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The GEO agency market appeared in about eighteen months, which means most vendors selling AI visibility today were selling something else last year. Some rebuilt their delivery around retrieval. Most renamed the SEO retainer. This is the due diligence checklist we would use if we were the ones buying.
- Ask for the measurement contract before the deliverables list — if they cannot define citation share, they cannot deliver it.
- The right first deliverable is a prompt set and a baseline, not a content calendar.
- Beware guaranteed rankings in ChatGPT — no vendor controls a non-deterministic system.
- Fixed-scope build then measured retainer beats an open-ended monthly content quota.
- Semrush shows geo agency at 260/mo, KD 27 — a thin but sharply commercial search market forming right now.
Every form is nonce-verified, every input sanitized, every output escaped and every admin action gated behind capability checks. A site-wide rollback log records who changed what — with a before/after diff and a one-click revert — so you can move fast without breaking canonicals, redirects or schema.
What a real GEO engagement actually includes
A credible AI visibility engagement covers five layers. If a proposal only covers content, you are buying a blog service with a new name.
| Deliverable | How to verify it happened | |
|---|---|---|
| Entity foundation | Canonical facts, schema graph, sameAs profile alignment | Validate JSON-LD; check facts match across site, docs and profiles |
| Answer surfaces | Answer-first pages for a defined prompt set | Each H2 opens with a standalone 40-90 word block |
| Technical retrieval | Crawlability for AI agents, clean canonicals, no schema conflicts | Crawl report plus validator output before and after |
| Corroboration | Third-party mentions carrying identical facts | A list of live URLs, not a plan to do outreach |
| Measurement | Prompt set, weekly runs, stored raw answers, scored report | Ask to see a sample report from another client, redacted |
If measurement is the last item on their proposal, it will be the first thing that never ships.
Twelve questions to ask on the first call
- How do you define and calculate citation share, precisely?
- How many prompts will be in our set, and who writes them?
- Which assistants do you measure, and how do you control for personalization?
- Do we own the prompt set, the raw responses and the tooling output when we leave?
- What does your baseline report look like — can I see a redacted one?
- What is the first change you would make to our site, and why that one?
- How do you handle it when an assistant states something false about us?
- What is your position on AI-generated content in our program?
- How do you keep pricing, limits and integration facts fresh after launch?
- Who does the work — named people, or a pool?
- What happens in month four if citation share has not moved?
- What do you refuse to do, even if a client asks?
A vendor with a real practice has a refusal list — no fabricated statistics, no fake reviews, no mass low-value pages, no irreversible site changes. A vendor with no refusal list has no methodology.
Red flags worth walking away from
We guarantee you will rank first in ChatGPT.
Assistant answers are non-deterministic and unranked. Any guarantee is either ignorance or a sales tactic.
We will publish 40 posts a month.
Volume is an input, not an outcome. Ask what citation share those posts are expected to move, and on which prompts.
Our proprietary tool is the deliverable.
The deliverable is measured visibility. Tooling that you cannot export or verify is lock-in, not value.
Results in two weeks.
Perplexity can move in two to four weeks. AI Overviews and Gemini take months. Anyone promising uniform speed has not measured it.
Pricing models and what they signal
| Typical shape | Best for | |
|---|---|---|
| Fixed-scope build | One-off foundation, entity work, first answer surfaces | Teams with in-house content capacity |
| Measured retainer | Monthly, with citation share targets in the contract | Most funded SaaS teams |
| Content quota retainer | N pages per month | Rarely the right fit |
| Performance-linked | Base plus bonus on measured citation share | Mature programs with clean baselines |
Our recommendation for most SaaS teams under Series B: buy a fixed-scope 90-day build with a measurement contract attached, then decide on a retainer once you have a real baseline and a real trend line. You will negotiate better with data, and a vendor confident in their method will accept that sequencing.
How to structure the first 90 days
- Days 1-14
Prompt set built from sales-call language; baseline run across all assistants; raw responses stored and shared with you.
- Days 15-30
Entity and technical foundation: canonical facts, schema graph, crawl access for AI agents, conflict removal.
- Days 31-60
Answer surfaces for the highest-intent prompts, plus internal linking into existing pillars.
- Days 61-80
Corroboration push — docs, review platforms, directories, partner and comparison pages carrying identical facts.
- Days 81-90
Second full measurement run, delta report against the frozen set, and a prioritized backlog for the next quarter.
Should we hire an agency or build in-house?
Build in-house if you already have a technical SEO and a content operator with capacity. Hire out the first 90 days if you need the measurement infrastructure and the entity foundation shipped fast, then bring the loop in-house.
What should the first 90 days cost?
Scope varies widely, but treat any proposal with no baseline measurement in the first two weeks as mispriced regardless of the number.
How do we exit cleanly?
Contract for ownership of the prompt set, the raw response archive, all published content and all schema. Confirm it in writing before signing.
Can one vendor cover both SEO and GEO?
Yes, and usually should — the technical foundation is shared. Just make sure GEO has its own deliverables and its own metrics rather than being a bullet on an SEO report.
What if our category is too new to have prompts?
Then your prompts are problem-shaped rather than category-shaped, and defining them is itself the most valuable early deliverable.
Researched sources & further reading
Plain-text excerpts from Wikipedia so you can verify the terms used above without leaving the page.
- Large language model— Wikipedia
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters and are trained with self-supervised learning on a vast amount of text.
Read on Wikipedia - Retrieval-augmented generation— Wikipedia
Retrieval-augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model so that the model responds to user queries with reference to a specified set of documents.
Read on Wikipedia - Google Search— Wikipedia
Google Search is a search engine operated by Google. It allows users to search for information on the Web by entering keywords or phrases. Google Search uses algorithms to analyze and rank websites based on their relevance to the search query.
Read on Wikipedia
Real-world examples
Three shapes this problem takes in the wild — and what the fix looked like when a team applied the Agentic SEO playbook end-to-end.
How it actually works — step by step
- 11. Detect
Run a full crawl and let the agent flag every agentic seo issue on the site — canonicals, schema, orphans, entity gaps.
- 22. Explain
Each finding gets a plain-English explanation with the exact rule it violates and the URLs affected.
- 33. Fix
The agent drafts the fix — meta rewrite, JSON-LD patch, internal link, redirect — as a diff you can read before applying.
- 44. Approve
You approve individual fixes or an entire batch. Nothing writes to the site until a human clicks approve.
- 55. Apply
Approved fixes are pushed live and mirrored to a changelog with the timestamp, actor and rule.
- 66. Track & rollback
Every change is monitored for regressions. One click rolls back any batch, cleanly, with schema intact.
The workflow at a glance
Final thoughts
The teams that pull ahead in 2026 are the ones that made agentic seo boring — repeatable, auditable, reversible. That's exactly what the WBP Omni-Agent is built to run.
Related tools built by the same team
Built by the same team as the guides on this site. Included here for context and provenance — not a paid placement.
WpBulkPublishingWordPress pluginUnified SEO, GEO, AEO, AIO and LLM ranking suite — the parent product of this site.
Content Quality & Spam Checker by WBPCustom GPTReviews content and templates for quality, duplication and spam risks.
Disclosure: WpBulkPublishing and the tools listed above are made by the same team as this site. Links open in a new tab.
External resources & further reading
Authoritative background from Wikipedia, community discussion, official docs and research bodies. Opens in a new tab.
Run this checklist against us
We will hand you the baseline, the prompt set and the measurement method before you commit to anything.
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About the author
Founder · WpBulkPublishing
Usman Jatoi — a 20-year-old creative artist, and tech innovator who began his digital journey at just 7 years old and started working professionally at 12. Founder of WP Bulk Publishing and creator of WpBulkPublishing.
4+ years shipping production WordPress builds for UK and US remote agencies — 20+ live sites redesigned or built from scratch in Elementor, ACF, and custom themes. The schema, silo, and AI-search patterns you read about here are the same ones running on client work every day.
- WordPress · Elementor
- Programmatic SEO
- Schema & JSON-LD
- AI Search (GEO)
- Silo architecture
- Bot-tracking
