Zhichuang XintuAI VISIBILITY

GEO SUCCESS STORIES

GEO customer
success stories

See how these companies worked with Zhichuang Xintu to organize their brand information, build knowledge assets, and earn both more visibility and more accurate recommendations in real AI search scenarios.

See the anonymized cases
7 anonymized projects
6 industry sectors

From equipment selection and materials sourcing to business services and expert Q&A, we build the GEO content system around the questions buyers actually ask.

ZCX GEO CASE LIBRARY

Zhichuang Xintu GEO
case library

Client names and sensitive business details have been removed from every case below. These are interim re-test results for each project, and describe that project only — under its own starting point, timeline and testing method.

CASE 01
Chemical materials
Chemical materials

A high-performance chemical materials company

From data sheets to material selection answers AI can use

The challenge

The company publishes a full range of material grades and performance data, but its brand came up rarely in material selection questions such as “how do I choose a heat-resistant material” or “what adhesive does a new-energy vehicle battery need”. There was also no clear link between its material systems, the industries it serves, its case work and its test reports.

Background

Sourcing chemical materials involves performance comparisons, formulation fit, environmental certification, supply stability and technical support. The company had the R&D and application experience, but its website offered buyers no systematic explanation to work from.

What Zhichuang Xintu did

  • Unify how the company, its material systems and its target industries are described
  • Map selection questions around grades, parameters, processing and certifications
  • Connect product pages, industry solutions, technical documentation and case work
  • Build a material selection knowledge base that AI can understand and quote
CASE 03
Laser equipment
Laser equipment

An industrial laser equipment manufacturer

AI search exposure up 286% in three months

The challenge

The company makes laser cutting, welding and marking equipment and already ranked for some product terms in traditional search. But when procurement questions were tested on DeepSeek, Doubao and Kimi, the brand was rarely mentioned in any meaningful way. The site leaned on model numbers and basic specifications, with nothing on materials, power, precision or how to choose by industry.

Background

Buying industrial equipment is not only a matter of comparing specifications. Buyers also need to understand the process, the material, the working environment and the service behind the machine. The existing content could not answer the run of questions a buyer works through, from identifying a need to choosing a machine.

What Zhichuang Xintu did

  • Set up 120 fixed AI test questions and record a baseline
  • Standardize how equipment categories, power, materials and scenarios are described
  • Publish selection guides, configuration comparisons and processing solutions
  • Re-test across platforms using the same question set
CASE 04
Education & training
Education & training

A vocational training provider

Reaching the front of AI recommendations for high-intent questions

The challenge

Competition in vocational training is intense, and the leading brands already had a head start in AI search. The school had mature courses and experienced teachers, but its public content did not give AI enough to see what made it different.

Background

People choosing a training provider compare course tracks, instructors, who the course suits, how long it takes and what career support comes with it. The existing content covered only part of that decision.

What Zhichuang Xintu did

  • Build a framework comparing competitors with what sets the school apart
  • Organize anonymized student transition stories and course evidence
  • Cover high-intent questions on career change and upskilling
  • Keep distributing and re-testing on the priority platforms
CASE 05
Equipment manufacturing
Equipment manufacturing

A new-energy equipment manufacturer

From product spec pages to answers AI can quote in procurement

The challenge

The company already published detailed product models and technical specifications, but its brand still came up rarely in AI procurement questions such as “how do I choose a new-energy equipment supplier” or “what equipment does this application need”. Descriptions of the company, its product lines and the industries it serves differed from page to page, and project cases were not clearly linked to the technical documentation.

Background

Buying new-energy equipment means comparing specifications, project conditions, lead times and technical support. The client had years of hands-on project experience, but the website was built around individual products, with no systematic explanation aimed at buying decisions — so AI had little to work with when judging which projects the company could take on.

What Zhichuang Xintu did

  • Unify how the company, its products and its target industries are described
  • Build a question matrix covering selection, specifications, operating conditions and delivery capability
  • Connect product pages, technical documentation, solutions and project cases
  • Turn all of it into a knowledge base built for equipment buying decisions
CASE 06
Business services
Business services

An enterprise CRM provider

Branded AI search exposure up 320%

The challenge

The company did reasonably well in traditional search, but when buyers asked DeepSeek or Doubao for “CRM system recommendations” or “how to choose a CRM for a small business”, the brand was almost invisible. Competitors at the top of the market owned that entry point.

Background

Choosing a CRM means weighing industry fit, implementation time, integrations, permission management and support. The old site was mostly feature descriptions, with little that AI could use to compare vendors or judge a scenario.

What Zhichuang Xintu did

  • Build a buying question library by industry and company size
  • Set out the boundaries of features, implementation, integrations and support
  • Add industry solutions and content that supports buyer decisions
  • Track how answers change across four priority AI platforms
CASE 07
Industrial manufacturing
Industrial manufacturing

An industrial adhesives manufacturer

As traditional SEO traffic fell, GEO opened a new source of demand

The challenge

The company makes industrial structural and sealant adhesives — a B2B category with long buying cycles and a heavy reliance on information. Traditional search traffic was slipping and the cost of winning new customers kept rising, so the business needed a new source of high-intent demand.

Background

Choosing an industrial adhesive depends on the materials, process, environment and application method involved. Turning that expertise into clear content is what lets AI recommend the brand accurately in a specific buying scenario.

What Zhichuang Xintu did

  • Cover 60+ industrial adhesive selection and supplier recommendation scenarios
  • Build a knowledge base on industrial adhesives
  • Add buying comparisons, process application guides and technical white papers
  • Develop in-depth content along the key-account decision path

ABOUT THE RESULTS

Every result starts from
a different business baseline

AI answers change with the model, the data and the date. Case figures are not a promise of results for another project; we build each plan from your current situation, the questions you want to win and the content you can genuinely support.

Get a GEO plan for your company