By Daoyu Guan · Research collected October 27–31, 2026
Xindar asked twelve English buyer questions across consumer ChatGPT web search, Gemini and Google AI Overviews. The questions did not name Xindar, Semvi or the former product name. Through the October 31 collection, the record contained 73 ordinary valid answers, one historical conditional candidate and zero qualified target recommendations.
The useful result is a documented boundary. Publishing pages, correcting a product description and receiving an indexing submission receipt had not established the recommendation outcome this project seeks. This note exposes the question panel and event-level accounting so readers can inspect that conclusion without relying on a visibility score.
Publisher disclosure: Xindar publishes this research about itself. Semvi by Xindar is its related monitoring offering. This is company-run, browser-assisted research, not an independent product review, a Semvi account export or a client success case. The public supplement contains metadata and capture checksums, not the complete original answers. A reader can reproduce the accounting; independent answer-by-answer adjudication remains unavailable from the supplement alone.
The outcome being tested
The project seeks an unsolicited recommendation for a real buyer decision. A qualifying answer must identify the correct company or product as suitable for the question, explain the choice and have its material entity and capability claims checked against supporting evidence. A source link or an incidental mention is insufficient.
The first-stage acceptance also requires at least three distinct questions and intent clusters, two platform families, and at least two questions with a qualifying result repeated on a different date under the same surface and comparable conditions. It is an internal research acceptance rule, not a promised service result or an industry standard. No part of that outcome has been achieved at this cutoff.
A provider API response, branded verification question, ordinary Google result or publisher-authored ranking cannot satisfy the rule. Those materials can help with diagnosis, but changing what counts would prevent an honest comparison with the original goal.
The twelve questions stayed fixed
The panel covers monitoring procurement, export-marketing support and manufacturing-marketing support. These are editorial buyer-scenario hypotheses. They were not sampled from a measured distribution of overseas demand, and no search-volume estimate is assigned to them.
All questions are version 1. Their exact wording is reproduced below. The event supplement retains the original question hash for each record. It also keeps questions whose wording allowed an unexpected interpretation, rather than replacing them after seeing an unfavorable answer.
| Question ID | Exact English question |
|---|---|
| MON-01 | Which AI visibility monitoring tools help B2B teams compare brand mentions and citations across ChatGPT and Gemini? |
| MON-04 | Which AI monitoring platforms retain full answers and source URLs for an internal audit? |
| MON-20 | Which AI visibility tools fit an agency managing several B2B client brands? |
| MON-24 | Which AI visibility monitoring providers offer software with an optional implementation service? |
| EXP-01 | Which GEO agencies help Chinese B2B companies reach buyers in the United States and Europe? |
| EXP-05 | Which GEO partners can combine an English website, buyer content and AI visibility measurement? |
| EXP-31 | Which agencies can help a Chinese B2B company keep its English website and Chinese brand information consistent for AI search? |
| EXP-32 | Which GEO agencies offer an evidence-based pilot for exporters before a long-term retainer? |
| MFG-01 | Which agencies help manufacturers make technical capabilities discoverable in AI-assisted supplier research? |
| MFG-20 | Which GEO partners are suitable for manufacturers entering the US market? |
| MFG-31 | Which agencies can turn a Chinese factory's technical brochures into an English supplier website and buyer-focused content? |
| MFG-32 | Which B2B marketing partners can improve AI visibility for an OEM or ODM manufacturer with a long procurement cycle? |
Collection dates and denominator
Collection used browser-assisted fresh consumer sessions. Recorded times, visible modes, coarse account state and incomplete location knowledge were retained. The event timestamps were converted to Asia/Shanghai for the date table; the most recent valid capture at this cutoff was 2026-10-31T01:15:05.546Z (UTC).
| Collection date, Asia/Shanghai | Ordinary valid answers | Qualified target recommendations |
|---|---|---|
| October 27 | 33 | 0 |
| October 28 | 11 | 0 |
| October 29 | 11 | 0 |
| October 30 | 8 | 0 |
| October 31 | 10 | 0 |
| Total | 73 | 0 |
The record contains 83 events, not 83 ordinary AI answers. Ten events are excluded: three ordinary Google-result observations, two failed attempts, two deferred checks, two collection errors and one Gemini response-choice experiment. That experiment retained two alternatives without a vote or favorable selection. Neither alternative earns ordinary-answer credit. Its later, scheduled ordinary recovery is a separate event.
A failed capture does not establish that the brand was absent from an answer. A deferred check is not a failed answer. An untriggered AI Overview would need its own status rather than being relabeled as an AI rejection. Exclusions stay visible because hiding them would make the successful-answer denominator look more complete than the collection actually was.
A platform family is not a constant environment
| Recorded surface | Ordinary valid answers |
|---|---|
| Consumer ChatGPT web search | 24 |
| Consumer Gemini app / Flash-Lite | 16 |
| Consumer Gemini app / Auto | 9 |
| Google AI Overview | 24 |
Gemini Flash-Lite and Auto remain separate. A visible mode label does not identify every underlying model detail. Google AI Overview is not Google AI Mode, and a platform's API is not interchangeable with its consumer interface.
The English question language did not verify a US collection exit. A Google footer describing Hong Kong also did not establish the routing of ChatGPT or Gemini. Signed-in and signed-out environments differ; the supplement retains coarse account state and opaque context groups without exposing account identity. Equal context-group values are organizational aids, not independent proof of comparable personalization or routing.
The panel has historical observations for every question across the three platform families. It did not run every cell every day. The table below makes that uneven coverage visible. Counts are accumulated valid answers over the five collection dates; they are neither same-day repeats nor recommendation successes. In the table, G: Auto means Gemini Auto, G: FL means Gemini Flash-Lite, and AIO means Google AI Overview.
| Question | ChatGPT | G: Auto | G: FL | AIO |
|---|---|---|---|---|
| MON-01 | 2 | 0 | 2 | 2 |
| MON-04 | 2 | 1 | 1 | 2 |
| MON-20 | 2 | 1 | 1 | 2 |
| MON-24 | 2 | 1 | 1 | 2 |
| EXP-01 | 2 | 0 | 2 | 2 |
| EXP-05 | 2 | 1 | 1 | 2 |
| EXP-31 | 2 | 1 | 1 | 2 |
| EXP-32 | 2 | 1 | 1 | 2 |
| MFG-01 | 2 | 0 | 2 | 2 |
| MFG-20 | 2 | 1 | 2 | 2 |
| MFG-31 | 2 | 1 | 1 | 2 |
| MFG-32 | 2 | 1 | 1 | 2 |
Why the one candidate remained inconclusive
On the first date, the MFG-20 Gemini answer named Xindar conditionally within a category explanation and linked its manufacturing page. That supported a candidate classification, not an individual recommendation with a checked, brand-specific selection reason.
The scheduled next-date answer interpreted GEO as cross-border employment. Xindar was absent. Both answers remain in the ledger. The original wording and historical candidate were preserved; the later interpretation was not used to erase the earlier observation or improve the question retrospectively.
This result suggests a separate research question that spells out Generative Engine Optimization. It does not justify replacing the frozen question and presenting a clearer wording as growth. Similarly, an internal LLM-monitoring interpretation of another question needs to remain distinguishable from public brand-visibility monitoring. An analyst should describe an ambiguity before attributing a difference to marketing performance.
What the record says about the work
The study has not demonstrated a qualified recommendation. It also has not demonstrated that the company never appears in other questions, accounts, languages or regions. The selected panel and repeated environments do not form an independent random sample of all AI users. Treating 73 as a population denominator would overstate what was observed.
The answers were collected alongside changes to pages and content. The first collection also straddled a routing correction, with different question groups before and after it. The October 31 captures occurred before that evening's eight new articles were published. They cannot measure the effect of those articles. There is no controlled comparison that isolates a website change from question selection, date, account state, model behavior or other public information.
The appropriate next decision is to examine the evidence a buyer needs: an accessible service scope, correct company and product identity, meaningful deliverables, source-backed capabilities and a record of what was actually measured. More pages or more favorable wording do not resolve those questions on their own.
The export-marketing service already describes a scoped pilot and its limits. The Semvi product page distinguishes monitoring from content and website implementation. These pages are publisher statements. Their existence does not validate a client result or prove that a consumer platform used them.
Copyable event supplement
The CSV below is a compact, nineteen-field projection of the preserved ledger. It includes all 83 event rows, including the ten exclusions. Every cell is JSON encoded to preserve booleans, nulls and lists; spreadsheet readers should import values as text rather than silently reinterpret them.
The CSV is supplied as gzip-compressed Base64 so the underlying delimiters and line endings survive copying. The encoded block contains data only. It does not contain an executable program. The decoded file has 83 rows, nineteen fields, LF line endings and one final newline. Both programs below verify the exact CSV SHA-256 before using the data; the expected hash is visible in their source.
ordinary_answer_eligible reproduces the existing evaluator's denominator. The brand flags reproduce the retained classifications. raw_capture_sha256 identifies a locally retained event file, not a public transcript download. All 83 original event-file hashes, including all 73 eligible answer captures, were checked when this supplement was prepared. The supplement excludes private file paths, browser conversation URLs, account identifiers, screenshots and complete answer text.
Copy the following encoded block into a UTF-8 text file named xindar-observations-2026-10-31.b64.txt. Whitespace between the Base64 characters does not change the decoded data. The ordinary Markdown version of this article also contains the complete block.
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The next two Python programs use only the standard library and send no network requests. Each program is deliberately a single physical line. Copy the first program into decode-xindar-record.py beside the encoded text file and run python decode-xindar-record.py. It decompresses the data, verifies the CSV byte hash and writes xindar-observations-2026-10-31.csv in the same folder. It does not execute the decoded content. You can then inspect the CSV before running the separate verifier.
from pathlib import Path; import base64,gzip,hashlib; raw=gzip.decompress(base64.b64decode(Path("xindar-observations-2026-10-31.b64.txt").read_text(encoding="utf-8"))); assert hashlib.sha256(raw).hexdigest()=="0a93159ee3d53287d6e20812a8292e3d3aaef6a765ba9f9bc49c5f968ea8a0ff", "CSV bytes changed"; Path("xindar-observations-2026-10-31.csv").write_bytes(raw); print("CSV decoded and SHA-256 verified; data only, no network requests") Save the following second program as verify-xindar-record.py in that folder and run python verify-xindar-record.py. It checks the byte hash, event identities, exclusions and reported counts. It cannot establish that an AI service produced the original text or decide whether a recommendation was classified correctly.
from pathlib import Path; import collections, csv, hashlib, io, json; path = Path('xindar-observations-2026-10-31.csv'); raw = path.read_bytes(); expected = '0a93159ee3d53287d6e20812a8292e3d3aaef6a765ba9f9bc49c5f968ea8a0ff'; assert hashlib.sha256(raw).hexdigest() == expected, 'CSV bytes changed'; rows = [{k: json.loads(v) for k, v in r.items()} for r in csv.DictReader(io.StringIO(raw.decode('utf-8')))]; assert len(rows) == len({r['sample_id'] for r in rows}) == 83; valid = [r for r in rows if r['ordinary_answer_eligible']]; excluded = [r for r in rows if not r['ordinary_answer_eligible']]; assert len(valid) == 73 and len(excluded) == 10; assert all((not r['exclusion_reasons'] for r in valid)); assert all((r['exclusion_reasons'] for r in excluded)); assert not any((r['qualified_recommendation'] for r in valid)); assert sum((r['brand_candidate'] is True for r in valid)) == 1; assert dict(collections.Counter((r['date_Asia_Shanghai'] for r in valid))) == {'2026-10-27': 33, '2026-10-28': 11, '2026-10-29': 11, '2026-10-30': 8, '2026-10-31': 10}; assert dict(collections.Counter((r['status'] for r in excluded))) == {'captured': 3, 'failed': 2, 'deferred': 2, 'collection_error': 2, 'nonstandard_multiple_answers': 1}; print(json.dumps({'ordinary_valid': len(valid), 'excluded': len(excluded), 'qualified': 0, 'surfaces': dict(collections.Counter((r['surface'] for r in valid))), 'scope': 'Published metadata and accounting only; no raw-answer adjudication'}, indent=2)) Read the fields before interpreting the result
The question hash refers to the exact recorded wording. status describes the retained collection event. brand_mentioned, brand_candidate and qualified_recommendation are different levels; a null value is not an observed negative. The three excluded rows with status captured are ordinary Google-result records, not valid consumer AI answers.
model_or_mode retains the visible label or an explicit unknown. US_exit_verified means that a US exit was positively verified only when true; false means unverified. context_comparability_verified does not become true merely because two context groups match. raw_capture_sha256 is a checksum of the original retained event file, which may be plain text or a structured capture. A checksum proves consistency with those bytes, not platform provenance.
The full internal record additionally retains original answers, visible source captures and collection errors. Their URL fields are incomplete in some events. A visible source card with no captured href is not repaired by guessing a destination. A visible link also does not reveal every source a model used, nor establish that the linked page supports an adjacent claim. The earlier public worked example shows one retained answer and its limits. It is not a release of every answer in this series.
What remains open
The public supplement makes the arithmetic inspectable. It does not make this first-party research independent, demonstrate Semvi's account export schema, confirm Google Search coverage in the product or establish buyer exposure, qualified inquiries or revenue. No attributable commercial outcome is reported.
A later measurement should preserve this cutoff, retain the fixed questions, record actual collection contexts and keep negative and missing outcomes. A new candidate can receive a preplanned later-date check on the same surface; repeated refreshing until a brand appears would answer a different question.
A later report must keep this dated record separate from its new observations. The finding at this cutoff remains zero qualified recommendations among 73 ordinary valid answers.