How Private Equity Firms Run AEO as a Portfolio Shared Service

How can a private equity firm run AEO across its portfolio as a shared service?

A private equity firm can run AEO across its portfolio as one central program that every company plugs into. Run as separate projects, AEO repeats the same research, tooling and learning in every company.

Without the shared layer, each company invents its own measurement, vendor relationships stay siloed, and the fund has no consistent AI visibility number to put in front of its investment committee or LPs.

KKR already works this way in other functions. KKR Capstone, its operations group, lists cross-portfolio programs as a focus area and has staff who run them, such as a shared procurement program open to every portfolio company.

In a 2017 article, Partners Group suggested central competency centers for an industry, function or region, so improvement work runs at the same quality and speed across a portfolio. The article predates AI search, but it describes the model AEO fits into.

A shared service also pays for what no single company would justify alone, such as one senior AEO operator working across the portfolio.

What should a private equity fund centralize in AEO across portfolio companies?

A private equity fund should centralize the measurement rules and the research method, and keep the message, the claims and the writing inside each company. In practice that means one shared definition of a mention, a citation and a factual error across the fund.

Central layer: the measurement and research standard

One set of definitions, one way of building a prompt set from buyer questions, one way of recording answers and one report format. This layer is small, and the monthly report below depends on it.

Templated layer: playbooks each company adapts

Persona research methods, answer-page brief templates, schema conventions and internal linking patterns. Each company adapts the playbooks that fit its market to its own buyers, products and proof.

Local layer: voice and content

The question set, the approved claims, the writing and the publishing calendar for one company. The fund should not write portfolio company content.

The common failure is pooling content production anyway. Kayana's analysis of portfolio-wide offshore capability found that standardizing how each company builds capacity tends to hold up, while pooling the work into one center tends not to.

The reason it gives is translation. Companies run different systems, calendars and industry vocabularies, and that overhead grows with every company added.

Which portfolio companies should share one AEO program?

Portfolio companies that share buyer personas, site infrastructure or geography should share one AEO program. Companies with none of these in common can still share the measurement standard, but the research and templates will not carry over.

• Shared buyer personas. SaaS companies that all sell to RevOps or finance leaders can reuse persona research and question patterns.
• Shared infrastructure. E-commerce and DTC brands on one commerce platform can share product schema and technical templates.
• Shared geography. Regional services businesses in the same markets can share location research and local citation sources.

In a mixed portfolio, a healthcare software company and a business services company share almost no buyer queries, so each needs its own seed questions, content and citations.

Ask whether one research brief could serve every company in scope. If it could, you have a program; if not, you have separate projects that happen to share an owner.

Revisit the grouping whenever the portfolio changes. A new platform deal, an exit or a company moving into a new market can each create overlap or remove it.

Who owns the shared service inside the fund?

An operating partner or a designated value-creation lead should own it, with a senior AEO lead running the central and templated layers day to day. The operating partner's job is to set priorities and clear blockers.

The company roles are in the table above. The central lead also needs enough authority to hold companies to the standard.

How should the shared service handle entity and structured data across companies?

Set one convention for how every company describes itself in structured data, then let each company apply it on its own site. A shared convention makes it easy to check that engines can tell the companies apart.

• Organization markup on one page. Google's Search Central documentation says Organization markup can help Google understand an organization's administrative details and disambiguate it in search results, and recommends the home page or an about page, adding that it need not go on every page.
• sameAs to real profiles. The same documentation describes sameAs as links to pages on other sites with more information about the organization, such as social or review profiles.
• Ownership only where it should be public. schema.org defines parentOrganization as the larger organization this one is a subOrganization of, and subOrganization as a relationship such as a subsidiary. Use them for a platform and the add-ons it legally owns, and leave the fund out unless that relationship should be public.
• brand for add-ons kept as brands. schema.org's brand property covers brands an organization maintains, which suits an add-on sold under its old name.
• Markup that matches the page. Google's guidance on AI features lists structured data that matches the visible text as a best practice.

Add-ons need the most care. An acquired company that changes its name or domain needs its entity records updated in the same project, following the steps for protecting citations through a rebrand.

Inside each portfolio company, the entity work itself follows our guide to building an entity that AI engines can place.

What if two portfolio companies sell into the same category?

Give each company its own territory of buyer questions before either starts publishing. The overlap shows up most in buy-and-build strategies, where a platform and its add-ons sell to the same buyers.

The tool is a citation territory map. It assigns each cluster of questions to the company best placed to own it, by segment, size or use case, and settles the overlap zones explicitly.

Measure each company against its own territory and track the shared questions separately. The full method, including when to split or firewall the work, is in when two portfolio brands compete for the same AI answer.

What AEO metrics should a PE operating partner see each month?

A PE operating partner should see one monthly portfolio report showing which companies are gaining visibility in AI answers, which are stuck, and where to put attention next. Define the metrics once for every company: citation share, order of mention, citation context, AI bot traffic and Search Console impressions.

• One row per company. Citation share and mentions on that company's own question set, with the change from last month.
• Representation, sources and activity. Track how the company is described, which sources the answer leans on, and what commercial activity follows.
• Traceable claims. Each AI-generated claim about a company should be traceable to the source and date behind it.
• Outside events. A short note when a model update or a new AI feature moved results that month.

Report weekly inside each company and monthly across the portfolio, and give the operating partner a short quarterly review. Company reports go deeper by persona and cited source, while the portfolio view stays as comparable as gross margin or net revenue retention.

Start with whether each company is named at all on its own questions, and add source support and commercial activity once a baseline exists.

Each company's own tracking can follow our walkthrough for measuring AI visibility. For the board pack, see which AEO metrics executives and procurement expect.

How do you roll it out across the portfolio?

Roll AEO out across the portfolio in phases, starting with one pilot company and following the six steps below.

Start with a pilot when the companies differ a lot or the standard is new. Start wider only when several companies share buyers and the standard has already been tested.

Phasing lets the fund fix the method before it is copied. Plan the second company's start around the pilot's results rather than a fixed calendar date, and add companies one or two at a time.

1. Write the standard first. Set the central layer described above before any company publishes.
2. Pick the pilot by readiness. Choose a company with a clear growth priority, reachable website owners and someone who can approve facts, not simply the largest business.
3. Run a baseline audit. Log each question with the engine, date, full answer, cited URLs and reviewer notes. Sort what you find into missing entirely, described wrongly, or named for the wrong buyer.
4. Fix entity records, then answer the gaps. Correct structured data and profiles, then cover the buyer questions where the company is missing or described wrongly.
5. Let the pilot sell the program. A mandate lands as compliance, while a peer already running the approach gets the next management team to adopt it.
6. Add new acquisitions at close. KKR Capstone helps boards and management build 100 day value creation plans after due diligence, a natural home for an AI visibility baseline. Review the target's names, domains and public descriptions during diligence too.

What happens to AI visibility work when a portfolio company is sold?

Everything built inside a portfolio company leaves with it at sale, and only the method stays with the fund (see the table above).

Partners Group's 2017 article explains why: portfolio companies are bought to be sold, so conglomerate-style deep integration does not suit private equity.

So build to transfer from the first day. KKR Capstone sets the same aim for its operating work: capability building and skill transfer back into each company.

Start the baseline early. A company that reaches the pre-exit window with no citation history has nothing to show a buyer's team.

If parentOrganization markup showed fund ownership, remove it at close.

Where does OnlyAEO fit?

OnlyAEO is an AEO and GEO agency that runs fully managed AEO end to end, with one team doing the work. In a portfolio, we carry out the AI search work for each company, and that company's own people review and approve every piece before anything is published.

What to look for in a portfolio AEO partner

A partner for a portfolio has to fit the shared-service model described above.

• One measurement method. Will every company be measured the same way?
• A separate record per company. Does each company keep its own facts and voice?
• Approval by each company. Does each company sign off before anything goes live?
• Answers anyone can rerun. Can the fund recheck any reported answer?
• Honest timelines. Will they say how long first results take, without guarantees?

How OnlyAEO works across a portfolio

Every portfolio company gets its own Context Graph: a single record, fed by the tools that company already runs, covering its facts, voice, buyers and competitors plus what it may and may not claim.

The rest of the managed service is described on our how it works page.

• Engines covered. Each company's work targets Google AI Overviews, Microsoft Copilot, ChatGPT, Perplexity, Claude, Gemini, Meta AI (Muse), Grok, DeepSeek and Instinct.
• Measurement. For tracking, the platform can query Grok, DeepSeek, Gemini, Claude and ChatGPT. A recommendation of the company is tallied on its own line, separate from a text-only mention or a bare source link.
• Five checks per company. Nothing for a company is published until it clears five checks: every number matched to an evidence row with unsupported claims cut, a humanizer pass in that company's voice, a link validator where one dead link stops publishing, a score of 85+ from a separate, independent scorer, and a human sign-off.
• Reproducible answers. The report stores each AI answer verbatim beside the prompt that produced it, so anyone, fund staff included, can paste that prompt into ChatGPT or Claude and get the answer again.

Our case studies are single-company work. A New York edibles retailer with roughly a dozen storefronts got location-aware content for the neighborhoods each store serves: one method, local content.

That retailer climbed from #50 at 1.5% AI visibility to #4 of 374 brands at 25% visibility, and its website became the category's most-cited source, ranking above New York State's official site. The details are on our case studies page.

Budget patience per company: the first results you can measure usually show up after 60 days or more. No portfolio company gets a results guarantee from us, and we do not promise specific rankings or citations.

Plans are scoped per company on a call. From each company we ask for a 30-minute call, its best writing and the facts it wants AI to get right, and its work starts with a free AI audit that sets the baseline.

The audit arrives within 48 hours and covers ChatGPT, Claude, Gemini and DeepSeek.

More questions about running AEO across a private equity portfolio

Can portfolio companies share AEO vendors and tools the way they share procurement?

KKR Capstone runs cross-portfolio programs so individual portfolio companies benefit from the scale of KKR's portfolio and its network of vendors. AEO tools can be bought the same way, within the exit rule above.

Where does AI Overviews traffic show up in each company's analytics?

Google includes traffic from AI Overviews and AI Mode in a site's total search traffic in Search Console. Search Console can feed the shared report as a search total, while the AI share has to come from tracking the answers themselves.

How should the fund handle a company in a regulated vertical or with a thin public footprint?

Both need extra groundwork before the shared program fits. Regulated companies need a stricter claims review before anything is published, and thin-footprint companies usually need entity records and third-party profiles built before answer content has much to stand on.