DEV Community: Dan Cristian

Can an AI agent find you? We measured the Agent Discovery Optimization (ADO) Score of 130 Romanian domains

Dan Cristian — Tue, 08 Sep 2026 18:09:52 +0000

Can an AI agent find you?

130 Romanian domains · 12 machine-verifiable signals · ADO Score 0–100 after the Exista.io framework · 0 Agent Cards · mean score 17 · 8 September 2026

Until now, this series has measured one thing: when a person asks ChatGPT or Gemini "where do I get a good laptop", which brands come back in the answer. The person reads, the person chooses. But a second kind of customer is arriving. An AI agent receives an objective ("find a supplier of corporate jewelry for 200 gifts, under a given budget, with an invoice") and solves it on its own. It does not search Google and does not read marketing copy. It requests configuration files at standardised paths, checks structured data, cross-references sources and, if it cannot find what it needs in a format it can process, moves on to the next candidate. The first customer is persuaded; the second is verified.

This is the fifth episode in the series measuring how Romanian markets appear in front of artificial intelligence. The first three measured the human layer: jewelry, books, electronics. The fourth measured a technical artefact, llms.txt. This one measures the next layer, the agents', using the framework Exista.io published in February 2026 under the name AI Visibility Stack: whether an autonomous agent can find, evaluate and select a Romanian company without a human stepping in. The paper proposes a metric for this, the ADO Score, and makes a prediction: most companies will score near zero, not because they are weak, but because the necessary artefacts are not yet part of standard practice. We tested the prediction on Romania.

Two changes of method from the previous episodes, stated up front. We did not query models; we probed websites: every figure below comes from an HTTP request or a Wikidata query, reproducible with the published script. And we did not build a new sample: we took exactly the brands the AI engines recommended to people in episodes 1–3, plus the marketing agencies from episode 4. That is, the companies that have already won layer 1 and the companies that sell AI visibility. If anyone is ready for agents, it should be them.

In short — what we found

Zero Agent Cards. None of the 126 domains with an HTTP response serves /.well-known/agent-card.json, the file the paper calls "the functional equivalent of a website for agents". Nor on the legacy path, /.well-known/agent.json. The dimension with the largest weight in the ADO Score, 30 points, is empty across the board.
The mean ADO Score is 17 out of 100; the maximum is 35. Nobody leaves the paper's "minimal visibility" band (0–20) by more than a little: 55 domains sit between 21 and 40, 61 between 1 and 20, 9 at exactly zero. Above 40 there is no one.
All the score comes from old SEO. Trust signals (JSON-LD, Wikidata) and sitemap freshness bring, on average, 12 of the 17 points. Interoperability brings 0.3 of 15. Whoever does well does well for reasons unrelated to agents.
The interoperability that exists comes from the platform, not from strategy. 8 domains serve MCP authorisation discovery (/.well-known/oauth-protected-resource): 3 are Shopify stores (the protocol ships with the platform), 4 run a WordPress plugin (/wp-json/mcp/…) and exactly one built its own server. A single domain out of 130, snsys.ro, an IT firm, wrote a manifest for agents by hand.
The agencies selling AI visibility do slightly better than their clients (17.9 vs 15.9 points), exclusively through llms.txt (66% vs 29%) and JSON-LD (80% vs 59%). On Agent Cards, zero as well.
Layer 1 transfers weakly to layer 2. Across the 41 brands, the Spearman correlation between visibility in AI answers (ep. 1–3) and the ADO Score is 0.31. The top 10 brands by AI visibility have a mean ADO of 17.2; the rest, 15.4. eMAG, the most visible brand in the series (94% of answers), scores 22.
Wikidata is the signal that separates brands from agencies: 12 of 41 brands have an entity with an official website declared; 3 of 85 agencies. But of the 4 domains that have both Wikidata and JSON-LD, only 3 carry the same name in both.
The closed door: 6 domains answer a JavaScript-free request with 403, 503, 406 or an anti-bot challenge, among them 4 of the 13 electronics retailers the engines recommend (Flanco, PC Garage, Vexio, Quickmobile) and Amazon.de, which additionally blocks all six answer crawlers. An agent-crawler gets exactly what the probe got.
A real false positive: libris.ro answers 200 with a generic JSON ("Forbidden!") on any .well-known path. A naive agent would read it as an Agent Card, mcp.json and ai-plugin.json at once. The mandatory-field filter removed it; the lesson stays.

Raw data, free. The three CSV files — scores and every signal per domain, adoption rate per signal and group, statistics per dimension — plus the scoring specification and the probe script are published under CC BY 4.0. Download the data

01 · How did we measure whether an AI agent can find a Romanian company?

Methodology

Parameter	Value
Theoretical framework	AI Visibility Stack / Agent Discovery Optimization — Marco, G. (2026), Beyond AEO: The AI Visibility Stack and the Era of Agent Discovery Optimization, Exista.io Working Paper, doi:10.5281/zenodo.18728629
Metric	ADO Score 0–100, five dimensions with the paper's weights: Agent Card 30 · trust signals 25 · knowledge completeness 20 · interoperability 15 · freshness 10
Operationalisation	Websem — one machine-verifiable criterion per point, published in `scor-ado.md`
Sampling frame	130 domains: 43 brands named by AI engines in episodes 1–3 (jewelry, books, electronics and IT) + 87 marketing agencies and sites cited by the engines in episode 4
Probing	8 September 2026, a single pass, from Romania, ~12 HTTP requests per domain, no JavaScript
Signals checked	`/.well-known/agent-card.json` and `/.well-known/agent.json` (A2A) · `/.well-known/oauth-protected-resource` and `/.well-known/mcp.json` (MCP) · `/.well-known/ai-plugin.json` · `robots.txt` · `llms.txt` and `llms-full.txt` · JSON-LD on the homepage · visible text without JavaScript · `sitemap.xml` (`lastmod`) · Wikidata (P856 = official website)
Instrument	`probe_ado.py`, standard Python 3, published with the data; declared User-Agent `WebsemADOProbe/1.0`
Domains with an HTTP response	126 of 130 (4 without: 2 timeouts, 1 DNS, 1 invalid certificate)
Domains in the ranking	125 (without websem.ro, the author's domain — reported separately, section 07)

Four design choices deserve an explanation up front, because they decide what the numbers mean.

We measure what an agent can verify, not what a person can read. The probe does not execute JavaScript, does not interpret marketing copy and does not judge design. It requests files at standardised paths, parses raw JSON and HTML, and queries a public knowledge graph. This is exactly how the Exista.io paper describes a procurement agent in its evaluation phase: "persuasive prose, testimonials, and visual design elements are irrelevant to this process".

The weights are the paper's; the criteria are ours. The paper defines the dimensions and weights of the ADO Score but does not publish scoring criteria. We wrote them, transparently, one binary signal per point, and publish them with the data. This is not the Exista.io instrument and claims no equivalence with it; it is a reproducible measurement of the same dimensions. Anyone can run the probe and get the same figures, on the same day.

A 200 response does not mean "served". Many servers answer 200 with an HTML page on any path, and some answer 200 with a generic JSON error. A file counts only if it parses as JSON and contains the protocol's mandatory fields: for an Agent Card, name and url or skills; for MCP discovery, resource or authorization_servers. Without this filter, one domain in the study would have had an Agent Card, mcp.json and ai-plugin.json simultaneously, all three being the same "Forbidden!" message.

The sampling frame is not the market. The 43 brands are exactly the companies the AI engines recommended to people in previous episodes, the winners of layer 1; the 87 agencies are those the engines cite on marketing and AI topics, i.e. those who sell AI visibility. It is not a representative sample of the Romanian economy. It is, however, the most favourable possible sample for the paper's hypothesis that LLM visibility is inherited by agent discoverability: if it does not hold here, it is unlikely to hold elsewhere.

The main limitation, declared from the start: a single pass, on a single day, with binary signals. A site that publishes its Agent Card the next day does not appear here. The score measures the existence of public artefacts, not the quality of the company.

02 · What do ADO, Agent Card, A2A, MCP and llms.txt mean?

Terminology, once

AI Visibility Stack is the three-layer framework proposed by Exista.io. Layer 1, LLM visibility, is what we measured in episodes 1–3: whether a model names you when a person asks. Layer 2, agent discoverability, is the subject of this study: whether an autonomous agent can find, evaluate and select you without a human in the loop. Layer 3, participation, is whether you yourself have a published agent other agents can transact with; it cannot be measured from the outside without initiating transactions, so it stays out of the study.

ADO, Agent Discovery Optimization, is the discipline of layer 2, and the ADO Score its composite metric, 0–100. The paper's interpretation scale: 0–20 minimal visibility, 21–40 emerging, 41–60 moderate, 61–80 strong, 81–100 dominant.

A2A, Agent2Agent, is the open protocol launched by Google in April 2025 and donated to the Linux Foundation, through which agents talk to each other. Its discovery artefact is the Agent Card: a JSON file at /.well-known/agent-card.json (the current path; the old one was /.well-known/agent.json) declaring the entity's name, description, endpoint, protocol version, capabilities, skills and authentication schemes. The paper calls it "the functional equivalent of a website for agents".

MCP, Model Context Protocol, introduced by Anthropic in 2024, is the protocol through which an agent connects to tools and data. Discovering a protected MCP server goes through /.well-known/oauth-protected-resource (RFC 9728), a JSON declaring the resource and its authorisation servers; /.well-known/mcp.json is an unofficial convention with no standard.

llms.txt is the Markdown file at the site root that gives models a clean map of the content; we measured it in episode 4 on 87 marketing domains, and here we re-read it as an agentic accessibility signal.

JSON-LD / schema.org Organization is the structured markup on the homepage that declares by machine who the entity is: name, URL, sameAs (official profiles), address, telephone, legal identifier. Wikidata is the public knowledge graph behind Wikipedia; an entity whose property P856 (official website) equals the domain is the simplest cross-verifiable identity signal an agent can ask for.

Answer crawlers (OAI-SearchBot, ChatGPT-User, PerplexityBot, Perplexity-User, Claude-User, Claude-SearchBot) bring content into real-time answers; training crawlers (GPTBot, ClaudeBot, Google-Extended, CCBot etc.) collect for future models. The taxonomy is the one from episode 4.

03 · Which Romanian companies are the most discoverable for an AI agent?

The ranking: who can be found

#	Domain	Group	ADO	Trust	Knowledge	Interop.	Freshness
1	IONA (iona.ro)	brand · jewelry	35.0	13	10.0	4	8
2	TEILOR (teilor.ro)	brand · jewelry	32.4	21	11.4	0	0
3	Litera (litera.ro)	brand · books	32.4	18	9.4	0	5
4	dafe.ro	agency	31.0	13	10.0	0	8
5	difrnt.ro	agency	31.0	13	10.0	0	8
6	privatebrands.ro	agency	30.8	15	7.8	0	8
7	digitaliomarketing.ro	agency	28.0	13	10.0	0	5
8	goai.ro	agency	28.0	13	10.0	0	5
9	seocherry.ro	agency	28.0	13	10.0	0	5
10	creativdigital.ro	agency	27.9	11	8.9	0	8

The rest of the ranking, with all 41 signal columns, is in the main CSV.

Three things are visible in the table before any interpretation. First: the Agent Card column is zero from top to bottom, across all 125 domains, and the interoperability column is non-zero for exactly 9. Second: the scores are compressed into a narrow band. Between rank 1 and rank 20 there are 9 points; between rank 20 and rank 100, another 15. On the paper's scale, 6 domains enter "emerging visibility" with more than 30 points and none reaches "moderate". Third: the top three are brands, not agencies, but for different reasons. IONA is first because it collects every small signal available to a well-maintained online store: JSON-LD with a legal identifier, llms.txt, a fresh sitemap and, alone in the top 3, MCP discovery, courtesy of a WordPress plugin. TEILOR and Litera are there because they have what almost nobody has: a Wikidata entity with a declared official website and an identical name between Wikidata and JSON-LD, the consistency signal the paper places at the base of machine trust.

The mean score over the 125 domains is 17.2, the median 19.7. The nine domains at zero (Amazon.de, Cel.ro, KULTHO, Librăria Română, Vexio and four agencies) carry no signal the probe could credit: no organisation JSON-LD, no Wikidata, no dated sitemap, no files for agents. It does not mean the companies do not exist or do not work; it means that, for a visitor who is not human, there is no machine-readable evidence of who they are.

04 · Where are the points lost: which ADO Score dimensions are empty?

The anatomy of the score

Dimension	Weight	Mean (125 domains)	Domains with points
Agent Card	30	0.00	0
Trust signals	25	8.19	102
Knowledge completeness	20	4.99	101
Interoperability	15	0.27	9
Freshness	10	3.76	89
ADO Score	100	17.22	116

Read by dimension, the figure of 17 tells a simple story: 45 of the 100 possible points, the two dimensions that explicitly concern agents, Agent Card and interoperability, produce on average 0.27. Every other point comes from three things a site has had for ten years for SEO reasons: organisation markup, an entity in a knowledge graph and a dated sitemap. The Exista.io paper builds the ADO Score so that layer 1 "feeds" layer 2; in our data, layer 1 is all there is.

Trust signals are the most populated dimension, with 102 domains above zero, but the mean of 8.2 out of 25 shows how thin the layer is. 92 of 126 domains have Organization-type JSON-LD on the homepage (73%), but with on average 5.4 of the 9 attributes checked: name and URL almost always, logo and sameAs often, address and telephone less so, a legal identifier on just 15 domains. Consistency between the declared URL and the actual domain is good (83 of 91). Name consistency with Wikidata can only be checked on 4 domains, because only 4 have both sources, and passes on 3.

Knowledge completeness has a structural ceiling: half the points come from Wikidata, and only 15 domains out of 126 have a Wikidata entity whose official website is the domain. The other 111 can take at most 10 points of 20, however good their JSON-LD. The 15 entities have between 3 and 9 of the 10 attributes checked; eMAG has all 9, Humanitas, Cărturești and TEILOR 7 each.

Freshness is the only dimension where the agencies clearly beat the brands (4.2 vs 2.8 out of 10): 65 domains have a lastmod within the last 30 days in their sitemap, another 20 within 180, and 30 earn extra points for an llms.txt with a Last-Modified from the last 90 days. It is a cheap dimension and explains part of the agencies' advantage in section 07.

05 · Which agent signals actually exist on Romanian websites?

Adoption, signal by signal

Signal	All (n=126)	Brands (n=41)	Agencies (n=85)
A2A Agent Card (`/.well-known/agent-card.json`)	0 (0%)	0	0
Agent Card, legacy path (`/.well-known/agent.json`)	0 (0%)	0	0
MCP discovery (`oauth-protected-resource`)	8 (6.3%)	3 (7.3%)	5 (5.9%)
`mcp.json`	1 (0.8%)	0	1
`ai-plugin.json` (legacy OpenAI)	2 (1.6%)	0	2
Wikidata entity with official website = domain	15 (11.9%)	12 (29.3%)	3 (3.5%)
Romanian Wikipedia article	10 (7.9%)	9 (22.0%)	1 (1.2%)
JSON-LD Organization on the homepage	92 (73.0%)	24 (58.5%)	68 (80.0%)
`llms.txt` served	68 (54.0%)	12 (29.3%)	56 (65.9%)
`llms.txt` spec-compliant (H1 + summary)	43 (34.1%)	8 (19.5%)	35 (41.2%)
Sitemap with `lastmod` ≤ 30 days	65 (51.6%)	20 (48.8%)	45 (52.9%)
≥ 200 words without JavaScript	111 (88.1%)	34 (82.9%)	77 (90.6%)
No answer crawler blocked	117 (92.9%)	38 (92.7%)	79 (92.9%)
`robots.txt` accessible	119 (94.4%)	39 (95.1%)	80 (94.1%)
HTTPS	126 (100%)	41	85

The table has a clear shape: the older and more "SEO" a signal is, the more adopted it is; the newer and more "agent" it is, the more absent. HTTPS, robots.txt and server-side content are above 88%. JSON-LD is at 73%. llms.txt, born in 2024, is at 54%, but with a brutal asymmetry between those who sell it (66%) and those it is sold to (29%). Wikidata, which is not new but requires curation work off-site, is at 12%. And the two files defined by the agent protocols, from 2025, are at 6% and 0%.

It is worth underlining that blocking answer crawlers is almost non-existent: 2 domains out of 126, Amazon.de (all six) and one agency (only ChatGPT-User). Blocking training crawlers is somewhat more frequent, 13 domains, with KULTHO and canopy.ro blocking eight each, but it does not affect the answer layer and is not penalised in the score. The Romanian market is not hiding from AI; it simply has not prepared anything for it.

Content without JavaScript deserves a note as well: 6 domains have under 200 words in their raw HTML, four of them under 25, i.e. homepages that exist only after a script runs. Three domains explicitly display the "enable JavaScript" warning. For a person with a browser they are normal sites; for an agent-crawler they are empty pages.

06 · Where does the interoperability that exists come from?

The platform, not the strategy

The 8 domains serving /.well-known/oauth-protected-resource, the file through which an MCP client learns where to authorise, are the only place in the study where agent protocols appear in production. We read the files, not just the response code, and their origin is unambiguous.

Domain	Group	What the file declares	Origin
moogu.ro	brand · jewelry	`authorization_servers: shopify.com/authentication/…`	Shopify
istyle.ro	brand · electronics	`authorization_servers: cont.istyle.ro, shopify.com/authentication/…`	Shopify
gregoire.ro	agency	`authorization_servers: account.gregoire.ro, shopify.com/authentication/…`	Shopify
iona.ro	brand · jewelry	`resource: …/wp-json/mcp/mcp-oauth-server`	WordPress plugin
limitless.ro	agency	`resource: …/wp-json/mcp/mcp-oauth-server`	WordPress plugin
arobsgrup.ro	agency	`resource: …/wp-json/mcp/mcp-oauth-server`	WordPress plugin
growwwise.com	agency	`resource: …/wp-json/mcp/mcp-oauth-server`	WordPress plugin
seo365.ro	agency	`resource: seo365.ro/mcp`, authorisation on Supabase	own server

Three stores run on Shopify, and Shopify has shipped MCP discovery to every store as platform infrastructure; the merchants did nothing and, most likely, do not know the file exists. Four sites run a WordPress plugin exposing an MCP server at /wp-json/mcp/; that is an installation decision, not an architecture one. A single domain out of 130, seo365.ro, built its own endpoint with its own authorisation.

It is the same pattern we found in episode 4 with llms.txt, where 21% of the files were plugin-generated and none of those followed the specification. The agentic interoperability of the Romanian market, such as it is, is a side effect of the platforms the sites run on. That has a practical consequence: if an agent started tomorrow to prefer suppliers with MCP discovery, the advantage would go not to the companies with a strategy, but to those that happened to be on Shopify.

Two cases fall outside the pattern and deserve naming. snsys.ro, an IT services firm in Bucharest, is the only domain with a hand-written mcp.json: name, version, description, vendor, contact with sameAs to Wikidata and LinkedIn, capabilities by topic and geography, resources (llms.txt, llms-full.txt, a company.json), certifications with issuer and registry code. It is not a standard, but it is exactly the kind of document the paper describes under "semantic clarity": precise terminology, explicit client segment, verifiable certifications. The paradox: the same server answers 406 on the homepage to a non-browser client, so the probe could read its manifest but not its site. The second case, difrnt.ro and again snsys.ro, serve ai-plugin.json, the ChatGPT plugin format retired by OpenAI in 2024. It is an artefact of the previous wave, published in good faith for a protocol that no longer exists. It does not score, but it says something about how fast "what you need to have" changes.

07 · Are the agencies selling AI visibility more discoverable than their clients?

Brands vs agencies

	Brands (ep. 1–3)	Agencies (ep. 4)
Domains in the ranking	41	84
Mean ADO Score	15.9	17.9
Median ADO Score	16.3	19.9
Maximum	35.0 (IONA)	31.0 (dafe.ro, difrnt.ro)
At zero	5	4
Above 20 points	15 (37%)	40 (48%)
Agentic accessibility index (mean)	72	83

The agencies hold a small advantage, fully explained by two cheap signals: llms.txt (66% vs 29%) and organisation JSON-LD (80% vs 59%), both delivered by the WordPress themes and plugins they use. On the expensive signals, those requiring a presence off-site, the brands lead clearly: 12 brands have Wikidata, 3 agencies do; 9 brands have a Romanian Wikipedia article, a single agency does. And on the dimensions that concern agents, both groups are at zero, save for the plugins of section 06.

In other words, those who sell AI visibility have done, on their own sites, the easy part of the homework and nothing of the hard part. It is not an accusation; it is a measure of how new the subject is. But for a buyer of AEO services, the question "what is your agency's ADO Score?" becomes, from September 2026, one that can be answered with a number.

By vertical, among the brands, luxury jewelry has the highest mean (19.0 over 10 brands), followed by the book market (15.8 over 21) and electronics (12.8 over the 10 with a response). The gap in electronics is produced largely by section 09: four of the retailers could not be probed at all.

Websem.ro, the author's domain, was in the sampling frame because the engines cited it in episode 4. We probed it with the same script and report it separately, with no place in the ranking: ADO Score 28.0 (trust 13, knowledge 10, interoperability 0, freshness 5, Agent Card 0), accessibility index 100. It would have ranked 7th. No Wikidata, no Agent Card, no MCP: the same gaps as the rest of the market. The study is not written from a position of advantage.

08 · Are the brands AI recommends to people also the ones an agent can find?

Layer 1 and layer 2

The Exista.io paper states that the layers are cumulative: "a company with strong Layer 1 visibility inherits credibility signals that support Layer 2 discoverability", and leaves the empirical check to "future research". We had, from episodes 1–3, exactly the layer-1 visibility of the 41 probed brands: the share of AI answers that named them. We set it against the ADO Score.

The Spearman correlation is 0.31 (n = 41). Positive, weak, far from "inheritance". The top ten brands by AI visibility have a mean ADO of 17.2; the other 31, 15.4. eMAG, named in 94% of the electronics answers and holding the most complete Wikidata entity in the study (9 of 10 attributes), scores 22, because it has no organisation JSON-LD on its homepage and no file for agents. TEILOR, at 60% in jewelry, scores 32.4. Flanco, PC Garage and Vexio, between 50 and 55% AI visibility, score 16, 18 and 0, because they refuse the connection of a client that is not a browser.

The figure must be read carefully in both directions. It does not say the paper is wrong: the mechanism it describes, LLM citation as a credibility signal for agents, may exist without showing in the public artefacts we measure. It does say that, in September 2026, in Romania, being recommended by ChatGPT to people does not come bundled with being findable by an agent. They are two separate subjects, with two separate to-do lists, and nobody has started the second one.

09 · What happens when an agent knocks and nobody opens?

Inaccessible sites and false positives

The probe requested each homepage first with its own declared User-Agent, then, on refusal, with a browser one. Six domains refused both: Flanco, PC Garage and Vexio with 403, Quickmobile with 503, snsys.ro with 406, and Amazon.de with 202 and an anti-bot challenge page. Four more did not answer at all (Altex and Media Galaxy with timeouts, start-seo.ro with no DNS, daredigital.ro with an invalid certificate). In total, ten of 130 domains cannot be read by an ordinary HTTP client, and seven of them are electronics retailers, precisely the market episode 3 found the most concentrated in AI answers: of 13 stores recommended by the engines, only 6 could be read in full.

The consequence for the score is mechanical: a blocked site cannot receive points for JSON-LD, content or freshness, however much of them it has behind the firewall. Flanco and PC Garage stay in the ranking only through Wikidata. The consequence for agents is worse than the score: the paper describes the specified behaviour of an agent that receives no answer, "to proceed to the next candidate". Anti-bot protections are legitimate and have good reasons; but they do not tell a scraper from a buyer with a budget, and in the absence of an Agent Card saying "come in this way", the agent leaves.

The opposite case is just as instructive. libris.ro answers 200 with valid JSON on any .well-known path: {"servlet":"default","message":"Forbidden!","url":"/.well-known/agent-card.json","status":"200"}. Code 200, parsable JSON, a message of denial. An agent that checks only for the file's existence would conclude that Libris has an Agent Card, mcp.json and ai-plugin.json. The probe required the protocol's mandatory fields and rejected all three. It is not a mistake by Libris, but a property of the real web: as agents start looking for files at standard paths, servers configured to answer "something" to any request will produce false identities. Verifying the schema, not the response code, is the first rule of layer 2.

10 · What are the limitations of this study?

Limitations

We list them in full, because a study that does not declare its limitations is not a study.

A single pass, on a single day. Everything above describes 8 September 2026. A site publishing an Agent Card on 9 September does not appear. There is no volatility section, because there is no second measurement.
The operationalisation of the score is ours. The Exista.io paper gives the dimensions and weights, not the criteria. Another set of criteria, with the same weights, would produce other absolute values; the order and, above all, the zeros would remain. The criteria are published in full in scor-ado.md, precisely so they can be contested point by point.
Binary signals, no quality. A JSON-LD with name and URL and one with twelve verified attributes both take the 6 base points; the difference shows only in completeness. A 200-byte llms.txt and a 60 KB one are "served" alike.
What we could not probe: Google Knowledge Graph (keyed API, non-deterministic results), certifications and accreditations (no machine-queryable public registry in Romania), address or telephone consistency across sources (no second machine source outside Wikidata). All three are in the paper; none is in the score.
Wikidata depends on search. An entity is credited only if it has the "official website" property equal to the domain and if it was found through the Wikidata search API. An entity without a declared official website cannot be machine-linked to the domain and is not credited; that is a design choice, not an oversight, but it may underestimate large brands. The Litera entity was found in the first pass and kept after verification; the method is noted in the raw data.
The sampling frame is not representative. 43 brands from three verticals and 87 marketing sites. The figures are about them, not about "Romanian companies" in general. One of the brands, Amazon.de, is not Romanian; it was kept because the engines named it in ep. 3 and is reported as such.
Ten domains without a read. Four did not answer, six blocked the request. They are in the CSV with their status and with what could be measured (Wikidata, robots.txt where it answered), but their scores are, by construction, incomplete.
Layer-1 visibility comes from different episodes, with different denominators (10, 180 and 87 answers), and is compared here as a plain percentage. For eMAG, present in two episodes, the higher value was used. The correlation in section 08 is indicative.
The probe's declared User-Agent may be treated differently from a real agent. We retried with a browser User-Agent on every refusal and recorded the result; no domain answered differently.
Declared conflict of interest: websem.ro is in the frame and is reported, but excluded from the ranking and the means. The author sells services on exactly the subject measured.

11 · What does this mean for a company in Romania?

Implications

Three implications that follow directly from the data, without exceeding it.

Layer 2 is empty, therefore it is cheap. In a market where nobody has an Agent Card, the first to publish a correct one competes with no one. The file is a JSON of a few dozen lines at a standard path; the A2A specification is public; the cost is measured in hours, not months. The same goes for MCP discovery, which three platforms already deliver for free. The difference between 17 and 50 points, i.e. between "minimal" and "moderate" on the paper's scale, is a list of four files and a Wikidata entry.

Machine identity is the real problem, not the files. Of 126 domains, 15 can be machine-linked to an entity in a knowledge graph, and 3 carry a consistent name between the graph and their own site. An agent cross-checking "who is this company" has, for 111 domains, no second source. Wikidata is public, free and editable by anyone, with notability rules that leave room for most of the companies in this study. It is the biggest lever in the signal table and the least used.

Anti-bot protection needs to learn to tell agents from scrapers. Six domains, four of them large electronics retailers, are invisible to any client that is not a browser. Until the protocols have identification mechanisms accepted by firewalls, the only way to remain accessible to an agent is to declare explicitly where it may enter: an Agent Card, llms.txt, a robots.txt with rules for answer crawlers. Otherwise the "let it in or not" decision is made by a WAF configured in 2019.

12 · Frequently asked questions

What is the ADO Score?
A 0–100 metric proposed by Exista.io (Marco, 2026) for a company's discoverability by autonomous AI agents, from five dimensions: Agent Card (30), trust signals (25), knowledge completeness (20), interoperability (15), freshness (10). This study keeps the dimensions and weights and adds published, machine-verifiable scoring criteria.

Is this the official Exista.io score?
No. It is Websem's operationalisation of their framework, with its own transparent criteria. The paper does not publish scoring criteria, so any ADO Score computed by anyone else is, likewise, an operationalisation.

Why does no domain have an Agent Card?
Because the A2A protocol is a year and a half old, and the file is not generated by any popular website platform, unlike llms.txt or MCP discovery. The Exista.io paper predicted exactly this outcome for February 2026; our data confirms it for September, in Romania.

What is "MCP discovery" and why does it matter that it comes from Shopify?
/.well-known/oauth-protected-resource is the file through which an MCP client learns where to authorise in order to talk to a site's server. Shopify publishes it for every store; the merchant decided nothing. It matters because it shows that agentic interoperability, such as it exists, is an effect of the platform, not of strategy.

Why is websem.ro in the study?
Because the AI engines cited it in episode 4, and this study's sampling frame is everything the engines cited. We probed it with the same script and report it with its score (28.0), but it enters neither the ranking nor the means.

How were the 130 domains chosen?
We did not choose them. They are the brands named by the AI engines in episodes 1–3 and the domains cited as sources in episode 4, deduplicated. A single domain appears in two episodes (emag.ro).

Why did Altex and Media Galaxy not answer?
Their servers sent no response within 20 seconds, twice, at different hours. They remain in the data with the Wikidata entity found and without the other signals. It is a limitation of the probe, not a statement about their sites.

Does a low score mean the company is weak?
No. It means the public artefacts an agent could verify do not exist. The Exista.io paper says the same thing explicitly. The largest companies in the study, eMAG or Amazon, are not at the top.

Why does Wikidata weigh so much?
Because it is the only public, free, machine-queryable source through which an agent can cross-verify a company's identity against its own website. The paper names it explicitly as a trust signal. Google Knowledge Graph would be the second, but cannot be probed without a key.

How can any figure in the study be verified?
With probe_ado.py and analiza_ado.py, published in the repository. The first runs the probe on domenii.csv, the second computes the scores from the raw JSON and checks programmatically that the sum of the dimensions equals the score, on every row. The run takes under an hour.

What should a company do to pass 40 points?
The data shows where the unclaimed points are: a complete A2A Agent Card (up to 30), a Wikidata entity with an official website and basic attributes (8 + up to 10), an organisation JSON-LD with address, contact, legal identifier and sameAs to Wikidata (up to 15 cumulated), a dated sitemap. The study does not measure the effect of these interventions; it measures the state of affairs.

Is the data reusable?
Yes. The three CSVs, the scripts and the scoring specification are published under CC BY 4.0, with attribution.

The data, deposited publicly

Responsibility note

This study measures exclusively the existence of public, machine-verifiable artefacts on 130 domains, on a single day. It does not measure the quality of the companies, their products, services or sales. "X is the most discoverable" does not mean "X is the best", and a score of zero means the signals are missing, not that the company is weak. The domains were named or cited by AI engines in previous episodes, not chosen by the authors. The ADO Score is Websem's operationalisation of a framework published by Exista.io and does not represent Exista.io's instrument or assessment. The figures can be re-verified from the raw data and with the published scripts, and any reported error will be corrected publicly, with versioning.

Original Websem study · Dan Cristian Alexandrescu · September 2026

Raw data: CC BY 4.0 — DOI 10.5281/zenodo.22664431

Who sells the laptop when the customer asks an AI? Romania's electronics market across 5 AI engines

Dan Cristian — Thu, 06 Aug 2026 09:56:07 +0000

Who sells the laptop when the customer asks an AI?

18 brand-free purchase questions · 5 AI engines · 87 analysed answers · 13 tracked retailers · 378 mentions · 249 domains cited as sources

A customer asking "where do I get a good laptop for university" no longer receives ten blue links to choose from. They receive one answer, naming three to six shops, in order. That order is neither neutral nor random — and Romanian retailers are not yet measuring it.

This is the third episode in a series measuring how Romanian markets appear inside AI answers. The first covered luxury jewelry, the second the book market. Electronics and IT is the most concentrated of the three and, for that reason, the most instructive: here the question is no longer "do you appear", because almost everyone appears. The question is how far down the list you appear.

One methodological change from the previous episodes, stated up front: this study rests on a single complete run, not two. It is an accurate photograph of 3 August 2026, not a trend measurement. The weekly volatility section from episodes 1 and 2 is absent here, and the conclusions are phrased accordingly.

In short — what we found

The market is concentrated, not fragmented. 86 of the 87 answers name at least one of the 13 tracked retailers. Exactly one answer names none. In the book market, 58% of answers named no major brand — the mirror image.
Position, not presence, separates the players. eMAG opens the list in 66 of its 82 mentions (80.5%). Altex has only 4 first places but 67 second-or-third places. The rest of the market shares positions 4-7.
The ranking is inherited, not computed. A single listicle — shopilo.ro — is cited in 21 answers, more than the websites of PC Garage or Media Galaxy. One Wikipedia list page is cited in 7.
Specialists own their territory and get paid for it. F64 appears in all 5 answers about professional cameras and almost nowhere else: 9 mentions produce 40% of the weighted visibility of a retailer with 47 mentions.
Google declined to show an AI Overview on 3 of the 18 questions — gaming laptop, large TV and cheapest phones, three of the most commercial queries in the set.
Being named and being cited are different things. 21 of the 47 answers naming evoMAG cite no evomag.ro page at all; in 4 others evomag.ro is cited as a source without the brand being recommended.
Reddit is the second most-cited non-retailer source in the market: 17 answers, behind the shopilo.ro listicle (21).
Sentiment is almost uniformly positive: one single negative mention across the whole tracked set.

The raw data, free. Three CSV files — the retailer ranking, the question × brand matrix and the cited domains — are published under CC BY 4.0. Download the data

01 · How did we measure electronics retailers' visibility in AI engines?

Methodology

Parameter	Value
Questions	18, none containing a brand name
Engines	ChatGPT, Google Gemini, Perplexity, Google AI Mode, Google AI Overviews
Language and country	Romanian, from Romania
Run	One complete run, 3 August 2026
Queries issued	90 (18 × 5)
Answers analysed	87
Tracked retailers	13
Mentions recorded	378
Domains cited as sources	249 distinct
Platform	LLM Pulse, no session history, no personalisation

Three design choices deserve to be stated openly, because they determine what the numbers mean.

No brands in the prompts. No question contains the name of any shop. Everything appearing in this study was named by the engine on its own. The questions are phrased the way a real buyer phrases them: "where do I buy a good laptop for university in Romania?", "where can I buy open-box or refurbished products with warranty?", "which online shop has the best return conditions for electronics?". Ten are transactional, eight commercial.

One mention per answer, per brand. If an engine names eMAG three times in the same answer, that counts as one mention. Otherwise long answers would weigh more than short ones for no good reason.

Denominators are declared, because they are not equal. The 90 queries produced 87 answers: Google did not display an AI Overview for three questions. Overall rates are computed against 87. Per-engine rates are computed against that engine's own answer count — 18 for ChatGPT, AI Mode, Perplexity and Gemini, but 15 for AI Overviews. The monitoring platform reports AI Overview rates against 18; in this dataset they are corrected to 15. The difference is not cosmetic: evoMAG's AI Overviews rate moves from 33.3% to 40.0%.

The main limitation, declared from the start: a single run. The previous episodes showed that these hierarchies move week to week. What follows is true for 3 August 2026 and must not be read as a trend.

02 · Which electronics and IT retailers in Romania are most visible in AI answers?

The ranking: who gets the customer

#	Retailer	Mentions	Visibility	Share of mentions
1	eMAG	82	94.25%	21.69%
2	Altex	76	87.36%	20.11%
3	Flanco	48	55.17%	12.70%
4	evoMAG	47	54.02%	12.43%
5	PC Garage	44	50.57%	11.64%
6	Media Galaxy	42	48.28%	11.11%
7	Vexio	11	12.64%	2.91%
8	F64	9	10.34%	2.38%
9	ITGalaxy	8	9.20%	2.12%
10	Cel.ro	5	5.75%	1.32%
11	Quickmobile	3	3.45%	0.79%
12	iStyle	2	2.30%	0.53%
13	Amazon	1	1.15%	0.26%

The market has three clearly separated tiers. Two leaders — eMAG and Altex — named in over 87% of answers. A pack of four — Flanco, evoMAG, PC Garage, Media Galaxy — tightly grouped between 48% and 55%, separated by one or two mentions. And a long tail below 13%, where the specialists live.

The first counter-intuitive observation: the distance between third and sixth place is six mentions. Across 87 answers, four retailers are effectively tied. Stopping at this table, one would conclude that the market has two leaders and a homogeneous middle.

That conclusion would be wrong. The table above measures whether a retailer appears. It does not measure what happens to it when it does.

03 · What position does each retailer occupy inside the AI answer?

Not who appears, but how far down

This is the central finding of the study.

Retailer	Position 1	Positions 2-3	Positions 4-7	Total
eMAG	66	15	1	82
Altex	4	67	5	76
Flanco	2	29	17	48
evoMAG	3	7	37	47
PC Garage	7	15	22	44
Media Galaxy	0	23	19	42

The structure of the market becomes visible only here, and it is far more rigid than the ranking suggested:

eMAG is the answer. Of 82 mentions, 66 are in first place. When an AI engine names Romanian electronics retailers, four times out of five it begins with eMAG. That is not a leader's position — it is the position of a default answer.

Altex is the alternative. Only 4 first places, but 67 second-or-third places. Altex almost never opens the list and is almost always the second name. Its position is as stable as eMAG's, but structurally different: it is not the answer, it is the confirmation that a serious alternative exists.

The rest of the market is "the list". Flanco, evoMAG, PC Garage and Media Galaxy share positions 4-7. Media Galaxy has no first place anywhere in the dataset. evoMAG has 37 of its 47 mentions in the 4-7 band — 78.7%.

The effect shows up in weighted visibility, the metric giving 100% to a first-place mention, 50% to second, 33% to third:

Retailer	Mentions	Weighted visibility
eMAG	82	7,308
Altex	76	3,643
PC Garage	44	1,738
Flanco	48	1,699
evoMAG	47	1,310
Media Galaxy	42	1,248
F64	9	521

The ranking rearranges itself. PC Garage rises above both Flanco and evoMAG despite having fewer mentions than either — because it holds 7 first places against 2 and 3. And eMAG, with 8% more mentions than Altex, has twice the weighted visibility.

For a retailer measuring its AI presence, that is the difference between a report saying "we are fourth, that's fine" and one saying "we are fourth in presence and fifth in influence".

04 · Which retailers do ChatGPT, Gemini, Perplexity and Google AI recommend in Romania?

The five engines do not see the same market

Retailer	ChatGPT (18)	AI Mode (18)	AI Overviews (15)	Perplexity (18)	Gemini (18)
eMAG	94%	94%	93%	89%	100%
Altex	83%	94%	93%	67%	100%
Flanco	50%	78%	53%	39%	56%
evoMAG	61%	61%	40%	61%	44%
PC Garage	50%	50%	53%	44%	56%
Media Galaxy	39%	50%	33%	33%	83%
Vexio	28%	6%	13%	0%	17%
F64	6%	6%	7%	17%	17%

Gemini is simultaneously the most generous and the most conservative. It names eMAG and Altex in 100% of answers and Media Galaxy in 83% — yet drops evoMAG to 44% and Flanco to 56%. It is not an engine that cuts the list short; it is an engine that includes generously, from a fixed set of big names.

Perplexity is the most open to the long tail. The only engine naming Amazon, the only one lifting F64 and Quickmobile to 17% and 11%, and the only one dropping Altex to 67%. It is also the engine most likely to answer without naming anyone: the single answer in the entire set that names no tracked retailer is a Perplexity answer about IT equipment for companies, which discusses "wholesale IT distributors" and "large shops" without naming one.

Vexio exists almost exclusively inside ChatGPT (28%, against 0-17% elsewhere). A retailer can hold real presence in one engine and be invisible in the other four.

Google declined three times. AI Overviews did not trigger for the gaming laptop, large TV and cheapest phones questions — three of the most commercial queries in the set. When Google decides a query is too transactional it generates no answer, and every retailer's AI visibility on that question is simply zero.

05 · In which product categories do specialist shops beat the generalists?

Territories: where it is won and where it is lost

The 18 questions are not equivalent. Some are contested by the whole market; others have a single winner.

Questions where the specialist wins:

Question	Who dominates
Where do I get a professional camera or camcorder?	F64: 5 of 5 — more than eMAG (2)
Where do I buy PC components for a build of my own?	eMAG and PC Garage 5 of 5; Vexio 4 — its best score
Where do I buy an electric scooter or e-bike?	No large generalist dominates; the engines name TrotiBike, ESBI, Maros Bike, BikeXpert, DualStore, Decathlon

The F64 case is the clearest argument in the whole study. Nine mentions in total — five times fewer than evoMAG — but a weighted visibility of 521, which is 40% of evoMAG's with 19% of the mentions. The explanation is that all 9 mentions are concentrated on its own territory, and there F64 comes first. One owned territory is worth more than ten territories where you are sixth.

The most concentrated questions are the general ones — "which are the best online electronics and IT shops in Romania", "which online shops have good discounts on electronics" — where 5 to 7 of the 13 tracked retailers appear, and always roughly the same ones.

The most dispersed question is the one about electric scooters and e-bikes: only 13 mentions in total across all 13 tracked brands. The engines overwhelmingly prefer specialists, and no large generalist owns the category. It is the only territory in the set where the "official" electronics market barely exists inside AI answers.

06 · Which sources do AI engines cite when recommending an electronics retailer?

Who writes the ranking the AI reads

We inventoried every domain cited as a source: 249 distinct ones. The top 20:

Domain	Answers citing it	Type
emag.ro	50	retailer
altex.ro	45	retailer
evomag.ro	30	retailer
flanco.ro	24	retailer
shopilo.ro	21	listicle
pcgarage.ro	17	retailer
reddit.com	17	community
voucher.ro	11	listicle
mediagalaxy.ro	10	retailer
compari.ro	9	price comparison
itgalaxy.ro	8	retailer
blackfriday.ro	7	listicle
wikipedia.org	7	encyclopedia list
cel.ro	7	retailer
vexio.ro	7	retailer
gadget.ro	6	media
flip.ro	6	retailer
price.ro	5	price comparison
cuponescu.ro	5	listicle
mariuscucu.ro	5	blog

A single article — "Romanian Electronics Shops: Top 6 Compared" on shopilo.ro — is cited in 21 answers. More than PC Garage's website. More than Media Galaxy's website. More than every technology publication in the set put together.

On the market's flagship question — "which are the best online electronics and IT shops in Romania" — Perplexity builds its answer citing the Wikipedia page List of online electronics and IT shops in Romania, plus shopilo.ro and voucher.ro. Gemini cites listamagazine.ro and the same Wikipedia page. AI Mode and AI Overviews both cite shopilo.ro.

The implication matters more than the number: the order in which retailers appear inside AI answers is not recomputed from scratch by each engine. It is, to a large extent, inherited from five or six listicles and one Wikipedia list. Whoever is missing there, or appears at the bottom, inherits that position across every engine at once.

Reddit is the second non-retailer source: 17 answers, from r/CasualRO, r/roFrugal and r/Romania — threads about open-box products, return policies, and which shop people actually use. That is where the sentiment the engines later summarise as a recommendation is formed.

07 · What does it mean when a retailer is cited as a source but not recommended?

Named, cited, or both

An engine does two different things with a retailer: it names it in the answer and it cites its website as a source. The ratio between the two shows how solid the recommendation is.

Retailer	Mentions	Citations	Citations / mentions
eMAG	82	50	0.61
Altex	76	45	0.59
evoMAG	47	30	0.64
Flanco	48	24	0.50
PC Garage	44	17	0.39
Media Galaxy	42	10	0.24
ITGalaxy	8	8	1.00

Two extremes are worth examining.

Media Galaxy: 0.24. Of 42 mentions, only 10 come with a citation of mediagalaxy.ro. In three quarters of cases the engine recommends Media Galaxy because it read about it somewhere else — usually a listicle, or altex.ro, with which it shares a group. That position is not its own and shifts when the third-party article shifts.

ITGalaxy: 1.00. Every time it is named, its own site is cited too. A small retailer, but with a verifiable presence of its own behind every appearance.

The phenomenon has a more instructive inverse. For evoMAG we could measure both directions: of the 47 answers naming it, 21 cite no page on evomag.ro; and in another 4 answers evomag.ro is cited as a source without evoMAG being recommended. In the latter case, a retailer's content informed an answer in which a competitor wins — the clearest instance being the large-TV question, where Perplexity uses evoMAG pages and recommends Altex, eMAG, Flanco and Media Galaxy.

08 · Which retailers does AI recommend beyond Romania's big chains?

The competitors nobody tracks

The 13 tracked brands do not exhaust the market, and the engines demonstrate it constantly. Beyond them, the 87 answers name retailers that appear in no conventional competitive review:

Flip.ro — 9 answers, on refurbished phones. It would rank eighth in our table, above ITGalaxy, Cel.ro and iStyle.
Decathlon — 5 answers, exclusively on electric mobility.
Refurbished specialists — ReBoxed, UsedProducts, CIT Grup, Expert Company, Refurbished.ro, Resellux, MarketOnline.
Mobility specialists — TrotiBike, ESBI, Maros Bike, BikeXpert, DualStore, Atu Tech, Bimax, Pegas.
Telecom operators — Orange and Vodafone recur on the questions about phone instalments and return conditions.

The practical conclusion for any retailer drawing up a competitive map: the competitor list inside AI is not the competitor list in the market. A large generalist can lose an entire category to a niche shop it does not consider a competitor and does not monitor.

09 · What are the limitations of this study?

Limitations of this study

We list them in full, because a study that does not declare its limits is not a study.

A single run, on a single day. This is the principal limitation. Earlier episodes in the series showed that these hierarchies move weekly; here we cannot observe the movement. Everything above describes 3 August 2026.
18 questions, not the purchase universe. A different question set would produce different weights.
13 tracked brands, not the whole market. As section 08 shows, the engines constantly name retailers outside the set. "Share of mentions" figures are shares of the 13 brands' mentions, not of all possible mentions.
Roughly 5 answers per question. Differences on a single question are noise, not signal. Only patterns repeating across questions are worth reading.
Mentions are not conversions. We measure who reaches the answer, not who sells. A link between the two is plausible but not measured here.
Models update continuously. An August 2026 answer does not guarantee the same answer in September.
A single unpersonalised profile, one country, one language. A user with search history might receive different answers.
Source type classification (retailer, listicle, community, price comparison) was done by the authors and is, by nature, debatable at the margins.

10 · What does this mean for an electronics retailer in Romania?

Implications

Three implications that follow directly from the data without exceeding it.

Presence is no longer the objective; position is. In a market where 86 of 87 answers name a major retailer, "we appear in AI" is no longer an achievement. The difference between first and sixth place in a generated list is the difference between receiving the decision and receiving a comparison. Any AI visibility report stopping at mention rate misses exactly the variable that matters.

Concentrated territory beats diffuse presence. F64 demonstrates the arithmetic: 9 well-placed mentions on one territory produce 40% of the weighted visibility of a retailer with 47 scattered ones. For a mid-sized retailer the rational strategy is not to compete with eMAG on general questions — it is to identify the categories it can own and own them visibly.

The ranking is written off your website. Five listicles and one Wikipedia page are cited in over 50 answers combined. They are public-relations inventory, not technical SEO, and right now they decide the order in which the market appears. And Reddit — 17 answers — cannot be bought or simulated; it is earned by solving real cases in public.

11 · Frequently asked questions

What does "visibility" mean in this study?
The percentage of answers naming the brand, out of the 87 analysed answers. One answer contributes at most one mention per brand, however many times it is named in the text.

Why 87 answers and not 90?
90 queries were issued (18 questions × 5 engines), but Google did not display an AI Overview for three questions. Those three produced no answer to analyse.

Why are AI Overviews rates computed against 15?
Because AI Overviews produced 15 answers, not 18. Using 18 would systematically underestimate every brand on that engine. The monitoring platform uses 18; the data published here corrects the denominator to 15.

What is weighted visibility?
A metric accounting for the position of the mention: 100% for first place, 50% for second, 33% for third and so on, summed across the five engines. It is unbounded and does not read as a percentage.

How were the 13 retailers chosen?
They are the retailers tracked in the monitoring project that generated the data: Romania's large generalists plus several relevant specialists. It is not an exhaustive list of the market, and section 08 shows explicitly what falls outside it.

Did you use brand names in the questions?
No. None of the 18 questions contains the name of any shop. Everything that appears was named by the engine.

Why a single run, when earlier episodes had two?
Because the study was built on the data available at the time. It is a real limitation, declared in sections 01 and 09, and the reason this episode contains no volatility section.

Can a retailer change its position?
The data shows where position is decided: in the listicles cited as sources, in Reddit discussions, and in whether the retailer has its own pages answering the question the way the buyer asks it. The study does not measure the effect of an intervention — only the state of play.

What does it mean for a brand to be cited but not named?
That the engine used a page from its website as a source of information, but recommended someone else. The content worked for an answer the brand is absent from.

Is the data reusable?
Yes. The three CSV files are published under CC BY 4.0, with attribution. Commercial use is permitted provided the study is cited.

Why are there no sales figures or real market shares?
Because the study does not measure them. It measures presence inside AI answers, and nothing else. Correlation with sales is plausible but not demonstrated here.

The data, deposited publicly

Statement of responsibility

This study measures only the presence of brands inside answers generated by AI engines, on a single day. It does not measure the quality of the retailers, their prices, their service or their sales. "X is the most visible" does not mean "X is the best". Every brand appearing here was named by the engines, not by the authors, and the 13 tracked retailers do not exhaust Romania's electronics and IT market. Mentions are not conversions. All figures can be re-verified from the published raw data, and any error reported will be corrected publicly, with versioning.

Original Websem study · Dan Cristian Alexandrescu · August 2026

Raw data: CC BY 4.0 — DOI 10.5281/zenodo.21821185

Who sells the book when the customer asks an AI? Romania's book market across 5 AI engines

Dan Cristian — Sat, 01 Aug 2026 13:19:01 +0000

When someone asks ChatGPT "which online bookstore should I use for children's books?", they get one answer, not ten blue links.

So we measured who is inside that answer.

We put 18 real purchase questions to five AI engines — ChatGPT, Google Gemini, Perplexity, Google AI Mode and Google AI Overviews — in Romanian, from Romania, across two complete weekly runs. No question contained a brand name. Every brand that appears was named by the engine on its own.

The result: 180 analysed answers, 247 brand mentions, 21 tracked brands, 422 distinct cited domains.

The headline finding is the one we did not expect.

58% of answers name nobody

Out of 180 answers, only 75 (42%) mention any of the 21 major brands in the market. The other 105 respond with book titles, authors and small sites — but never say where to buy.

And of those 75, only 19 recommend a single brand. The rest share the stage between 3.3 brands on average.

Six of the eighteen questions gathered three or fewer brand mentions in total, across every engine, in both weeks:

Question territory	Total brand mentions
Business books for entrepreneurs	1
Books for a teenager who barely reads	1
Motivational vs. psychology	1
Easy English reading	2
Cooking	3
Nutrition and health	3

A third of the market's purchase territory currently belongs to nobody.

Being mentioned is not the same as being recommended

Here is the part that changes how you should measure this.

The market leader, Libris, has 37 mentions — 20.6% of all answers. But the interesting number is where those mentions land:

	Position 1	Positions 2–3	Positions 4–7	Position 8+
Libris (37 appearances)	24 (65%)	8 (22%)	5 (13%)	0
BookZone (24 appearances)	3 (12%)	12 (50%)	8 (33%)	1 (4%)

Libris doesn't just appear most often — it appears first two times out of three.

Weight by position (1st = 100%, 2nd = 50%, 3rd = 33%…) and the hierarchy rewrites itself. Editura Trei is 6th by raw mentions but 3rd by weighted visibility: it appears rarely, but when it does, it appears at the top. Few territories, owned.

If you are tracking AI visibility with a mention counter, you are measuring the wrong thing.

Each engine is a different channel

Mention rate per brand, as a share of each engine's 36 answers:

Brand	ChatGPT	Gemini	Perplexity	AI Mode	AI Overviews
Libris	13.9%	30.6%	16.7%	13.9%	27.8%
Litera	13.9%	25.0%	5.6%	27.8%	11.1%
Cărturești	11.1%	25.0%	8.3%	16.7%	19.4%
BookZone	8.3%	8.3%	11.1%	22.2%	16.7%
Humanitas	5.6%	19.4%	5.6%	5.6%	0%

Two things worth pulling out:

Engines differ wildly in generosity. Gemini produced 78 mentions across its 36 answers — 2.2 brands per answer. Perplexity produced 30 — 0.8 per answer. Statistically, one recommendation in Perplexity is worth more than two and a half in Gemini, because there are far fewer places on the podium.

Same company, opposite verdicts. Humanitas sits at 19.4% on Gemini and 0% on Google AI Overviews. Same brand, same content, two Google products, completely different retrieval logic.

Ghost brands: cited but never named

This is the finding with the most direct practical value.

Being cited (your site feeds the answer) and being named (the answer recommends you) are different currencies. Cross-reference them and two species appear:

Brand	Cited as source	Named as brand	Diagnosis
Târgul Cărții	18	4	ghost
Librarul	14	1	ghost
eMAG	15	9	under-named
Editura Trei	5	17	memory
Nemira	1	10	memory
Humanitas	0	13	memory

Ghost brands get crawled, their listings and prices become raw material for the answer — and then the engine recommends somebody else. Their content is credible; their entity is not. In practice this points at weak entity signals: incomplete structured data, no consolidated identity (sameAs, profiles, reviews), a generic name that is hard to bind to a domain.

If your site is cited but your name is missing from recommendations, the fix is not more content. It's entity work — and it's the fastest win in this whole field.

Memory brands are the mirror image. Humanitas is recommended 13 times with zero citations of humanitas.ro in the entire window. It lives purely off the models' parametric memory — decades of cultural presence. Flattering, but fragile: wherever the answer gets built from crawlable sources and your site isn't among them, you drop out of the conversation.

Reddit is the market's second source

422 distinct domains were cited across the 180 answers. The top of that list:

Domain	Answers citing it
carturesti.ro	46
reddit.com	34
litera.ro	34
libris.ro	34
bookzone.ro	29
targulcartii.ro	18

Reddit sits above Facebook (11), YouTube (6) and every media publication in the set. When somebody asks an AI which bookstore to use, part of the answer comes from readers arguing with each other in threads no brand controls.

There is also a shadow market in that list — clb.ro, librex.ro, librariadelfin.ro, booknation.ro — sites with almost no public recognition that show up constantly. Models don't cite by fame. They cite what they can crawl and parse.

The rankings move weekly

Same 18 questions, same 5 engines, seven days apart:

Brand	20 Jul	27 Jul	Change
Editura Corint	2	8	×4
elefant.ro	9	13	+44%
Humanitas	5	8	+60%
Libris	21	16	−24%
Editura Trei	12	5	−58%

elefant.ro went from 7.0% to 10.9% share of voice in one week — a jump that in organic Google would take months.

The right word for this isn't chaos, it's plasticity. Answers regenerate at every query, from re-crawled sources and constantly updated models. For today's leaders that means the advantage needs maintenance. For everyone else it means the window to enter is permanently open — unlike classic SEO, where positions 1–3 are set in concrete by years of accumulated authority.

Reproduce it

The per-engine rates are n/36 rounded to one decimal, so the underlying counts reconstruct exactly:

import csv

rows = list(csv.DictReader(open("piata-carte-ai-2026-07.csv", encoding="utf-8")))
engines = ["chatgpt_pct", "gemini_pct", "perplexity_pct",
           "google_ai_mode_pct", "google_ai_overviews_pct"]

by_engine = {e: sum(round(float(r[e]) * 36 / 100) for r in rows) for e in engines}
print(by_engine)                # 33, 78, 30, 56, 50
print(sum(by_engine.values()))  # 247

247 also equals the sum of mentiuni_total, and the sum of the two weekly runs (128 + 119), and the sum of mentions across all 18 questions. Four independent axes, one number.

One trap: the dataset uses two denominators. Mention rates are against 180 answers (per-engine: 36). Citation percentages are against 172 — only the answers that actually contain citations. Eight answers were generated with no links at all.

Get the data

Everything above comes from three CSV files, published CC BY 4.0 — free to use, including commercially, with attribution:

Zenodo (canonical deposit, DOI): 10.5281/zenodo.21736211
GitHub: WebSEM-ai/websem-ai-visibility-book-market-ro
Kaggle: ai-search-visibility-romania-book-market
Hugging Face: WebSEM-ai/ai-search-visibility-romania-book-market

All four carry byte-identical files. Take them and check us.

Limits, stated up front

Two runs over two weeks — enough to see volatility, not enough for trends. 18 questions, not the full purchase universe. 21 tracked brands, not the whole market; smaller players appear only in the citation data. Roughly 10 answers per question, so single-question deltas are noise. Mentions are not conversions. Models update continuously — these figures describe engine behaviour between 18 and 31 July 2026, not a permanent truth.

All brands named here were named by the AI engines, not by us. This is market research, not an evaluation of anyone's products or service.

Full study, with all 18 questions and interactive charts: websem.ro

Episode 1 of the series asked ten LLMs a single identical question about luxury jewelry and got 29 different brands back, with 18% agreement between models — DOI 10.5281/zenodo.21724399.

We Asked 10 LLMs to Recommend Brands. They Gave Us 29 Different Answers.

Dan Cristian — Fri, 31 Jul 2026 13:23:59 +0000

When querying Large Language Models for product or brand recommendations, developers and marketers often assume top-tier models converge on a shared ground truth.

To test this assumption empirically, we conducted a benchmark across 10 major LLM architectures to evaluate entity retrieval variance, inter-model consensus, and RAG divergence in a niche market context: luxury jewelry brands in Romania for wedding and engagement rings.

Here is what the data showed.

🛠️ Experimental Methodology

To isolate model behavior and prevent prompt-induced bias, we established a strict protocol:

Prompt: "Ce branduri de bijuterii de lux din România îmi recomanzi pentru verighete și inele de logodnă? Dă-mi un top 5, cu un argument scurt pentru fiecare și sursele pe care te bazezi." (Zero-shot, asking for Top 5, short justification, and cited sources)
Brand Hints: 0 (No brands mentioned in the prompt).
Session State: Stateless, fresh context window, zero history/custom instructions.
Execution: Single run per model (no cherry-picking or regeneration).
Models Evaluated (N=10): ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Copilot (Microsoft), Grok (xAI), Perplexity AI, DeepSeek, Kimi (Moonshot), GLM (Zhipu), and Qwen (Alibaba).

📊 Key Findings & Metrics

Across 10 models, there were 50 total available ranking slots (10 models × 5 slots).

1. High Entity Entropy

29 distinct entities were returned across the 50 slots.
19 out of 29 brands (66.2%) were recommended by only a single model.
No single brand achieved 100% reach across all 10 models.

2. Inter-Model Agreement

The average pairwise overlap between any two arbitrary models was 18% (an average of 0.91 shared entities per 5 recommendations).
Top-ranked entity: TEILOR appeared in 6 out of 10 models (Average rank: 2.0, 3× #1 placements).
Runner-up: Malvensky appeared in 5 out of 10 models (Average rank: 1.2, 4× #1 placements).

3. The Copilot Anomaly (RAG Divergence)

Microsoft Copilot exhibited an overlap coefficient of 0.00 relative to the other 9 models. It returned a set of entities completely unique to its search context, highlighting how divergent web search grounding mechanisms (Bing RAG pipeline) drastically alter LLM outputs compared to base model training data or alternative web indices.

📈 Aggregated Leaderboard

Rank	Brand	Model Frequency	Market Coverage (%)	Avg Position (Lower = Better)	#1 Placements
1	TEILOR	6 / 10	60%	2.0	3
2	Malvensky	5 / 10	50%	1.2	4
3	Sabion	3 / 10	30%	2.0	0
4	Coriolan	3 / 10	30%	2.3	1
5	KULTHO	3 / 10	30%	3.7	0
6	Sabrini	3 / 10	30%	3.7	0

🧠 Technical Implications for AEO / GEO

For engineers building Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) pipelines, this benchmark highlights several critical systems insights:

Deterministic Consensus is an Illusion: In niche or regional e-commerce queries, model outputs do not collapse into a single consolidated knowledge graph. Output entropy remains high.
RAG vs. Parametric Memory: Models leaning heavily on real-time web search (Perplexity, Copilot) diverge significantly from standard base models depending on web index freshness and search query decomposition.
Structured Data Visibility: Entities with strong digital footprints, explicit schema markups, and widespread press mentions (TEILOR, Malvensky) consistently bypass RAG filtering barriers across multiple model architectures.

🔗 Resources & Raw Data

You can inspect the full raw dataset (CSV) and detailed breakdowns on the study page:

Study URL: Websem AEO Research: Luxury Jewelry Study
Dataset: Raw response logs and screenshots archived for replication.

What techniques are you using to measure LLM visibility or RAG consistency across regional datasets? Let's discuss in the comments below!

Generative Engine Optimization (GEO) & AEO: How We Replaced Traditional SEO for LLMs

Dan Cristian — Thu, 30 Jul 2026 21:44:22 +0000

Generative Engine Optimization (GEO) and AEO: How We Replaced Traditional SEO for Search LLMs
Tags: seo, webdev, ai, digitalmarketing

The landscape of online discovery is undergoing a seismic shift. Users are no longer just typing two-word keyphrases into standard search boxes and clicking through pages of blue links. Instead, they are having full conversational interactions with Search-Aware Large Language Models like ChatGPT, Perplexity, Gemini, and Claude to get immediate, synthesized answers.

If your web strategy is still optimized purely for traditional 2015-era search crawlers, your content is quickly becoming invisible to the engines that drive modern user behavior.

At websem.ro, we have spent the last few years analyzing how Retrieval-Augmented Generation (RAG) pipelines and generative vector search engines index, weigh, and cite digital sources in real time. Our primary conclusion is simple: traditional Search Engine Optimization (SEO) must evolve into Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO).

Here is an in-depth breakdown of how generative engines process information and how you can optimize your digital assets to ensure your brand gets consistently cited by AI agents.

Understanding the Shift: GEO vs. AEO vs. Traditional SEO To optimize for AI discovery, you first need to understand the fundamental mechanical differences between how standard algorithms rank web pages and how generative models retrieve information.

Traditional SEO
Focuses on keyword matching, page-level authority (backlinks), and domain architecture to rank a specific URL on a Search Engine Results Page (SERP).

Answer Engine Optimization (AEO)
Focuses on single-intent, factual queries. The primary objective of AEO is to position your brand as the single authoritative, zero-click data source for direct answer modules like Perplexity Quick Search, Google AI Overviews, or voice assistants.

Generative Engine Optimization (GEO)
Focuses on broader, comparative, and complex multi-source synthesized responses. GEO ensures that when an LLM builds a summary (e.g., "Compare top digital marketing and technical SEO frameworks in Eastern Europe"), your brand is included in the generated narrative due to strong semantic vector associations and cross-web consensus.

Technical Infrastructure: Semantic Entity Alignment and JSON-LD Large Language Models do not read web pages like humans do, nor do they rely solely on standard HTML structure like basic web scrapers. They look for clear entity relationships to prevent hallucination.

If an AI engine cannot definitively verify who you are, what you do, and what specific topics you hold authority over, it will exclude your domain from its citation pool.

Implementing Rich Entity Schemas
To build a permanent semantic record for AI agents, you must implement multi-layered JSON-LD Schema.org markup across your primary pages. Your schema should explicitly define your entity, linking it to established Knowledge Graphs across the web.

Key schema properties to prioritize include:

@type Organization or ProfessionalService: Clear definition of your identity.

knowsAbout: A dedicated array of exact domain topics (e.g., "Generative Engine Optimization", "Answer Engine Optimization", "Semantic Web Architecture").

sameAs: Direct references to your official social profiles, GitHub repositories, Crunchbase profiles, and verified local directories.

By establishing these explicit semantic ties on websem.ro, we give AI crawlers absolute clarity on our core competencies, drastically increasing the likelihood of brand inclusion in AI-generated answers.

Information Architecture for RAG Engines and Vector Search Most modern search-aware AI platforms rely on RAG (Retrieval-Augmented Generation). When a user submits a prompt, the system breaks down top-retrieved web pages into small text fragments called chunks, converts those chunks into vector embeddings, and selects the chunks with the highest cosine similarity to the user's prompt.

If your content is buried inside long-winded introductions or conversational filler, the RAG engine will skip your page entirely.

Core Rules for Chunk-Friendly Content Design
Rule A: The Inverted Pyramid Model
Always state the direct answer or core solution within the first two sentences immediately following an H2 or H3 heading. Provide the high-density answer first, then elaborate with technical context below it.

Rule B: Conversational Question-and-Answer Headers
Phrase your subheadings (H2s and H3s) as literal questions that real users ask LLMs. For example, instead of naming a section "GEO Strategies", use "How Does Generative Engine Optimization Work for Web Publishers?".

Rule C: Data Density and Structured Tables
LLMs display a strong bias toward high information density. Incorporating clean HTML data tables, step-by-step numbered technical processes, concrete stats, and original research makes your content significantly easier for an LLM to extract and quote accurately.

Measuring and Auditing Your GEO Performance One of the biggest hurdles for digital strategists transitioning to AEO and GEO is analytics. Traditional metrics like overall SERP rank or impressions in Google Search Console do not give you the full picture of your visibility inside conversational AI environments.

To effectively monitor your AEO and GEO footprint, focus on three primary metrics:

Brand Citation Frequency: Continuously test relevant industry prompts across ChatGPT, Perplexity, Gemini, and Claude to monitor whether websem.ro is listed as an inline footnote or source link.

Bing Webmaster Tools Indexing: AI platforms like ChatGPT Search rely heavily on the Bing search index and Bing API. Maintaining zero crawl errors and instant sitemap submission in Bing Webmaster Tools is critical for AI visibility.

Referral Traffic from AI Domains: Track direct referral sessions coming from user interactions on platforms like perplexity.ai, chatgpt.com, or copilot.microsoft.com inside your analytics dashboard.