Getting help with AEO (2025): Tools, agencies and consultants that actually work

By Stefan Finch | Engineering-first consultant specialising in AI-driven growth systems Last updated: November 2025

Why finding AEO help remains confusing in Q4 2025

If you've tried to find help with answer engine optimisation, you've probably hit the wall.

Search the term and you'll get a mash-up of AI hype, generic SEO promises, and tool vendors claiming to "track AI visibility". Everyone sounds confident — until you ask them how to actually make your content citability-ready.

For marketing directors at mid-market industrial companies, this space feels like déjà vu: a new acronym, the same noise. You're being told to "leverage AI", "optimise for the future of search", and "get ready for ChatGPT". Yet no one explains what that means in practical, measurable steps.

For CMOs under board pressure to deliver growth, this creates a double bind. You need to show progress on AI adoption, but the market offers no clear path to measurable results. Agencies promise visibility; tools promise tracking. Neither delivers the systematic diagnostic and engineering work that actually moves citations and pipeline.

The confusion is understandable. Generative AI has rewritten the search landscape faster than the market could adapt. Tools are rushing to add "AI features", agencies are rebranding overnight, and LinkedIn is filled with self-proclaimed AI consultants.

The result: an overwhelming fog of advice — and very few credible operators who can turn AEO theory into commercial results.

Let me help you cut through it.

What to look for in an AEO partner: 5 criteria that matter

Before you evaluate tools or agencies, understand what actually drives AI visibility. Here's the framework that separates signal from noise:

1. Proof of diagnostic ability

A credible AEO partner should be able to run a structured diagnostic that quantifies your visibility gap:

• Can they measure your AI Visibility Score (AVS) or Citation Share?
• Do they audit your E-E-A-T signals (expertise, experience, authority, trust)?
• Can they show how technical structure impacts citation probability?

If they can't demonstrate a repeatable diagnostic process, you're not talking to an AEO operator — you're talking to a content vendor.

2. Engineering competence, not just copywriting flair

AEO is more technical than creative. It involves schema markup, JSON-LD structured data, and product information architecture.

Ask whether their team includes developers or data engineers who understand structured data implementation. If the answer is "no", they can't make your content machine-readable — full stop.

3. Verifiable metrics aligned to business outcomes

Any proposed plan should map to measurable outcomes:

• Increased citation count or share of voice in AI responses
• Higher entity recognition accuracy across search platforms
• Improved AI Visibility Score over defined time periods

These are the numbers that matter. "Traffic" is a lagging indicator, but citation share and trust velocity drive revenue.

4. System thinking, not one-off tactics

Beware any partner whose solution lives inside a spreadsheet or a one-month campaign. AEO is a continuous system that improves with every data point.

Look for frameworks, not tasks. Automation, not checklists. Partners who can explain how diagnostic data feeds engineering work feeds measurement feeds optimisation.

5. Transparency in methodology and measurement

If an agency can't show you how they measure progress, they probably aren't measuring it. AEO transparency means giving you access to the same diagnostic data the operator sees — visibility scores, schema health, and citation deltas.

When you find yourself asking "but how will we know it's working?", that's a red flag.

The landscape of AEO tools and platforms

Over the past 18 months, several tools have emerged to help marketers observe how their content appears in AI search results. You'll see names like PEEC.AI, Visibility.app, Kalicube, and new modules from SEMrush and Ahrefs.

Each provides part of the picture:

Monitoring tools (what they do well)

PEEC.AI lets you simulate prompts across ChatGPT, Gemini, and Perplexity, showing whether your domain is cited or mentioned. It's useful for spot-checking visibility but requires manual testing.

Visibility.app exports screenshots and share-of-voice data from Google's AI Overviews, giving you a sense of where you appear in AI-generated summaries.

Kalicube focuses on knowledge-graph optimisation and entity recognition, particularly useful for brand identity consistency across platforms.

SEMrush's experimental AEO module benchmarks domains that appear inside AI summaries, though as of Q4 2025 it's still in beta testing with limited coverage.

What monitoring tools don't do

These are useful directional tools. They show symptoms — where you appear, how often, and in which model outputs. But they don't fix the underlying problem.

None of these platforms actually make your content machine-readable, restructure PDFs, or implement the schema and author-credibility signals that answer engines need to cite you. They can monitor, but not engineer.

Even the biggest AI tools you use daily — GPT-4, Claude, or Copy.ai — can't do this. They generate language, not visibility. They'll happily create copy or headlines, but they can't tell you whether your technical data is structured correctly, whether your schema validates, or whether an AI model can verify your claims.

So what separates monitoring from engineering?

That's why so many industrial marketing teams end up frustrated. They're surrounded by tools, dashboards, and reports — yet nothing moves.

The difference lies at the infrastructure level. Real AEO work diagnoses how your content is interpreted by AI, identifies structural blind spots, and creates schema-ready outputs that machines can actually parse. That's the step most marketers are missing — the bridge between "we have content" and "AI trusts our content".

Why agencies fail in the AEO era

If tools show you symptoms, agencies often sell you band-aids.

The structural misalignment

Most legacy SEO agencies are built around a model that rewarded surface-level metrics: keywords, backlinks, rankings. Their playbooks haven't changed in twenty years.

Now, they're rebranding as "AI SEO specialists" — but the deliverables look identical: a monthly report, a content calendar, and some polite emails about "optimising meta titles".

This isn't malice; it's misalignment. Agencies are optimised for retainers and reporting, not for rebuilding data architectures.

The evidence of failure

The data backs this up. Only 48% of digital initiatives meet or exceed their business goals — Gartner (2024).

77% of B2B purchases are classified as "very complex", requiring multiple stakeholders and extended evaluation periods — Gartner.

Yet most marketing budgets are still spent on low-value SEO retainers — typically under £1,000 per month — that deliver minimal return and no systemic progress.

The pattern I see consistently: agency delivers a deck, rankings move slightly, traffic goes up, pipeline stays flat. Another report, another quarter gone.

The architectural problem

The core issue is architectural. AEO requires schema implementation, entity mapping, author validation, and structured data pipelines — not just blogs and backlinks.

Agencies can't deliver that because they don't own the systems layer. They're not equipped to connect your PIM to your CMS, validate your JSON-LD, or audit your author credibility architecture.

When visibility depends on data readability, not keyword density, the traditional agency model collapses.

Why DIY rarely works (and when it might)

Some marketing teams try to handle AEO internally. They install plugins, add FAQ schema, or paste JSON-LD snippets into pages.

It's a start — but it's not enough.

The diagnostic data gap

Without diagnostic data, these efforts become guesswork. AI visibility depends on deep entity alignment, consistency across multiple systems, and credible author signals — things you can't fix by toggling a plugin.

Even sophisticated in-house teams underestimate how fragmented their data really is. Product specifications trapped in PDFs, author pages with no schema markup, internal systems that contradict public content — AI doesn't reconcile that for you. It treats inconsistency as risk and simply excludes you from its results.

The velocity problem

DIY also fails because the goalposts keep moving. AI models update weekly. Search overviews change daily. Keeping up requires dedicated focus — not spare-time effort alongside your day job.

When DIY might work

That said, if you have in-house engineering resources, clear diagnostic tools, and executive commitment to systematic implementation, DIY becomes viable. The key is having someone who can bridge marketing, content, and data architecture — and the authority to make cross-functional changes happen.

Most teams don't have that combination.

The Growth Accelerator: a different approach

Here's what's different about how I work with clients on AEO.

Most marketers don't need another audit; they need progress. The Growth Accelerator is a 30-day sprint designed to move the needle fast and give your business measurable competitive advantage.

Three outcomes in 30 days

Each sprint focuses on three specific outcomes:

1. Visibility Engineering — identify and fix the structural issues that block AI citation. You'll know exactly what's broken and see the roadmap to fix it.

2. Authority Amplification — strengthen E-E-A-T signals and author credibility across your content architecture, making you citeable by AI systems.

3. Measurement and ROI Loop — set up dashboards to track citation share, AI Visibility Score, and entity recognition accuracy so you can measure what's working.

In one month, you'll go from "we think we're invisible" to "we know exactly what's blocking us and how to fix it".

Why 30 days

The Growth Accelerator isn't a subscription or a retainer. It's a results-focused engagement designed for under-resourced teams that need speed and clarity.

At the end of the sprint, you'll have a functioning AEO framework — one you can scale internally or with continued support. The choice is yours.

The 2025 competitive window

Your competitors are implementing AEO systems now. Not in 2026, not "when they're ready" — right now, in Q4 2025 and Q1 2026.

The companies that move in the next 90 days will build citation share that compounds. Those who wait will spend 2026-2027 trying to catch up while competitors own the AI citation graph in their category.

Why this matters for industrial companies

Generative AI has levelled the playing field. A small, well-structured industrial brand can now out-visibility a global enterprise simply by being machine-readable.

That's why AEO is no longer a "nice-to-have". It's defensive necessity and offensive advantage.

If your technical data is structured, your authors are credible, and your systems are clean, you'll be the source AI cites — and the brand buyers trust when they're evaluating solutions.

The knowledge moat

The companies that act now will build a lasting knowledge moat. Those who wait will find that once competitors own the citation graph, displacing them requires exponentially more effort.

This is the window. The question isn't whether to implement AEO — it's whether you'll lead the category or chase it.

What's your next move?

Book a Growth Accelerator Sprint → 30 days to diagnostic clarity, visibility engineering, and measurable competitive advantage

Three outcomes. One month. No retainer.

See exactly what's blocking your AI visibility and get the engineering roadmap to fix it.

Frequently asked questions

How much does AEO typically cost?

AEO costs vary widely based on scope and implementation approach. Monitoring tools typically range from £100-500 per month. Agency retainers claiming AEO services run £1,000-5,000 monthly but often deliver traditional SEO with rebranded language.

Proper AEO implementation — including diagnostic assessment, structured data engineering, and measurement systems — typically requires £15,000-50,000 investment depending on content volume and technical complexity. The Growth Accelerator sprint model provides a fixed-scope entry point.

Can we build AEO capability in-house?

Yes, if you have engineering resources who understand structured data, schema implementation, and can bridge marketing and technical systems. The challenge isn't any single skill — it's the combination of diagnostic ability, engineering implementation, and continuous measurement that most teams lack.

How long does it take to see results?

AI citation improvements typically appear within 60-90 days of implementing structured data and author credibility signals. However, visibility compounds over time as systems learn to trust your content architecture. Think of it as building authority, not running campaigns.

Is AEO different from technical SEO?

Yes, fundamentally. Technical SEO optimises for crawler access and ranking algorithms. AEO optimises for machine comprehension and citation-worthiness. The skill sets overlap but the objectives differ significantly.

Traditional SEO asks "can search engines find and rank this?" AEO asks "can AI systems understand, verify, and cite this?"