How to Run GEO Services as an Agency: A Step-by-Step Playbook

Clients are starting to ask why they're invisible in ChatGPT, Perplexity, and Google's AI Overviews, even when they rank well on traditional search. That gap is your opportunity. Running services as a generative engine optimization agency is a real, billable practice, but it requires a structured approach that's different from classic SEO. This guide walks you through exactly how to build and deliver those services.

Step 1: Audit Your Client's Current AI Visibility

Before you can improve anything, you need a baseline. This is the discovery phase that justifies your engagement and sets measurable goals.

Checkpoint: You should leave this step with documented baseline scores for at least 3 AI engines.

Run 10-20 target queries in ChatGPT, Perplexity, Claude, and Gemini that your client would want to appear in. These are not branded queries, they're informational and commercial-intent queries in the client's category.
For each query, note: Is the client cited? Where in the response? What competitors are cited instead?
Use a tool like Bingly to automate this across engines and track it over time rather than running manual spot checks. Manual audits degrade fast, AI answer sets shift weekly.
Document the client's current content assets: blog posts, landing pages, product pages, FAQs, and any existing structured data.

This audit is your deliverable for the discovery phase. It should answer: "In AI search today, who owns this topic, and it isn't your client yet."

Step 2: Map the Content Gaps AI Models Are Filling

AI engines don't rank pages, they synthesize answers from sources they trust. The content gap analysis for GEO is fundamentally different from a traditional keyword gap audit.

Checkpoint: Produce a gap document listing 5-10 topic areas where the client has no authoritative content.

Study the AI-generated answers from Step 1 closely. What sources are being cited? What specific claims, stats, or frameworks are those citations supporting?
Check whether those cited pages have any of the following: clear entity definitions, specific statistics, process explanations with steps, expert attribution, or structured schema markup. These are citation triggers.
Cross-reference with the client's existing content. Is there a mismatch between what AI engines are looking for and what the client has published?
Review community conversations, Reddit keyword research is underrated here. The questions people ask on Reddit are close proxies for what users ask AI engines. If a subreddit thread has 300 upvotes asking "how do I compare X vs Y", and your client has no page that answers that directly, that's a gap.

The output of this step is a prioritized content brief list, not a keyword list. Each brief should specify what the page needs to do to become a citable source.

Step 3: Build the Content Infrastructure for AI Citability

This is where most of the production work happens. A generative engine optimization agency builds content that AI models want to cite, not content that search algorithms want to rank. The principles overlap but are not identical.

Checkpoint: Launch or revise at least 5 pages that address the gaps from Step 2, each meeting the citability checklist below.

Citability checklist for each page:

Clear entity definition up top. AI models synthesize answers fast. The first 100 words should unambiguously define what the page is about, no burying the lede.
Specific, verifiable claims. Pages with cited statistics, named studies, or exact figures get pulled into AI answers more reliably than vague editorial content.
Structured schema markup. FAQ schema, HowTo schema, and Article schema all increase the probability of being indexed and cited. See the schema markup for AI search guide for implementation specifics.
An llms.txt file. This is a lightweight signal to AI crawlers about what your client's site covers. It takes under an hour to implement and is increasingly expected. The llms.txt guide walks through the format.
Concise, quotable summaries. Add a TL;DR or summary block at the top of long-form content. AI models often pull these verbatim.

Don't try to do all of this at once. Prioritize the 2-3 pages most likely to capture high-value queries and build from there.

Step 4: Build the Measurement and Reporting Loop

GEO reporting is different from SEO reporting. There's no rank tracker that shows position 1-10. You're measuring presence, prominence, and sentiment in generated answers, and you need a consistent methodology clients can understand.

Checkpoint: Deliver a GEO report at the end of month one with before/after visibility data.

Define your core query set with the client (20-50 queries representing their most important topic areas). These become the fixed test set you run every week or month.
Track three metrics per query: cited (yes/no), position in the response (early mention vs. buried vs. not present), and competitor citations (who else shows up).
Aggregate into a visibility score. Clients respond well to a simple number: "Your AI visibility score went from 24% to 41% this month." AI citation tracking tooling can automate this calculation.
Report on content performance separately: which pages got cited, in which engines, and for what queries. This creates a feedback loop, you can see which content is working and double down.

Set expectations clearly in your SOW: GEO results lag content production by 2-8 weeks, because AI models need to crawl and index new content before incorporating it into answers. This is similar to organic SEO lag but slightly less predictable.

Step 5: Layer in Brand Monitoring for Ongoing Intelligence

The best generative engine optimization agencies don't just optimize for AI visibility, they monitor what AI engines and communities are saying about clients on an ongoing basis.

Checkpoint: Set up monitoring so you catch new AI mentions and community signals within 48 hours.

Monitor AI-generated answers weekly for new citations, competitor moves, and any misinformation or outdated claims about the client. AI engines sometimes perpetuate stale content, catching this quickly is a service clients genuinely value.
Run parallel community monitoring on Reddit and forums where the client's audience is active. When a thread surfaces a misconception your client could address, that's content brief material. It's also a direct signal of what AI engines will encounter next, since community content is heavily crawled.
Combine this with a GEO tools stack that lets your analysts work efficiently. Manual monitoring doesn't scale past a small number of clients.

The combination of AI visibility tracking and community signal monitoring is what separates a transactional content vendor from a strategic generative engine optimization agency partner. Clients who see that you're watching and responding, not just producing and reporting, retain longer and expand engagements faster.

Building a Repeatable Agency Practice

The workflow above is a single client engagement loop, but the goal is to make it repeatable across your client roster. That means:

Standardizing your audit template and content brief format so junior team members can execute them
Building a shared query bank by industry vertical so you're not starting from zero for every client
Creating tiered service packages (audit-only, audit + content production, full ongoing retainer) with clearly scoped deliverables

The agencies doing this well treat GEO as a distinct practice, separate billing codes, separate team training, separate reporting formats, rather than stapling it onto existing SEO retainers as a line item.

If you want to understand where GEO fits relative to traditional SEO, the GEO vs SEO breakdown is a useful reference for client conversations and for positioning your own services.

Start tracking your clients' AI visibility with the tools that make this workflow practical. Bingly monitors AI answer presence across ChatGPT, Perplexity, Claude, and Gemini, and surfaces the community signals that tell you what's coming next. Run your first audit free at bing.ly.