Diagnostic sample · even we did not pass

We ran an AI information audit on Sinnet Cloud Data Co., Ltd. itself — in 2026 Q2 its AI match score was only 42 out of 100. This is not modesty but the most honest persuasion there is: dare to find out how your brand really performs in AI?

AI information audit report · anonymized sample

Sinnet Cloud AI match score 42/100(fail — pass mark is 60/100)

Sample: Sinnet Cloud Data Co., Ltd. · 2026 Q2 · six-dimension weighted score (0–100)

42/100

AI match score · fail

Of the six dimensions, entity knowledge building and content authority are the clearest weak points — the typical profile of a company with “strong technical foundations but thin external knowledge assets”.

Verifiable data · Sinnet Cloud's own sample

Six-dimension diagnosis (0–100)

content authority 35

Information structure 48

Technical accessibility 55

entity knowledge building 28

Authoritative source coverage 52

Cross-platform consistency 38

What the six dimensions mean and this diagnosis's conclusions
Dimension	Score	What is assessed	Diagnostic conclusion
content authority	35	Whether there is verifiable specific data, technical parameters and expert attribution	Much of the wording stays at the level of adjectives, lacking hard facts AI can cite directly
Information structure	48	Schema markup, semantic tags and logical hierarchy	Core pages have incomplete Schema coverage and lack cross-referenced entities
Technical accessibility	55	robots allowances, static rendering and performance	Crawler allowances are incomplete, and the site structure relies on single-page anchors
entity knowledge building	28	How firmly the brand entity is established in external knowledge sources	The biggest weakness: the brand entity is missing from external knowledge sources and is easily confused with a same-name entity
Authoritative source coverage	52	Citations by authoritative media and industry bodies	Limited media coverage and insufficient association with authoritative bodies
Cross-platform consistency	38	Consistency of key parameters across the website, encyclopedias, WeChat accounts and industry media	Key parameters are worded inconsistently, leaving room for AI hallucination

“Even we only scored 42 — dare to find out how your brand really performs in AI?” Sinnet Cloud aileo sales team · 30-second elevator-pitch closer

Representative engagements

Five brands, five different GEO propositions

All case summaries come from real projects; client-sensitive information is anonymized. For detailed client content, visit each company's GEO page.

Cloud computing / AI compute centers

Sinnet Cloud · getting its compute foundation recommended by AI

Drawing on parent company Sinnet Technology's 4,000P compute clusters and real operating data, we rebuilt the fact base and Schema system for core scenarios such as “AI compute services” and “GPU compute rental”, turning technical strengths into hard facts AI can cite.

Core proposition: strong technical capability but thin external knowledge assets
Key actions: fact-base governance · full Schema markup · authoritative source placement

Visit the Sinnet Cloud GEO page →

AI application platforms

Molly platform · turning product capability into AI's default answer

For enterprise AI application development and collaboration scenarios, we mapped the capability boundaries and conditions of Agent building, knowledge-base management and workflow orchestration, so questions like “how do I choose an enterprise Agent platform” reliably bring up Molly.

Core proposition: a new product form, with no established industry question set
Key actions: semantic map construction · scenario-based question sets · capability comparison content

Visit the Molly platform GEO page →

Industrial wireless / edge networks

Altai · getting “industrial wireless private networks” described accurately

Industrial wireless is a classically parameter-dense category, where AI easily hallucinates coverage radius, frequency bands and deployment conditions. The project focused on structuring qualifications, parameters and real deployment scenarios into verifiable facts.

Core proposition: many technical parameters, high risk of AI hallucination
Key actions: parameter fact base · scenario structuring · cross-border compliance content

Visit the Altai GEO page →

Environmental protection / resource recycling

Langfang Jinye Environmental · writing “compliant, verifiable, traceable” into AI's fact base

When AI misstates an environmental company's qualifications and service boundaries, it turns into a trust crisis. The project used qualifications, licenses, treatment capacity and processes as a skeleton to build a compliance narrative that machines can parse completely.

Core proposition: complex compliance qualifications, where wording errors are costly
Key actions: qualification structuring · process semantics · ESG narrative

Visit the Jinye Environmental GEO page →

Brand services / GEO

aileo · practicing what we preach: the provider's own site gets cited first

aileo's own website is a full build-out: an official fact base, an entity-disambiguation knowledge graph, a site-wide JSON-LD entity graph with an llms.txt machine-readable layer, plus a bilingual mirror and the source publishing API, all built to the same standard we deliver to clients.

Core issue: a GEO provider's own site must stand up to AI interrogation first
Key moves: site-wide Schema entity graph · llms.txt machine-readable layer · bilingual mirror and source API

Enter the aileo GEO page →

See all clients and client content →

Case disclosure principles

Verifiable data, no fabrication, no exaggeration

Real project sources

Every case we disclose comes from a real delivered project: no fictional scenarios, no falsified data, and no unauthorized client names or logos.

Anonymization and authorization

Client-sensitive details are anonymized or given as ranges; anything involving client names or trademarks is disclosed only after explicit authorization.

Verifiability first

Our own Sinnet Cloud sample uses complete raw data and is directly verifiable; after improvements we disclose before-and-after data, including any parts that fell short.

Case FAQ

Three questions about the audit report and case data

Which dimensions does the AI information audit report cover?

Six dimensions, each scored 0–100: content authority, information structure, technical accessibility, entity knowledge building, authoritative source coverage, and cross-platform consistency. A weighted combination of the six produces the AI match score composite, with 60 as the pass mark.

Why is Sinnet Cloud's own AI match score only 42/100?

It is a real 2026 Q2 diagnosis using Sinnet Cloud itself as the sample, run to validate the diagnostic methodology and establish an improvement baseline. A score of 42/100 shows that even inside a company with compute and AI capability, brand visibility in AI has clear weak points. We publish it as a public case and will disclose the before-and-after data as we improve.

Can the case data be verified?

Yes. aileo strictly forbids fabricating data for use as client cases; all case data comes from real projects, client-sensitive information is anonymized or given as ranges, and disclosure happens only with client authorization.

Is your brand a 42 or an 82 in AI?

One AI information audit: 10–15 business days, six dimensions, 50–100 core questions, up to 5 mainstream platforms, and an actionable 90-day roadmap.

Book an AI information audit → See the full service offering