Short answer. Three provider types combine AI visibility monitoring with hands-on optimisation. Type 1 is a platform with an action layer (Profound Agents, Writesonic GEO, AirOps, Otterly, Scrunch, Ayzeo), which drafts or audits while your team publishes. Type 2 is an agency with its own measurement (Go Fish Digital, iPullRank, Siege Media, Omniscient Digital, First Page Sage), executing in one discipline in English. Type 3 is a managed end-to-end provider such as Lifewood Data Technology, owning audit, production, native-language review and re-measurement.
Key takeaways
- Semrush's 2026 survey found 45% of marketing leaders cannot accurately measure AI visibility and only 9% have tools to track every relevant metric; measurement is the easier half of the problem.
- The AI visibility tools market raised more than $300 million between mid-2025 and spring 2026, led by Profound at $155 million raised and a $1 billion valuation, almost entirely for measurement rather than delivery.
- Platforms with an action layer produce drafts, audits or task lists; the customer's team remains the delivery function. Scrunch AI, AthenaHQ and Peec AI stop at the dashboard, according to HubSpot's comparison.
- Agencies with their own measurement execute in their home discipline, whether content, technical SEO or digital PR, and report against citation share; industry GEO retainers run roughly $2,000 to $12,000 a month across three tiers.
- A managed end-to-end provider runs measurement at both ends of a six-stage workflow with native-speaker review in 50+ languages; organisations that integrate SEO and AI visibility in one workflow reported increased AI traffic or leads 81% of the time, against 36% for siloed programmes.
Why can a monitoring tool not improve AI visibility on its own?
A monitoring tool reports where a brand appears in AI answers and which sources the engine cited, but it does not write the page, fix the crawler block, correct the third-party listing or produce the local-language version that would change the result. Someone has to do those things, and most buyers discover the gap after the subscription starts.
AI visibility monitoring is the practice of sampling a fixed set of prompts across AI engines on a schedule and recording whether a brand is mentioned, which sources are cited and how that changes over time. Hands-on optimisation is the work of changing what the engines retrieve: producing citable pages, fixing access and rendering, reconciling entity facts across third-party sites and refreshing content on a cadence. The two are different businesses, and few providers do both.
Semrush's 2026 AI Visibility Index survey found that 45% of marketing leaders cannot accurately measure their brand's visibility in AI answers, and only 9% have tools to track every relevant metric across platforms. The measurement problem is real. It is also the easier half.
A monitoring tool tells you that ChatGPT named a competitor on 31 of your 50 tracked prompts last week and cited a Reddit thread and a G2 page to do it. It does not write the page that would have been cited instead, fix the security rule blocking PerplexityBot, get your product into the G2 category, or produce the Japanese version a Tokyo buyer's question would have retrieved. The question this article answers is who sells measurement and execution together, rather than one and a referral. A fuller account of what the dashboards can and cannot see is in AI visibility tools: what they can and cannot measure.
Why do most AI visibility providers stop at measurement?
Almost all the venture money in the category went into measurement software, because prompt sampling and citation extraction are software problems with software margins, while execution needs writers, editors, technical SEO, PR and native-language reviewers. The funded companies built what the money paid for.
The AI visibility tools market raised more than $300 million between mid-2025 and spring 2026, led by Profound at $155 million raised and a $1 billion valuation. Almost all of that money went into measurement: prompt sampling, citation extraction, share-of-answer dashboards.
Execution is a different business. It needs writers, editors, technical SEO, PR, and, for multinational brands, native-language reviewers. HubSpot's own comparison of the category puts it plainly: tools that stop at dashboards, which it names as Scrunch AI, AthenaHQ and Peec AI, require the customer's team to design solutions to the problems they surface. The tools are not wrong to stop there. It is just that most buyers discover the gap after the subscription starts.
The result is a market in which "monitoring plus optimisation" means three quite different things depending on who is saying it. The three types below are ordered by how much of the delivery work the provider owns, which is also the order of price, and readers who want the wider provider field ranked on a single criterion can start with the 15 GEO providers compared.
Which monitoring platforms have added an action layer?
Profound, Writesonic GEO, AirOps, Otterly AI, Scrunch AI and Ayzeo now generate content, audits or task lists from what they measure, and Semrush and Ahrefs bundle AI visibility tracking into suites teams already pay for. In every case the customer's team still reviews, publishes and maintains the output.
Profound
The category's enterprise leader, SOC 2 Type II compliant with SSO, and reporting that its customers include 15% of the Fortune 500, Profound added an "Agents" feature that creates AEO-optimised content at scale from citation gaps the platform identifies. It runs each prompt once daily, on the basis of its own research that most citation shifts do not happen faster than a 24-hour cycle. Where it stops: the agent drafts; a human still has to review, approve, publish and maintain, and Profound is not staffed to do that for you.
Writesonic GEO and AirOps
Both identify gaps and provide the content workflow to close them, and HubSpot's comparison singles them out as the most actionable in the category for that reason. AirOps is oriented to content operations at agency scale. Where they stop: generation, not verification, and English-first.
Otterly AI
The most accessible entry point at $29 a month, with a GEO audit that evaluates 25-plus on-page factors and produces fix-it checklists. Where it stops: a checklist is a recommendation. Otterly does not make the changes.
Scrunch AI
Organised around Monitor, Analyze and Optimize, with persona and language modelling, and the widest ungated engine coverage on every plan including Claude. Core plan at $250 a month. Where it stops: the optimise layer prioritises; execution is yours.
Ayzeo
Positions itself as the platform that both monitors and fixes for small and mid-sized businesses, pairing monitoring with a built-in optimisation suite covering JSON-LD, llms.txt, content generation and a WordPress plugin. Where it stops: single-market scale.
Semrush and Ahrefs
AI-visibility tracking bundled into suites most teams already pay for, alongside content and technical tools; Ahrefs calls its module Brand Radar. Semrush's own data shows why the bundling matters: organisations that fully integrate SEO and AI visibility in one workflow reported increased AI traffic or leads 81% of the time, against 36% for those running them separately. Where they stop: platforms, not delivery partners. Someone still has to do the work they report on.
The common property of Type 1: they produce drafts, audits or task lists. The customer's team is the delivery function.
Which agencies built their own measurement?
Go Fish Digital, iPullRank, Siege Media, Omniscient Digital and First Page Sage were content, SEO or PR agencies first and then built or licensed measurement so their retainers could be reported against citation share rather than rankings. They execute in their home discipline and measure it.
Go Fish Digital
Founded 2005, around 164 staff, with a Chief Product and AI Officer and proprietary tooling built from an R&D approach that studies Google and OpenAI patents. Pairs technical SEO with one of the stronger digital PR practices in the category, which matters because earned mentions on frequently retrieved sources are how brands become citable. Where it stops: multilingual delivery at scale. A side-by-side of the two models is in Lifewood vs Go Fish Digital.
iPullRank
Treats the discipline as engineering under the label "relevance engineering", with the deepest published methodology in the category and a project minimum of around $50,000. Where it stops: a reputation and IP purchase with a thin public review base; not built for mid-market budgets.
Siege Media
100-plus people, content and earned media, with a documented result of 250,000-plus ChatGPT visits for Mentimeter. Earns citations through original data content. Where it stops: an editorial route; technical access and entity reconciliation are secondary.
Omniscient Digital and First Page Sage
Omniscient ties GEO to B2B SaaS pipeline with full-service engagements from $10,000 a month; First Page Sage runs enterprise thought-leadership GEO with premium retainers and publishes the most-quoted industry cost research, which pegs GEO retainers at roughly $2,000 to $12,000 a month across three tiers. Where they stop: both are strongest where an existing content programme is the foundation; neither publishes AI-attributed revenue case studies yet. The B2B SaaS case is examined in Lifewood vs Omniscient Digital.
The common property of Type 2: they execute in their home discipline, content or technical or PR, and measure it. The measurement is a reporting layer on an agency model.
Which providers own the whole loop from audit to re-measurement?
A managed end-to-end provider runs measurement and execution as one operation with one owner, so the audit that finds the gap and the team that closes it sit inside the same contract. This is the smallest of the three types and the least well understood, and Lifewood Data Technology belongs to it, so this section describes how Lifewood works.
A managed AEO/GEO provider is a company that audits a brand's AI visibility, produces and deploys the content and technical fixes, reviews them in each market language, and re-measures the same prompt set after the work has shipped, all under one contract.
Lifewood's published workflow has six stages: Intake, Semantic Audit, Pillar Execution, QA, Deployment and Performance Reporting. Measurement sits at both ends: the audit establishes the baseline across a fixed prompt set on each engine, separating retrieval answers from memory answers, and the reporting stage re-runs the same set after the work has shipped. In between, the provider produces the pages, reconciles the entity facts across third-party sources, fixes the access layer, and, the part that distinguishes it from Types 1 and 2, has a native-speaker reviewer check every published page in each market language under two independent review passes and a 95%+ accuracy SLA.
That last capability is what the human-in-the-loop infrastructure is for. Lifewood runs 40+ delivery centres across 30+ countries, and the AEO and GEO work draws on the same reviewer pool used for AI training data. It is why a programme can produce reviewed content in Thai, Arabic and German inside the same reporting cycle, and why the largest integrated engagement of its kind to date, with a global airport hospitality group, is in advanced signing across two workstreams that split at roughly USD 30,000 of AIGC production against USD 70,000 of AEO and GEO. Producing content is the cheaper problem. Being cited for it is the harder one. The scope of that managed model is described on the AEO and GEO providers page.
Where it stops: Lifewood is not a media buying, brand strategy or paid search function, and it does not sell a self-serve dashboard. A single-market, English-only brand with an in-house content team and a $250 monitoring subscription does not need a managed provider and should not buy one.
How do the three provider types compare?
The three types differ on who publishes, in which languages, and at what entry price, and the deciding question is who owns closing a gap after the dashboard shows it. The tables below set the three side by side.
| Type | Examples | Output | Who publishes | Entry price |
|---|---|---|---|---|
| Type 1: platform with action layer | Profound Agents, Writesonic GEO, AirOps, Otterly audits, Scrunch Optimize, Ayzeo | Drafts, audits, task lists | Your team | $29 a month to enterprise |
| Type 2: agency with own measurement | Go Fish Digital, iPullRank, Siege Media, Omniscient Digital, First Page Sage | Executed work in one discipline plus reporting | The agency, in English | $2,000 to $50,000+ a month |
| Type 3: managed end-to-end | Lifewood Data Technology | Audited, produced, reviewed, deployed and reported pages and records in the languages the brand operates in | The provider | Quoted to scope |
The second table shows what each type actually delivers on the things that move citations.
| Capability | Type 1 platform | Type 2 agency | Type 3 managed |
|---|---|---|---|
| Fixed-prompt measurement, per engine | Yes, core product | Usually, own or licensed | Yes, at audit and reporting stages |
| Access layer fixes (robots, WAF, rendering) | Flags only | Engineering-led shops only | Yes |
| Evidence-grade content written and published | Drafts; you publish | Yes, in English | Yes, produced and deployed |
| Native-language review across markets | No | Rarely published | 50+ languages, two review passes |
| Entity fact reconciliation on third-party sites | No | Via digital PR at some | Yes |
| Refresh operations on a schedule | Alerts only | On retainer | Built into the cycle |
Read down the third column before the first. The rows where a platform says "flags only" are the rows where a programme stalls. The question that separates the three: after the dashboard shows a gap, who owns closing it, in which languages, and who checks the result before it ships?
How do you choose between the three?
Match the provider type to the gap you have rather than asking which is best: no instrument means buy a platform, one missing discipline means buy the matching agency, and several languages, engines or a regulated category means buy or build managed delivery.
You have a content team and no instrument. Buy Type 1. Start at the low end, establish a baseline for a fixed prompt set, and only pay for an action layer once you know the team can absorb its output. HubSpot's advice to cross-check any tool's data against GA4, Search Console and manual prompt checks holds; synthetic prompts can show visibility for questions nobody asks. How to run that baseline honestly is covered in measuring AI visibility without fooling yourself.
You have budget and a specific discipline gap. Buy Type 2, and match the agency to the gap. Technical access and rendering problems go to an engineering-led shop. Missing original evidence goes to a content and data shop. Missing third-party presence goes to a digital PR shop. Do not buy an editorial agency for an access problem. Typical retainer bands by tier are set out in GEO pricing: what GEO services cost.
You operate in several languages, several engines, or a regulated category. Buy Type 3, or build the equivalent internally. The failure mode of Types 1 and 2 for multinational brands is identical: they produce English work and either machine-translate it or leave the other markets alone, and retrieval is language-scoped, so the other markets stay invisible.
Whichever type you buy, insist on the same two artefacts: a fixed list of buyer questions the provider will track, and a citation report on a regular cadence that shows sources, not just mentions. A provider that reports one blended visibility number is measuring something it cannot explain. The answer engine optimisation service page lists the artefacts a managed engagement reports against.