Free for First 20 Companies: Launching AI-Search Specialized Web Diagnosis (GEO Optimization) for Manufacturing & B2B Sites to 'Stay in the Running'

FinMark Edge Inc. (Shibuya-ku, Tokyo; CEO: Yurika Hiyama) announces the launch of 'Manufacturing & B2B Web Sales Power AI Diagnosis,' a generative AI-search specialized service that evaluates whether a company's website is in a state of being 'cited and remaining in the results' during generative AI searches. The service will be offered free of charge to the first 20 applicants starting June 17, 2026 (registration closes once capacity is reached).

This service conducts a simplified diagnosis of the applicant's homepage and competing websites, evaluating elements such as the homepage, presentation of technical strengths, product and application pages, and inquiry pathways from the perspective of whether they are likely to be 'cited in responses' when potential customers input conditions into generative AI. Diagnosis results will be provided as a PDF report.

Background: B2B Procurement Starting Point Shifts from 'Keyword Search' to 'Consulting Generative AI'

In B2B procurement, the widespread adoption of generative AI is significantly changing how potential customers search for, compare, and narrow down candidate companies.

For example, conditions that were previously expressible only as keyword lists—such as 'Please recommend a company with experience machining titanium medical device components with a tolerance of ±0.01mm or less'—can now be consulted directly as natural language prompts to generative AI. The AI reads multiple company websites and summarizes only those companies that meet the criteria, providing them with supporting URLs. This enables potential customers to quickly narrow down their options.

However, the strengths of SME manufacturers and B2B companies often lie in niche, specific conditions such as compatible materials, machining precision, applications, industry-specific expertise, small-lot capabilities, and short lead times. If these strengths are not linguistically expressed on the website in a way that generative AI can cite, they may not be picked up during AI summarization and comparison, potentially excluding the company from consideration altogether.

Internal Verification: Confirmed That 'Selected Pages' Differ Between Generative AI Search and Google Search

Prior to developing this service, our company conducted a comparative verification of 15 search scenarios simulating procurement situations in the metal processing industry (e.g., precision titanium cutting for medical devices, small-lot quick-turnaround stainless steel, hard-to-cut materials like Inconel and Hastelloy, VA/VE proposal capabilities). We compared keyword-based Google searches with natural-language prompt searches using generative AI, utilizing Claude Fable 5 (prior to its discontinuation on June 12, US time) and actual search operations. (Verification details here)

The results confirmed a clear difference: while keyword searches display a list of links dependent on titles and brand recognition, generative AI search reads the full text of web pages and cites only those where condition matching is explicitly confirmed in the text.

The pages cited by generative AI shared the following common characteristics:

- Explicit numerical values: Tolerances, lot sizes, and delivery times are written in the body text (HTML text) with specific numbers—e.g., 'tolerance ±0.01mm,' 'from 1 unit,' 'as fast as ◯ days'—instead of vague terms like 'high precision.'

- Use of proper nouns: Material codes such as SUS304 and Inconel 718, and industry names like 'medical devices' and 'semiconductor manufacturing equipment,' are used exactly as potential customers would input them.

- Question-and-answer structure: Directly answering potential customer inquiries in FAQ or Q&A format—e.g., 'Can you handle VA/VE proposals?' → 'Yes, we can.'

- Dedicated pages by material or application: Instead of a general homepage, separate pages are created for specific topics such as 'titanium machining,' 'hard-to-cut material processing,' and 'VA/VE proposal case studies,' following a 'one theme, one page' structure.

- Text-based presentation of evidence: Credibility indicators such as ISO certifications, medical device manufacturing license numbers, equipment lists, and track record counts are written in the body text rather than embedded in images or PDFs.

The diagnostic criteria and improvement suggestions for this service are designed based on these verified characteristics of pages cited by generative AI.

Overview of 'Manufacturing & B2B Web Sales Power AI Diagnosis'

The 'Manufacturing & B2B Web Sales Power AI Diagnosis' is a service where experts, using AI tools, diagnose existing websites of SME manufacturers and B2B companies specifically from the perspective of their readiness for generative AI search.

It goes beyond simple design checks or traditional SEO diagnostics, focusing instead on whether the company's capabilities—such as 'which applications, industries, or challenges they can address' and 'what differentiates them from competitors'—are communicated in a way that generative AI can cite when potential customers use tools like ChatGPT, Claude, or Gemini to compare candidate companies.

This service serves as an entry point to what is known as LLMO (Large Language Model Optimization) or GEO (Generative Engine Optimization).

Contents of 'Manufacturing & B2B Web Sales Power AI Diagnosis'

The main diagnostic items are as follows, all based on the 'textual characteristics of pages cited by generative AI' verified in our internal research:

- Homepage diagnosis: Evaluates and scores whether the company's identity, target audience, technical offerings, and types of consultations are immediately clear from text readable by generative AI.

- Language-based diagnosis of technical strengths: Assesses whether strengths such as compatible materials, machining precision, applications, industry-specific expertise, small-lot capabilities, and short lead times are expressed using 'AI-citable language' that includes numerical values, material codes, and industry names. We also provide examples of improved content.

- Product and application page diagnosis: Checks whether information is organized by product, application, or industry in a 'one theme, one page' format usable by generative AI for condition matching, and whether potential customers can easily navigate to information relevant to their challenges. We provide examples of improved structures.

- Simplified competitor site comparison: Compares designated competitor sites or peer company websites to analyze differences in how technical strengths, applications, and inquiry pathways are presented, from the perspective of which is more likely to be cited in generative AI responses.

- Inquiry pathway diagnosis: Evaluates whether the pathways guiding potential customers—arriving via generative AI—to take the next step (e.g., request a quote, technical consultation, or general inquiry) are clearly designed and easy to follow.

Target Companies

This service targets SME manufacturing and B2B companies facing the following challenges:

- Having a website but not receiving inquiries, quote requests, or technical consultations

- Not appearing in responses when asking generative AI tools like ChatGPT about their field, while competitors are frequently cited

- Struggling to effectively communicate their technical capabilities, processing strengths, and product advantages