NASDAQ: ADVB

Our shift to becoming an AI financial verification company is driven by what we believe are the following key drivers of demand for AI financial verification: (1) the wide use of large language models and AI agents by financial institutions in credit analysis, investment research, risk assessment and compliance review; (2) the risk that AI output contains errors from fabricated or miscalculated figures, confused entities, incorrect dates for event sequences, logical contradictions, incorrect policy or regulation citations, and overly optimistic or pessimistic assessments which in turn can lead to poor rating inaccuracies, poor credit decisions, compliance and legal risk; (3) increasing regulatory focus on the explainability, accountability, and accuracy of AI used by regulated financial institutions; and (4) audit and internal-control requirements for documented review and audit trails of the analysis used in decisions and disclosures.

The market for AI verification remains at an early stage, and adoption will depend on financial institutions’ technology budgets, regulatory requirements, model performance, integration costs, data availability and users’ willingness to add automated verification to existing workflows. As such, since our transition from a business focused on biomedical operations to a technology company focused primarily on enterprise AI applications, we have shifted our focus to financial verification and other professional, regulated-use cases. Our AI technology operations currently include: (a) continued development and operation of FinTruth; (b) development of financial verification algorithms, financial rules and related data and knowledge infrastructure; (c) product and software engineering; (d) enterprise product testing, deployment and customer implementation; (e) discussions, demonstrations and commercial engagement with prospective financial-institution and technology customers; and (f) development of commercial models including enterprise subscriptions, usage-based application programming interface (“API”) fees and integrated solutions.

Following the disposition of our biomedical operations, we have maintained a lean, technology-focused operating structure, with personnel primarily engaged in AI research and algorithm engineering, product and software engineering, data and knowledge engineering, customer implementation and commercialization, supported by the finance, legal and corporate functions necessary for a Nasdaq-listed company. We currently do not own any data centers, and the SaaS version of FinTruth runs on rented infrastructure from multiple international public cloud providers. On-premises deployments of FinTruth run on the customer’s own infrastructure. We are subject to the Hong Kong Personal Data (Privacy) Ordinance. For on-premises deployments, customer data remains within the customer’s own environment and is subject to the customer’s security controls.

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Our objective is to develop recurring and usage-based enterprise revenue from our AI technology operations while maintaining an operating structure in which product development, hiring and commercial expansion are aligned with customer adoption, revenue generation and available financial resources. As such, we are actively engaging in discussions regarding product deployment, business models and pricing with prospective customers and strategic partners, including financial IT companies, MaaS and AI distribution platforms, and fintech subsidiaries of large financial institutions. Target customers include commercial banks, securities firms, insurance companies, asset managers, auditing and professional-services firms, and financial technology companies.

During the fiscal year ended June 30, 2026, we generated approximately $127,637 (HK$1,000,000) in revenue from our operations related to FinTruth. We expect to finance the continued operation and commercialization of FinTruth through a combination of operating revenues, proceeds from the disposition of Advanced Biomed Inc. (Taiwan), and future capital-raising activities.

Recent Developments

The ELOC Agreement and the Termination thereof. On June 6, 2025, we entered into a purchase agreement (the “ELOC Agreement”) with HELENA GLOBAL INVESTMENT OPPORTUNITIES I LTD. (the “Investor”), pursua nt to which we have the right to issue and sell to the Investor, from time to time as provided therein, and the Investor is obligated to purchase from us, up to Twenty-Five Million United States Dollars ($25,000,000) of our Common Stock, subject to terms and conditions set forth in the ELOC Agreement. On July 23, 2025, we filed with the SEC a registration statement on Form S-1 (File No. 333-288907) to register under the Securities Act an aggregate of up to 40,000,000 shares of our Common Stock, which represents the shares available for issuance under the ELOC Agreement (as defined below). The registration statement was never declared effective. On July 20, 2026, we filed with the SEC an application for the withdrawal of the registration statement pursuant to Rule 477 under the Securities Act, as we determined not to proceed with the offering registered thereunder. No securities were sold under the registration statement. On January 30, 2026, we issued 1,650,710 shares of Common Stock (82,536 shares as adjusted for the Reverse Stock Split) to the Investor as commitment fee shares under the ELOC Agreement, valued at $500,000. On July 14, 2026, we delivered to the Investor a notice of termination of the ELOC Agreement pursuant to Section 11.02(b) thereof, and the ELOC Agreement terminated effective July 21, 2026. No advances were made under the ELOC Agreement prior to its termination.

Disposition of Advanced Biomed HK and Shanghai Sglcell. On December 23, 2025, we entered into a spin-off agreement with Wei Ha Hui, an unrelated third party, pursuant to which we sold 100% of the equity interests of Advanced Biomed HK, which wholly owned Shanghai Sglcell, for a purchase price of US$23,000, determined based on a valuation report commissioned by us. All intellectual property owned by Advanced Biomed HK and Shanghai Sglcell was transferred to the buyer at closing. On January 26, 2026, we entered into a supplemental agreement with Advanced Biomed HK and Wei Ha Hui, pursuant to which Advanced Biomed HK acknowledged that it was indebted to us in the aggregate amount of $6,925,549 and irrevocably agreed to repay such amount in full on or before January 26, 2027.

Nasdaq Compliance. On July 18, 2025, we received a written notification from Nasdaq notifying us that we were not in compliance with the minimum bid price requirement set forth in Nasdaq rules for continued listing on the Nasdaq, and we were provided 180 calendar days, or until January 14, 2026, to regain compliance. On January 16, 2026, we were notified by Nasdaq that our securities would be scheduled for delisting from the Nasdaq Capital market at the opening of business on January 27, 2026 as our common stock had closed below the minimum $1.00 per share as required by Nasdaq Listing Rule 5550(a)(2), unless we requested an appeal of this determination by January 23, 2026 before a Hearings Panel (the “Panel”). Accordingly, on January 21, 2026, we appealed the Staff’s delisting determination by submitting a hearing request to the Panel, which stayed the suspension of our delisting. On January 12, 2026, holders of approximately 79.92% of our outstanding voting power approved, by written consent, a reverse stock split at a ratio of between 1-for-2 and 1-for-100, as determined by our board of directors. On January 30, 2026, our board of directors approved a ratio of 1-for-20, and we filed a Certificate of Amendment to our Articles of Incorporation with the Nevada Secretary of State on February 3, 2026. The Reverse Stock Split became effective on February 13, 2026, and our Common Stock began trading on a split-adjusted basis on the Nasdaq Capital Market at market open on February 20, 2026 under the new CUSIP number 00752P203. On March 12, 2026, we received a written decision from the Panel stating that the Company had regained compliance with Nasdaq Listing Rule 5550(a)(2) following the completion of the Reverse Stock Split on February 20, 2026.

Private Placement. On January 28, 2026, we entered into a securities purchase agreement with certain investors, pursuant to which we issued and sold an aggregate of 4,000,000 shares of Common Stock (200,000 shares as adjusted for the Reverse Stock Split) at a purchase price of $0.062 per share (pre-Reverse Stock Split), for aggregate gross proceeds of $248,000. The transaction closed on January 29, 2026. The shares were issued in reliance on the exemptions from registration provided by Section 4(a)(2) of the Securities Act and/or Regulation S promulgated thereunder.

Reverse Stock Split. On February 20, 2026, we effected a reverse stock split of the Company’s common stock, at a ratio of 1-for-20. The Company subsequently began trading on a post-split basis on the Nasdaq Capital Market at market open on February 20, 2026 under the new CUSIP number 00752P203.

Changes in Management. Effective March 25, 2026, Dr. Steven I-Fang Cheng resigned as our director and Chief Technology Officer, and Mingze Yin, our Chief Financial Officer, was appointed as a director. Effective April 28, 2026, Dr. Yi Lu ceased to serve as our Chief Executive Officer, director and Chairman of the board of directors pursuant to a termination agreement, and Dr. Xiaomin Chen was appointed as our Chief Executive Officer, director and Chairman of the board of directors.

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Acquisition of Acellent. On April 2, 2026, we entered into a share purchase agreement with Acellent and Xiaomin Chen, the sole shareholder of Acellent, pursuant to which we agreed to acquire 100% of the equity interests of Acellent in exchange for 270,000 shares of Common Stock, valued at an estimated $4.00 per share, or $1,080,000 in the aggregate. The acquisition closed on April 30, 2026, and the 270,000 shares were issued to Mr. Chen in reliance on the exemption from registration provided by Section 4(a)(2) of the Securities Act.

Loan Agreement. On April 13, 2026, we entered into a loan agreement with Jie Wang, pursuant to which Mr. Wang agreed to lend us US$600,000 for operating purposes. The loan is unsecured and bears an interest rate of 10% per annum and matures six months after disbursement, subject to extension for an additional six months by mutual written agreement.

Disposition of Advanced Biomed Taiwan. On June 30, 2026, we entered into a share purchase agreement with Miaozhu Zhao, an unrelated third party, pursuant to which we agreed to sell 100% of the equity interests of Advanced Biomed Inc. (Taiwan), which operates our A+PerfusC integrated perfusion 3D cell culture platform business, for an aggregate epurchase price of US$490,000.

Corporate History and Corporate Structure

We were incorporated in Nevada in July 2021 as a holding company. We started operations in 2014 through Advanced Biomed Taiwan as a research and development center for technology research and product development. In August 2021, we established Advanced Biomed HK as the wholly owned subsidiary of Advanced Biomed Taiwan to integrate market development and commercialization in the PRC. On January 1, 2022, Advanced Biomed HK acquired 100% equity interest of Shanghai Sglcell from its stockholders for a consideration of RMB12 million. In March 2022, Advanced Biomed HK established Sglcell (Huangshan) Biotech Co., Ltd.

In July 2022, we consummated a reorganization pursuant to which we acquired 100% equity interest of Advanced Biomed Taiwan, making it our wholly owned subsidiary. On November 7, 2022, we obtained the approval of Taiwan Investment Commission for the reorganization, the Issue No. of which is “經審一字第11100116890號”. Additionally, the Bureau of Economic Development of Tainan City Government has also approved the reorganization in accordance with the Taiwan Company Act on December 26, 2022.

On June 8, 2023, pursuant to the Share Transfer Agreement entered into by Ting Wang and Haifeng Zhang, who are independent third-party individuals, and Shanghai Sglcell on June 2, 2023, Shanghai Sglcell transferred its wholly owned subsidiary, Nanjing Yitian Biotech Co., Ltd. and its subsidiary, Beijing Yitan Jiarui Technology Co., Ltd., to Ting Wang and Haifeng Zhang at aggregate consideration of RMB500,000 (approximately US$72,780) without any other obligations arising from the transfers.

On June 9, 2023, pursuant to the Share Transfer Agreement entered into by Ting Wang and Haifeng Zhang, who are independent third-party individuals, and Shanghai Sglcell on May 31, 2023, Shanghai Sglcell transferred its wholly owned subsidiary, Shandong Sglcell Medical Devices Co., Ltd., to Ting Wang and Haifeng Zhang at zero consideration without any other obligations arising from the transfers.

On June 15, 2023, pursuant to the Share Transfer Agreement entered into by Quantum Capital (Hong Kong) Limited, who is an independent third-party corporation, and Advanced Biomed HK on June 9, 2023, Advanced Biomed HK transferred its wholly owned subsidiary, Sglcell (Huangshan) Biotech Co., Ltd., to Quantum Capital (Hong Kong) Limited at zero consideration without any other obligations arising from the transfers.

On December 23, 2025, we entered into agreement to sell Advanced Biomed HK (together with its subsidiary, Shanghai Sglcell) to Wei Ha Hui, an unrelated third party, for US$23,000.

On April 2, 2026, we acquired 100% of equity interests of Acellent Technologies (Hong Kong) Co. Limited (“Acellent”), who develops and operates the FinTruth Financial Verification Agent Platform (“FinTruth”).

On June 30, 2026, we entered into an agreement to sell Advanced Biomed Taiwan to Miaozhu Zhao, an unrelated third party, for US$490,000. As a result, we have exited our biomedical operations and have since fully shifted our focus towards developing AI application through FinTruth.

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The chart below depicts the corporate structure of the Company as of the date of this Report.

The SEC maintains a website at www.sec.gov that contains reports, proxy and information statements, and other information regarding issuers that file electronically with the SEC using its EDGAR system.

About FinTruth

FinTruth is our primary AI platform and the sole product offered to our customers. The product operates as a rule-based and model-based check platform, with research and development and engineering activities conducted by our employees, who are based primarily in Hong Kong. Its reasoning core, Smith RM, is a proprietary financial reasoning model developed and owned by Acellent. Smith RM is owned by Acellent and is not subject to any third-party license restrictions.

The platform is model-agnostic, meaning that, at a customer’s request, it can be configured to work with third-party or open-source large language models specified by the customer. FinTruth can also verify the output of third-party AI financial reasoning systems. Our AI research and algorithm engineering team is responsible for Smith RM, the verification algorithms, model evaluation, hallucination detection, AI safety mechanisms and related model-improvement work. Meanwhile, implementation, configuration, proof-of-concept support, system integration, testing and technical support are handled by our client solutions and commercialization function, which is supported by our product and software engineering team. The platform currently supports verification and user interface in English and Chinese. It does not support cross-language verification. In other words, FinTruth only verifies English-language reports against English-language source documents, and Chinese-language reports against Chinese-language source documents.

The platform is currently delivered in three ways: (1) a browser-based SaaS workspace hosted on international public cloud infrastructure provided by multiple cloud providers; (2) APIs, including a step-by-step verification API that allows verification checkpoints to be embedded in a customer’s or partner’s AI financial-analysis workflow; and (3) on-premises deployment within the customer’s own environment. Our commercial models also include integrated or bundled solutions offered through foundation-model providers, enterprise technology providers, financial IT platforms, and strategic technology partners.

The platform currently supports, in production, credit and credit-rating analysis reports, research reports, and financial statements and annual reports. Source materials include financial statements, credit reports, business registration information, judicial information and industry data.

Notably, FinTruth is designed to operate as a verification and audit layer, not as a replacement for the underlying AI model or for the professional judgment of analysts, auditors and compliance personnel. It consists of five principal components: (i) data ingestion layer – ingests corporate credit information, financial statements, business registration information, judicial information and industry data, as well as the AI-generated report and source documents uploaded by the user; (ii) Smith RM reasoning core– a proprietary financial reasoning model developed by Acellent, which provides step-by-step verification through an API and includes a self-repair mechanism, so that issues identified during verification can be fed back for correction; (iii) formal verification engine (“FV Engine”) consisting of four verification modules: numerical, logical, ontology and narrative consistency verifiers; (iv) financial rules knowledge base consisting of accounting standards, banking regulations and industry benchmarks; and (v) output layer – report rendering, format conversion and audit logs. Our knowledge base covers PRC Accounting Standards for Business Enterprises, International Financial Reporting Standards (IFRS) and Hong Kong Financial Reporting Standards (HKFRS), and U.S. GAAP.

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FinTruth is supported by our technology and data assets, which include proprietary financial datasets accumulated over approximately three years; internal financial rules and knowledge structures (including a financial ontology and a rules knowledge base of accounting standards covering PRC, IFRS/HKFRS and U.S. GAAP standards, banking regulations and industry benchmarks); and proprietary training and evaluation datasets developed through expert annotation, human feedback and product-use feedback. Our datasets are derived from data generated internally by Acellent. Because these datasets are generated internally, they are not purchased or licensed from third parties. As such, these datasets are owned by Acellent, which has the right to use them commercially and for model training. We believe these data are crucial to FinTruth’s functioning, as they support FinTruth’s verification, reasoning, evidence-tracing and model-evaluation capabilities. Our data pipelines, datasets, ontology construction, evidence-chain processing, knowledge-base maintenance and data-quality control are managed by our data and knowledge engineering function.

FinTruth currently provides three principal capabilities: (i) document and source comparison, allowing AI-generated financial analysis to be checked against underlying source documents; (ii) source-level evidence tracing, allowing statements and numerical values to be traced to supporting evidence; and (iii) verification and audit reporting, including consistency checks, confidence indicators and audit trails. In document and source comparison, FinTruth provides a side-by-side comparison of the AI-generated report and source files, with automatic highlighting of differences to identify data deviations and factual conflicts. In the detailed analysis view, a claim list, the AI report and the source materials are linked in three panels. In source-level evidence tracing, FinTruth traces claims and numbers back to their specific location in the source files. By selecting a claim, the reviewer is taken to the corresponding paragraph in the source materials, allowing the reviewer to check the evidence supporting each conclusion. Lastly, in verification and audit reporting, FinTruth produces a report overview with an overall pass score, counts of passed, suspect and erroneous claims, chapter-level quality scores, review suggestions and operating metrics (processing time, token usage and retry counts). Audit reports contain conclusions and evidence and can be exported in PDF and Word formats.

In general, the platform uses numerical, logical, ontology and narrative verification functions to identify inconsistencies in financial data, relationships, reasoning and narrative content. Through FinTruth’s task creation interface, users can upload an AI-generated report and the corresponding source documents for review against these capabilities. Following the continued development, refinement and commercialization of FinTruth, we plan to deploy the AI application to financial institutions, financial technology companies and other enterprise customers.

FinTruth supports two modes of use: (1) standalone verification of a completed report and (2) embedded verification checkpoints. Under standalone verification, the user uploads the AI-generated report and the corresponding source documents. The platform then extracts the claims in the report, traces each claim to the source documents, runs the verification modules and produces a report overview and a detailed claim-by-claim analysis, which can be exported as an audit report and kept in “History.” Under embedded verification checkpoints, FinTruth is integrated into an AI-financial analysis workflow, and verification is performed at each stage—data collection, fundamental analysis, risk identification, rating recommendation and report generation. Under both modes, FinTruth provides a verification report with confidence scores and an audit trail.

Additionally, FinTruth provides confidence indicators to our users. In verification and audit reporting, confidence is presented at three levels:

1. Claim level - each claim is classified as “pass,” “suspect” or “error.” A claim is classified as “error” when it contradicts the source documents or violates a hard constraint (e.g., an accounting identity that does not hold). It is classified as “suspect” when the source documents do not contain sufficient evidence to support it, but no contradiction is found. Soft-constraint checks (e.g., industry benchmark alignment, narrative–sentiment consistency) produce confidence scores where deviations are tolerated.

2. Report level (“detection pass rate”) – a score is given out of 100, calculated as the number of passed claims divided by the total number of claims checked. Suspect and erroneous claims are not counted as passed.

3. Chapter level – each chapter receives a separate score across dimensions such as content completeness and data accuracy. Low-scoring chapters and identified numerical-extraction and calculation errors generate review suggestions.

The classifications and scores are tested periodically against our proprietary evaluation datasets, which are annotated by our internal staff and external financial experts. We also provide opportunities for customer feedback on production use to identify and correct any possible misclassifications. However, automated verification may produce false positives or false negatives, and the confidence indicators do not guarantee the accuracy of any report or claim.

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We plan to generate revenue through multiple channels: (1) usage-based API fees, including token- or transaction-based metering; (2) enterprise subscription revenue through annual or periodic arrangements; and (3) integrated or bundled solutions offered through collaboration with foundation-model providers.

1. Usage-based fees: charged to financial institutions, financial IT companies and other enterprise customers on a per-report or per-task basis, for both web and API use

2. Enterprise subscriptions and on-premises licenses: recurring annual or periodic subscription fees; for on-premises deployments, a one-time license fee plus an annual maintenance fee

3. Integrated or bundled solutions: offered in collaboration with foundation-model providers, enterprise technology providers and financial IT platforms, with pricing negotiated with each partner

FinTruth Interface

A typical standalone verification task proceeds as follows:

Step 1: Upload – The user creates a new task, uploads the AI-generated report and the source documents, names the task and clicks “Start.”

Step 2: Ingestion – The files are received in the data ingestion layer and a task record is created. For on-premises deployments, documents remain within the customer’s own environment.

Step 3: Parsing – Text, tables and numerical values are extracted from the report and source documents, with page and paragraph locations kept for evidence tracing.

Step 4: Claim extraction – Statements and numerical values in the AI-generated report are identified as individual claims.

Step 5: Matching – Each claim is linked to the relevant location in the source documents to form an evidence chain.

Step 6: Verification – The FV Engine applies the numerical, logical, ontology and narrative verifiers under the three-layer framework (hard constraints, logical consistency, soft constraints), using the financial rules knowledge base. Each claim is classified as pass, suspect or error and assigned to an issue type (e.g., numerical conflict, factual inconsistency, unsupported citation).

Step 7: Report Output – The platform produces the report overview (detection pass rate, chapter scores, review suggestions, operating metrics) and the three-panel detailed analysis, and the user can export an audit report containing conclusions and evidence in PDF or Word format. A typical report is processed in a few minutes.

Below are sample screenshots from our platform:

Figure 1: FinTruth intelligent report generation platform user task creation interface. Through this initial interface, users are prompted to upload an AI-generated report and the corresponding source documents for review.

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Figure 2: FinTruth History Interface

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Sales and Marketing

We plan to engage directly with prospective financial-institution and technology customers and product demonstrations through pilot or proof-of-concept activities. Meanwhile, we plan to distribute our product indirectly through financial IT companies, MaaS, AI distribution platforms, foundation-model providers and other strategic technology partners through integrated or bundled offerings. As part of our indirect approach, we recently entered into an agreement with Quantum Capital (Hong Kong) Ltd as our marketing partner to promote and market FinTruth to prospective customers. Based on future customer demand and available financial resources, we intend to expand distribution through additional strategic technology partners and by adding business-development personnel.

Competition

FinTruth’s competitors include large financial technology companies, AI model providers, enterprise software companies, and financial institutions developing internal verification systems. We believe our competitive strengths include: (i) FinTruth’s design as an independent verification layer that evaluates AI output against source evidence, financial constraints and domain-specific rules, rather than relying only on improvements in the underlying generative model; (ii) Smith RM, our proprietary financial reasoning model, and the platform’s ability to work with models specified by customers; (iii) the combination of four verification modules (numerical, logical, ontology and narrative) in a three-layer framework, with a financial rules knowledge base covering PRC, IFRS/HKFRS and U.S. GAAP accounting standards; (iv) claim-level, source-level evidence tracing with a three-panel linked review interface and exportable audit trails; (v) flexible deployment through SaaS, API or on-premises, so that customer data can stay within the customer’s environment; and (vi) our proprietary financial datasets and expert-annotated evaluation datasets.

However, we are at an early commercial stage, with a limited operating history, a relatively small customer base, a small team compared with large technology and financial-data companies, limited cash resources and limited brand recognition among large financial institutions. AI model providers or financial institutions may also build similar verification capabilities into their own products. In addition, we do not currently hold ISO/IEC 27001, SOC 2 or other security certifications or attestations, which some large financial institutions require of their vendors.

Growth Strategy

We have adopted a twelve-month business and marketing plan running from July 1, 2026 to June 30, 2027. Our objectives include plans to commercially deploy FinTruth to twenty institutional customers; conversion of ten pilot engagements into commercial contracts; growth from twelve to approximately eighteen full-time employees, and maintaining sufficient liquidity to fund planned operations. There can be no assurance that we will achieve these objectives within the anticipated timeframe, or at all. Specifically, our twelve-month plan consists of three stages:

Stage I – Product Enhancement and Commercial Readiness (months 0-3; completed): We enhanced document comparison, source-level evidence tracing, numerical and logical consistency verification, ontology and entity verification and audit-report generation; improved enterprise deployment, including on-premises deployment; refined our financial datasets and rules; established our pricing models (per-report usage fees, subscriptions, and on-premises license and maintenance fees); and held demonstrations and started pilot and proof-of-concept engagements with prospective customers.

Stage II – Initial Commercial Deployment and Recurring Revenue (months 3-6; in progress): FinTruth is set to be in commercial use by paying customers, including commercial banks and financial IT and fintech companies. Between five and ten pilot or proof-of-concept engagements are underway or under discussion, and we are working to convert them into paying customers and recurring revenue.

Stage III – Commercial Scaling and Product Expansion (months 6-12; not yet started): this stage is set to begin in approximately January 2027.

As part of our twelve-month plan for developing and promoting FinTruth, we intend to possibly extend FinTruth’s functionality with additional verification modules, including additional financial-report verification functions and increased compatibility with additional financial documents and AI-output verification applications. We also intend to improve our automated customer onboarding experience and integration capabilities. We are currently in discussions regarding the potential acquisition of a legal AI agent designed to assist with the preparation, review and analysis of disclosure documents relating to U.S. capital markets transactions (due diligence, document analysis, disclosure drafting, document categorization and information collection), which could potentially expand FinTruth’s customer base. Its target users include law firms, public companies, investment banks, financial advisors, accounting firms and other participants in U.S. securities offerings and capital markets transactions. However, we have not yet entered into any binding or definitive agreement with respect to this potential acquisition, and there is no assurance that any transaction will be completed. We believe that the combination of an established operating platform, advanced technology and data assets, a dedicated technical support team, a defined commercial pipeline, and potential access to capital markets as a public company provides a strong foundation for the continued growth and commercialization of our AI-driven technology business.

Intellectual Property

We own 1 registered domain name in the United States.

Item Domain Name

1 advb.ai

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Employees

As of the date of this Report, we had 12 employees. The following table sets forth the number of our employees by function as of the date of this Report.

Number % of Total

Employees

Functions:

Executive & Product Leadership 2 17 %

AI Research & Algorithm Engineering 3 25 %

Product & Software Engineering 2 17 %

Data & Knowledge Engineering 1 8 %

Client Solutions & Commercialization 1 8 %

Finance & Accounting 2 17 %

Legal & Corporate Operations 1 8 %

Total number of employees 12 100.0 %

Licenses and Approvals

The following table sets forth licenses and approvals that our subsidiaries are required to obtain for our operations in Hong Kong as of the date of this Report:

Name Licenses and

Approvals PRC and Taiwan Regulatory

Authority Expiration Date

Acellent Technologies Certificate of Incorporation Registrar of Companies, Hong Kong Special Administrative Region indefinite

Acellent Technologies Business Registration Certificate Registrar of Companies, Hong Kong Special Administrative Region July 8, 2027

Insurance

We and our subsidiaries do not currently maintain any commercial insurance. As such, we are susceptible to losses including property damage, accidents, or liabilities. In the event that such damages are substantial, we may experience materially negative impact on our business operations, financial condition, and results of operations.

Government Regulation

This section summarizes the principal Hong Kong and PRC laws, rules and regulations related to our business and operations.

Regulations in Hong Kong

Advanced Biomed Inc. is a Nevada corporation with its operating business located in Hong Kong. As such, the patent holding company, Advanced Biomed Inc. is subject to the laws and regulations of the United States of America while our operating business is subject to the laws and regulations of Hong Kong, as applicable, including labor, occupational safety and health, contracts, tort and intellectual property laws. Furthermore, because our server is located in Hong Kong, we need to comply with the rules and regulations of Hong Kong governing the data usage and regular terms of service applicable to our potential customers or clients. As the information of our potential customers or clients is preserved in Hong Kong, we need to comply with the Hong Kong Personal Data (Privacy) Ordinance, which regulates the collection, holding, processing and use of personal data by “data users.” A data user is any person who, alone or jointly with others, controls the collection, holding, processing or use of personal data. “Personal data” means a data relating directly or indirectly to a living individual from which it is practicable to ascertain the individual’s identity and which is in a form in which access to or processing of the data is practicable.

If PRC authorities reinterpret PRC laws to apply to Hong Kong companies, we may become subject to the laws and regulations of China governing businesses in general, including labor, occupational safety and health, contracts, torts and intellectual property. We may also become subject to foreign exchange regulations that might limit our ability to convert foreign currency into RMB, acquire any other PRC companies, establish variable interest entities in the PRC, or make dividend payments from any future wholly foreign owned entities to us.

Privacy and Protection of User Data

We and our subsidiaries are subject to a number of laws, rules, directives, and regulations relating to the collection, use, retention, security, processing, and transfer of personally identifiable information about our customers and employees in the countries where we operate. Our business will involve the processing of personal data in many jurisdictions and the movement of data across national borders. As a result, much of the personal data that we process, which may include certain financial information associated with individuals, is regulated by multiple privacy and data protection laws and, in some cases, the privacy and data protection laws of multiple jurisdictions. In many cases, these laws apply not only to third-party transactions, but also to transfers of information between or among us, our subsidiaries, and other parties with which we have commercial relationships.

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Employment Ordinance

The Employment Ordinance is the main piece of legislation governing conditions of employment in Hong Kong since 1968. It covers a comprehensive range of employment protection and benefits for employees, including Wage Protection, Rest Days, Holidays with Pay, Paid Annual Leave, Sickness Allowance, Maternity Protection, Statutory Paternity Leave, Severance Payment, Long Service Payment, Employment Protection, Termination of Employment Contract, Protection Against Anti-Union Discrimination. In addition, every employer must take out employees’ compensation insurance to protect the claims made by employees in respect of accidents occurred during the course of their employment.

An employer must also comply with all legal obligations under the Mandatory Provident Fund Schemes Ordinance, (CAP485). These include enrolling all qualifying employees in MPF schemes and making MPF contributions for them. Except for exempt persons, employer should enroll both full-time and part-time employees who are at least 18 but under 65 years of age in an MPF scheme within the first 60 days of employment. The 60-dayemployment rule does not apply to casual employees in the construction and catering industries. Pursuant to the said Ordinance, we are required to make MPF contributions for our Hong Kong employees once every contribution period(generally the wage period within 1 month). Employers and employees are each required to make regular mandatory contributions of 5% of the employee’s relevant income to an MPF scheme, subject to the minimum and maximum relevant income levels. For a monthly-paid employee, the minimum and maximum relevant income levels are HKD$7,100 and HKD$30,000 respectively.

Regulations Relating to National Security

Law of the People’s Republic of China on Safeguarding National Security in the Hong Kong Special Administrative Region. On June 30, 2020, the Standing Committee of the National People’s Congress of the PRC adopted the Law of the People’s Republic of China on Safeguarding National Security in the Hong Kong Special Administrative Region (the “Hong Kong National Security Law”). The law was added to Annex III of the Basic Law of the Hong Kong Special Administrative Region (the “Basic Law”), was promulgated for application in Hong Kong, and took effect on the same day. The Hong Kong National Security Law establishes four categories of offenses: secession, subversion of state power, terrorist activities, and collusion with a foreign country or with external elements to endanger national security. The most serious offenses are punishable by up to life imprisonment. Corporate entities convicted of an offense may be fined, and their operations may be suspended or their licenses or business permits revoked. The law also has extraterritorial reach, as it applies to offenses committed from outside Hong Kong by persons who are not permanent residents of Hong Kong. It established the Committee for Safeguarding National Security of the Hong Kong Special Administrative Region and the Office for Safeguarding National Security of the Central People’s Government in Hong Kong, and provides that, in specified circumstances, the Office may exercise jurisdiction over a case. In addition, the Implementation Rules for Article 43 of the Hong Kong National Security Law, which took effect on July 7, 2020, give the Hong Kong Police Force investigative powers relating to national security offenses. These include the power to search premises and electronic devices, to require service providers to remove electronic messages that are likely to constitute a national security offense, to restrict access to platforms or services, and to require service providers and other persons to provide information and assistance. Service providers that fail to comply with these requirements may be subject to fines and imprisonment.

Safeguarding National Security Ordinance. Article 23 of the Basic Law requires Hong Kong to enact its own laws to prohibit treason, secession, sedition, subversion against the Central People’s Government, and theft of state secrets, among other acts. To implement Article 23, the Legislative Council of Hong Kong passed the Safeguarding National Security Ordinance (the “SNSO”) on March 19, 2024, and the SNSO took effect on March 23, 2024. The SNSO complements the Hong Kong National Security Law and covers the following categories of offenses: treason; insurrection; offenses relating to state secrets and espionage; sabotage endangering national security; external interference; and offenses involving seditious intention.

Regulations in the PRC

Regulations Relating to Artificial Intelligence

The PRC has adopted a series of regulations that govern the provision of algorithm-based, deep synthesis and generative artificial intelligence (“AI”) services to the public within the PRC. These regulations are administered principally by the Cyberspace Administration of China (the “CAC”), together with other authorities. They supplement the general framework established by the PRC Cybersecurity Law, the PRC Data Security Law and the PRC Personal Information Protection Law. The principal AI-specific regulations are summarized below.

Provisions on the Administration of Algorithm Recommendation for Internet Information Services. On December 31, 2021, the CAC, the Ministry of Industry and Information Technology (the “MIIT”), the Ministry of Public Security (the “MPS”) and the State Administration for Market Regulation jointly issued the Provisions on the Administration of Algorithm Recommendation for Internet Information Services (the “Algorithm Recommendation Provisions”), which became effective on March 1, 2022. The Algorithm Recommendation Provisions apply to the use of algorithm recommendation technologies to provide internet information services within the PRC. These technologies include generation and synthesis, personalized push, sorting and selection, search and filtering, and dispatching and decision-making algorithms. Service providers must, among other things, assume primary responsibility for algorithm security; establish management systems for algorithm review, data security, personal information protection and emergency response; regularly review and evaluate their algorithm mechanisms, models, data and outcomes; and refrain from using algorithms to generate false information or to manipulate rankings or search results. Providers must also disclose the basic principles, purposes and main operating mechanisms of their algorithm recommendation services and allow users to turn off personalized recommendations. Providers of services with “public opinion attributes or social mobilization capabilities” must file their algorithms with the CAC through its algorithm filing system within ten working days after they begin providing services, and must conduct security assessments in accordance with relevant regulations. Violations may result in warnings, orders to rectify, suspension of information updates and fines.

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Provisions on the Administration of Deep Synthesis of Internet Information Services. On November 25, 2022, the CAC, the MIIT and the MPS jointly issued the Provisions on the Administration of Deep Synthesis of Internet Information Services (the “Deep Synthesis Provisions”), which became effective on January 10, 2023. “Deep synthesis technology” refers to technologies that use deep learning, virtual reality and other generative synthesis algorithms to produce text, images, audio, video, virtual scenes and other network information. The Deep Synthesis Provisions impose obligations on deep synthesis service providers, technical supporters and users. Service providers must, among other things, verify users’ real identities; establish systems for content review, data security, personal information protection and the prevention of fraud; strengthen the management of training data and ensure its security; and review and evaluate their generation and synthesis models and algorithms. Where a service provides editing functions for biometric information such as faces or voices, providers must notify users and obtain consent from the individuals whose information is edited. Content generated or edited using deep synthesis services must be labeled in a manner that does not affect use, and content that may cause public confusion or misidentification must carry a prominent label. Deep synthesis service providers and technical supporters are also subject to the algorithm filing requirements of the Algorithm Recommendation Provisions.

Interim Measures for the Management of Generative Artificial Intelligence Services. On July 10, 2023, the CAC, together with the National Development and Reform Commission, the Ministry of Education, the Ministry of Science and Technology, the MIIT, the MPS and the National Radio and Television Administration (the “NRTA”), jointly issued the Interim Measures for the Management of Generative Artificial Intelligence Services (the “Generative AI Measures”), which became effective on August 15, 2023. The Generative AI Measures apply to the use of generative AI technologies to provide services that generate text, images, audio, video and other content to the public within the PRC. They do not apply to industry organizations, enterprises, academic and research institutions or public cultural institutions that research, develop and apply generative AI technologies without providing generative AI services to the public within the PRC. Under the Generative AI Measures, providers must, among other things, use training data and foundation models from lawful sources; refrain from infringing the intellectual property rights of others; obtain consent or have another lawful basis for processing personal information; and take effective measures to improve the quality of training data, including its truthfulness, accuracy, objectivity and diversity. Providers bear responsibility as producers of online information content, must enter into service agreements with users, must label generated content in accordance with the Deep Synthesis Provisions, and must establish mechanisms to handle complaints and to promptly address illegal content. Providers of generative AI services with public opinion attributes or social mobilization capabilities must conduct security assessments and complete algorithm filings. Where generative AI services provided from outside the PRC to the public within the PRC do not comply with PRC laws and regulations, the CAC may notify the relevant authorities to take technical and other necessary measures. Violations are punishable under the PRC Cybersecurity Law, the PRC Data Security Law, the PRC Personal Information Protection Law and other applicable laws.

Measures for Labeling Artificial Intelligence-Generated Synthetic Content. In March 2025, the CAC, the MIIT, the MPS and the NRTA jointly issued the Measures for Labeling Artificial Intelligence-Generated Synthetic Content (the “Labeling Measures”), which became effective on September 1, 2025. A related mandatory national standard, Cybersecurity Technology – Labeling Method for Content Generated by Artificial Intelligence (GB 45438-2025), took effect on the same date. The Labeling Measures require providers of internet information services that generate or synthesize text, images, audio, video, virtual scenes and other content to add explicit labels to such content where required. Explicit labels are labels, such as text, audio or graphics, that users can readily perceive. Providers must also add implicit labels, such as metadata identifying the content as AI-generated and identifying the provider, to the files of generated content. Online content distribution platforms must take measures to detect and verify such labels and to alert the public to content that is or may be AI-generated. Providers may not delete, alter, falsify or conceal required labels. Providers must also specify labeling requirements in their user service agreements and must provide labeling-related materials when completing algorithm filings or security assessments.

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