Your customer now asks an AI assistant. Are you in the answer?
GEO (Generative Engine Optimization) and AI search optimization
101 Digital works to increase the likelihood that your brand is cited as a source inside ChatGPT, Perplexity, Gemini and Google AI Overview answers: AI bot access policy, llms.txt, entity and schema structure, citable content design, and the measurement behind all of it.
What GEO is, and how it differs from SEO
GEO (Generative Engine Optimization) is the work of increasing the probability that generative answer engines — ChatGPT, Perplexity, Gemini, Google AI Overview — use your page as a source when they compose an answer.
In classic SEO the goal is a ranking: the user sees a list and clicks. In GEO the goal is not a position but presence inside the answer itself. An answer engine assembles its response from several sources, not one page, so there is no such thing as "rank 1 in AI" — there is only "being one of the cited sources".
- Goal: not a ranking, but being cited inside the generated answer.
- Answers are assembled from multiple sources; there is no fixed "first place".
- To be quotable, content must be structurally copyable: a direct-answer block, a definition, a table, an FAQ.
- Technical side: AI bot access policy (robots.txt), llms.txt, and entity/schema markup.
- GEO does not replace SEO. SEO, Local SEO and GEO are three separate buying intents.
Honest framing: No agency can guarantee that you will appear in AI answers. Google states plainly that structured data does not guarantee appearance in search results and does not guarantee ranking; generative engines pick their sources with their own models. Our job is not to promise placement — it is to raise the probability of citation and make the outcome measurable.
SEO, Local SEO and GEO are not the same thing
The three work on different surfaces, with different signals and different measurement. This table shows which one you actually need.
Table 1 — SEO vs Local SEO vs GEO / AI Search
| Criterion | SEO | Local SEO | GEO / AI Search |
|---|---|---|---|
| Goal | Climb the organic rankings | Be found on maps and in "near me" searches | Be cited inside the generated answer |
| Primary surface | Google and Yandex organic results | Google Business Profile, Google Maps, Yandex Maps | ChatGPT, Perplexity, Gemini, Google AI Overview |
| Primary signal | Content quality, links, technical SEO | Name-address-phone consistency, reviews, location | Entity clarity, citable structure, bot access |
| Measurement | Rankings, clicks, organic traffic | Search views, direction requests, calls | Mention rate, citation rate, AI referrals |
| Click expectation | High | High (calls and directions) | Low — the user usually stays in the answer |
| Time to first result | Typically 2-3 months | Typically 4-8 weeks | Typically 4-12 weeks, query dependent |
| Who needs it first | Any business that depends on organic traffic | Businesses with a physical address | Complex, long-consideration services and B2B |
Timeframes are based on 101 Digital's hands-on experience and vary with industry, competition and site history.
What we actually hand over
GEO is not an abstract promise. Everything below is delivered as files, code and reports.
llms.txt setup
A Markdown summary file describing your key pages and service definitions in a form AI clients can read plainly. On multilingual sites we ship one file per language and update it as content changes.
robots.txt AI bot policy
Explicit per-bot rules for GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and others. Which bots you allow is a business decision; we make it with you and document the reasoning.
Entity and schema graph
Organization @id, sameAs, Service, FAQPage and BreadcrumbList markup wired together as one connected graph. The aim: a single, non-contradictory machine-readable definition of your brand.
Citable content structure
Adding a direct-answer block, a definition sentence, a comparison table and an FAQ to priority pages. Answer engines attribute more readily to short, verifiable, copyable blocks.
Brand data consistency
Name, founding year, service definitions, price band and contact details identical across every language and page. Inconsistent data is the main source of wrong brand facts in AI answers.
AI visibility measurement
A fixed query set measured monthly across four engines: mention rate, citation rate, wrong-information rate and AI referrals, plus the priority list for the following month.
Table 2 — Deliverables, timing and cadence
| Deliverable | What is done | First delivery | Cadence |
|---|---|---|---|
| AI visibility baseline | 30-50 queries tested across four engines; mention / citation / wrong-info logged | 1-2 weeks | Monthly |
| robots.txt AI bot policy | Per-bot allow/block decision and implementation | 2-3 days | Quarterly review |
| llms.txt setup | Markdown summary file and link map per language | 3-5 days | When content changes |
| Entity / schema graph | Connected Organization, Service, FAQPage, BreadcrumbList markup | 1-2 weeks | When pages are added |
| Citable content structure | Direct-answer, definition, table and FAQ blocks added | 1-3 days per page | Ongoing |
| Brand data consistency | Cross-language audit and correction list | 3-5 days | Quarterly |
| AI visibility report | Metric table, trend commentary and next steps | End of month | Monthly |
Timings assume a single-language, mid-size site. On multilingual and large sites delivery time scales with the number of languages.
How AI visibility is actually measured
Measurement is the weakest point of GEO. That is exactly why we state the method and its limits up front.
Table 3 — AI visibility metrics
| Metric | What it measures | How it is collected | Healthy direction |
|---|---|---|---|
| Brand mention rate | In how many test queries your brand name appears | Fixed query set, repeated tests across four engines | Should rise |
| Citation rate | In how many answers your page is given as a source or link | Logging the answer sources for the same query set | Should rise |
| Wrong brand info rate | Share of answers with incorrect price, service or location | Manual review of answer texts | Should fall |
| AI referral traffic | Visits from chatgpt.com, perplexity.ai and similar sources | GA4 referral report and server access log | Should rise |
| AI bot crawl volume | Request counts from GPTBot, PerplexityBot and others | Server access log | Should be steady |
| GSC Generative AI report | Impressions, pages, countries and devices on AI surfaces | Google Search Console | For monitoring |
AI answers can differ from session to session for the exact same query. That is why we look at the monthly repetition and trend of one fixed query set rather than any single measurement.
Limits of the Search Console Generative AI report: Google Search Console's Generative AI report shipped on 3 June 2026. It gives impressions, pages, countries and devices on AI surfaces — but it does NOT give clicks, CTR or queries. In other words, "which question did we appear for" is not answerable from that report. This is precisely why we complement it with our own fixed query set.
The first 30 / 60 / 90 days
First we measure and open access, then we build the structure, then we expand.
Baseline and access
- Building a fixed 30-50 query test set for your industry and languages
- Baseline across four engines: mention, citation and wrong-info rates
- Deciding and implementing the robots.txt AI bot policy
- Publishing the first version of llms.txt
- Auditing existing schema and listing brand data inconsistencies
Structure and entity
- Building the Organization / Service / FAQPage / BreadcrumbList entity graph
- Adding direct-answer blocks, tables and FAQs to 10-15 priority pages
- Fixing brand data inconsistencies across all languages
- First comparative measurement and delta analysis against the baseline
Expansion and reporting
- Extending the query set to topics not yet covered
- Producing content for topics where you are missing from answers
- Tracking AI referral traffic via GA4 and server logs
- Establishing the monthly report rhythm and the next quarter plan
Who this is for — and who it is not for
Good fit
- B2B and service companies that need to answer several questions to explain what they do
- Brands whose SEO already works but who never appear in AI answers
- Businesses whose price, scope or location AI reports incorrectly
- Long-consideration sectors: healthcare, education, real estate, software, B2B manufacturing
- Multilingual sites carrying inconsistent facts between languages
Not a fit
- Anyone expecting a "guaranteed number one in AI" — no such guarantee exists
- New brands whose site is not indexed yet or has no content (basic SEO comes first)
- Anyone expecting measurable sales growth within a single month
- Businesses unwilling to publish service scope and pricing information
- Anyone who only wants local "near me" traffic — that is Local SEO work
GEO / AI search engagement pricing
Pricing follows the search visibility band in the 101 Digital 2026 service price list.
The exact figure depends on site size, number of languages and the scope of the query set. We issue a fixed quote after the first scan.
- Monthly fixed query-set measurement across four AI engines
- robots.txt AI bot policy and llms.txt maintenance
- Entity / schema graph setup and upkeep
- Converting priority pages into a citable structure
- Monthly AI visibility report with a priority list
- Brand data consistency audit across up to 5 languages
Frequently asked questions
The questions we get most often about GEO, AI search and measurement.
No. SEO aims to climb the organic rankings; GEO aims to be cited as a source inside a generated answer. They do not replace each other: a solid SEO foundation makes GEO easier, but it is not a substitute for it.
No — and we suggest treating any offer that guarantees it with caution. Google states plainly that structured data does not guarantee appearance in search results or a ranking. Generative engines select sources with their own models. What we commit to is doing the work that raises the probability of citation, and reporting the result in measurable terms.
Honest answer: Google has not stated that it uses llms.txt. Some AI clients read it, others do not. We implement it because it is cheap, harmless and clarifies your site structure — but on its own it does not decide the outcome. The real leverage is in content structure and entity clarity.
That is a business decision. Block access and your chance of being a cited source drops; allow it and your content may be used in answer generation. So we do not make a single blanket allow/block call in robots.txt: we decide per bot with you and write down the reasoning.
Structural changes typically show up in measurement within 4-12 weeks. Because AI answers vary even for the same query, we read the monthly trend of a fixed query set rather than any single reading.
An AI answer usually does not push the user to click through — the answer stays on screen. So the main gain is not traffic but your brand being mentioned in the right context and wrong information going down. We do measure AI referral traffic, but we do not sell it as the headline promise.
Yes. GEO does not collide with content or technical SEO; it operates at the schema, llms.txt, bot policy, brand consistency and content structure layer. We hand you an open list of deliverables and changes that you can share with your existing agency.
ChatGPT (including search mode), Perplexity, Google AI Overview and Gemini. The query set is built around your industry and target languages; for the Uzbekistan market we include Uzbek and Russian queries as well.
Do you show up in AI answers at all? Let us measure first.
We will run the first AI visibility scan on a query set built for your industry, and look at your mention, citation and wrong-information rates together.