investment in entity and measurement work, which is where most of the new value sits.

How do you measure AI search visibility if results change every time?

You measure with repeated sampling rather than single checks. A defined prompt set is run at a fixed cadence across multiple assistants, and appearance, position within the answer and the language used to describe you are logged each time. Because outputs are non-deterministic, the meaningful unit is a rate across many samples, not a single result. You then combine that with referral traffic segmentation, so AI-sourced sessions are separated from general organic in your analytics. Any consultancy presenting a single screenshot as evidence is showing you noise.

How long before we see results?

It depends on which layer is broken. If AI crawlers are blocked or your content is not present in the server response, fixing that can produce movement within a few weeks of re-crawling. If your entity is inconsistently described across the third-party sources models rely on, correction takes longer because you are waiting on those sources and on model refresh cycles. If you are competing in a dense category against firms already doing this work, expect a sustained programme rather than a quick fix. A credible consultancy will tell you which of these you are, based on the audit, before quoting a timeline.

Do we need this if our traditional rankings are strong?

Often yes, and strong rankings can actively mask the problem. Ranking well and being cited are correlated but not identical, and there are well-documented cases of sites holding position while losing click-through because a generated answer satisfied the query above them. There are also sites that rank modestly but get cited frequently because their content is unusually extractable and their entity is unusually clear. The only way to know which describes you is to measure inclusion directly rather than inferring it from rank.

Can you guarantee we will appear in ChatGPT or AI Overviews?

No, and you should decline any proposal that offers a guarantee. Inclusion is decided by systems we do not control, using retrieval and generation processes that change without notice. What a competent AI SEO consultant can commit to is the work: crawler access confirmed, entity defined and marked up correctly, coverage gaps identified and closed, and visibility measured consistently so you can see whether the programme is moving. Commitments should be about deliverables and measurement, never about a third party's output.

Should we hire in-house or use a consultancy?

Both, ideally in sequence. The diagnostic and architectural work benefits from a specialist who has done it across many sites and knows what the common defects look like. The ongoing content and maintenance work is often cheaper and better in-house, because your team knows your subject matter. A sensible path is an external audit and remediation programme, with explicit knowledge transfer, followed by in-house ownership of the sustaining work and a lighter advisory retainer for measurement and review.

What should we prepare before the first conversation?

Four things make the first call much more productive. Your current organic performance data covers at least the last twelve months. A list of the questions you believe your buyers ask, in their language rather than yours. Access details or at least a clear description of your CMS and hosting stack, since technical constraints shape everything. And an internal answer to the question "what makes us the right choice", because entity and content work both depend on having a genuine, statable differentiator.

Does this work differently for local businesses?

Yes, substantially. Local queries route through a different signal mix, weighted heavily towards business profile accuracy, review corpora, address and proximity consistency, and local citation coherence. A national category visibility programme and a local AI search programme share tooling but diverge in priority order. If you are a single-location or regional business, ensure any proposal you receive addresses local entity signals specifically rather than treating your site as a national publisher.

What is generative engine optimization compared to LLM optimization?

The distinction is largely emphasis rather than substance. Generative engine optimization tends to describe optimising for generative answer surfaces broadly, including Google's AI features. LLM optimisation tends to emphasise the underlying models and how they represent and retrieve information about your brand. In a scope of work, what matters is not which label is used but which deliverables are listed underneath it. Ask for the deliverables, then compare vendors on those rather than on vocabulary.

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