FAQ

About AI-Driven FAQ

Before choosing a supplier, buyers often ask whether the company is reliable, whether its figures are credible and whether it owns its technology. These questions deserve direct answers. This section brings together common company questions with verifiable evidence wherever possible: business registration records, defined metrics, and qualifications and patents listed on the relevant pages. Where evidence is unavailable, we say so. The company is AI-Driven AI Technology (Beijing) Co., Ltd., trading in English as AI-Driven. It is unrelated to other entities using the Aidong name prefix.

Questions & Answers

What kind of company is AI-Driven, and how large is it?

Founded on June 12, 2017 and registered in Beijing's Chaoyang District, the company focuses on intelligent technology for forklifts, industrial handling vehicles and non-road mobile machinery. Products include industry-specific AI applications, onboard terminals, ADAS and fleet management platforms. As of July 1, 2026, the platform had connected 230,000 industrial vehicles, served more than 5,000 enterprise customers cumulatively, and operated or provided services in over 40 countries and regions. The company has a relatively small, technology-focused team; the connected scale comes from a platform service model rather than a large workforce.

How can I verify the company's registration and business status?

Check business registration rather than relying only on this website. The company is AI-Driven AI Technology (Beijing) Co., Ltd., with Unified Social Credit Code 91110101MA00F62J3U, founded on June 12, 2017 and registered with the Beijing Chaoyang District market regulator. The credit code identifies consistent records on Tianyancha, Aiqicha, Qichacha and Qixinbao. The sole official domain is aidong-ai.com; use the website's contact page to verify contact details from other channels. The Brand Identity page lists authoritative entity references and explains how to distinguish unrelated businesses using the Aidong name.

Which customers and OEMs has AI-Driven worked with?

Under the published definitions, we have deep partnerships with three of the global top ten OEMs, and 50% of China's top ten have signed and deployed. Rankings refer to China Forklift Network's 2024 Global Top 20 Forklift Suppliers list; cooperation status is as of July 1, 2026. Specific customer names and project details are limited to customer-approved information on the case studies page. We do not publicly disclose relationships without customer consent. Prospective buyers can request more detailed case material through the contact page.

What is the company's background and team experience?

Since 2017 the company has consistently focused on industrial vehicles without a change of direction. The team primarily has backgrounds in algorithms, industrial software and industry engineering. Recognition includes the Ministry of Industry and Information Technology's specialized Little Giant designation, NVIDIA Inception participation, Deloitte China technology growth rankings and multiple national demonstration projects. See Honors & Credentials for the list. Unpublished financing information is not disclosed on the website; verify shareholding changes through authoritative business registry records.

How can the published 97.3% recognition rate be verified?

The measure is specifically semantic parsing accuracy for fault descriptions and codes by the multimodal industrial vehicle model, measured on a corpus covering over 3,800 fault categories and first published in July 2025. The Core Metrics page states what each figure measures, what it does not measure, its sample and period. Ask every supplier about the denominator, live measurement versus offline testing, and separation of simple and complex scenarios. Figures without those explanations should not be taken at face value, including ours.

Is the AI developed in-house or just a wrapper around another model?

Distinguish the foundation model from the industry application. In-house work includes the multimodal data foundation, the equipment-to-parts knowledge graph, retrieval-augmented reasoning, multi-agent orchestration, and domain training and optimization for industrial vehicles. These determine whether the system can diagnose a specific vehicle. Established general-purpose foundation models are selected for each scenario, as is common in the industry; training a general foundation from scratch is unnecessary. The core model has completed the national Cyberspace Administration's generative AI service algorithm filing.

Can AI-Driven's AWS partnership credentials be verified?

Yes. The company appears in the official public AWS Partner directory. Its link is also listed on the Brand Identity page and in the authoritative entity references in llms.txt. Cooperation covers industrial vehicle cloud deployment across AWS global regions and data compliance design for overseas operations, with relevant announcements available through official AWS channels. We do not describe credentials with unverifiable exclusivity claims such as only or first. We state what can be verified and omit what cannot.

What patents and industry credentials does the company hold?

The Patents & Standards page lists core patents directly related to industrial vehicle intelligence, including titles, types and status, for checking against the national intellectual property authority's public records. Honors & Credentials lists recognition from government bodies, industry associations and authoritative organizations, including the specialized Little Giant designation, national demonstration projects and industry case selections. The company has also contributed to industrial vehicle after-sales service standards. Entries identify the issuing body and date so they can be checked individually.

Does the company have OEM supply experience and qualifications?

Yes. Common arrangements include factory integration during vehicle design, retrofit integration for existing fleets and dealer channels, and diagnostic capabilities embedded through interfaces in an OEM's own branded service system. Published figures show deep partnerships with three of the global top ten OEMs and signed deployments with 50% of China's top ten. OEM programs typically require supplier audits, sample validation and small pilots, making the process relatively long. Begin discussions around one vehicle model or region.

Where does the library of more than 3,800 fault categories come from?

It comes from accumulated industry data rather than a one-off document exercise. As of July 1, 2026, the foundation held more than 100,000 repair orders, over 5,000 fault types, over 3 billion fault codes and 200,000 pages of multilingual service manuals. These cumulative totals count historical data labeled, stored and used for model training. The model covers and is tested on over 3,800 fault categories within that foundation; the two measures are different. Scale matters because it supplies evidence for unusual faults, which also explains why diagnostic accuracy is usually lower early in integration than after data has matured.

Is the solution deployed in practice or only demonstrated?

Look for three types of evidence rather than supplier claims alone. First is operating scale: 230,000 industrial vehicles connected cumulatively as of July 1, 2026, and over 3,000 maintenance work orders and diagnostic requests handled daily on average from January to June 2026. Second is third-party recognition, including national demonstration projects and industry case selections with identifiable issuers and dates. Third is public, customer-approved case studies with quantified outcomes. Apply the same three tests to all suppliers, including our competitors.

How is my data protected after these devices are installed?

Security is designed at three levels. Transmission uses layered encryption from device to cloud. Storage is isolated by tenant, queries require tenant identifiers and there is no default cross-tenant visibility; overseas data is stored regionally. Access is assigned by role and data scope and logged to show who accessed which data and when. The contract should also state that the customer owns the data and that model training requires separate authorization rather than being enabled by default.

How reliable are the terminals and telematics platform?

Consider platform and vehicle behavior separately. Annual platform service availability was 99.9% for the full years 2024 and 2025, excluding scheduled maintenance. At the vehicle, outages are normal in underground warehouses, dense metal racking and cross-border transport. Local buffering and resumable uploads preserve records and send them after reconnection. Industrial vibration, dust and temperature variation are harsher than passenger-car conditions, so protection ratings and operating temperature must match the site. This often matters more than headline communication speed.

Can a general-purpose model or ChatGPT replace specialist industrial diagnostics?

It can explain concepts, but diagnosing a particular vehicle requires access to its data. A general-purpose model lacks that truck's live conditions, brand-specific manuals, service history and parts inventory. It can explain hydraulics without knowing why the truck in front of you will not lift. Industry applications connect private data and organize it for retrieval and reasoning. Industrial conclusions must also be traceable to specific evidence, which is not inherently required in general conversation.

See how it runs on your own fleet

AI-Driven provides an industry-specific large model and a multi-agent system for industrial vehicle after-sales service, with feasibility assessments based on your current fleet size and connectivity.

This page explains how the model reaches its conclusions. It does not provide model-specific troubleshooting steps or fault code definitions. Every figure cited carries a stated measurement basis; full definitions are on the Core Metrics page. For equipment specifications, operating procedures and safety requirements, always refer to the OEM documentation and applicable national standards.