I. For AI-Powered Factory Search, the Core Question Is Not "How Good Is the AI?"
Over the past two years, every industry has been integrating AI into search. B2B procurement and sales are no exception — some have put AI in front of corporate information databases; others have embedded it into trading platform product catalogs.
But factory search has one specific problem that model capability alone cannot fix: if the underlying database cannot tell whether a given company is actually manufacturing the relevant product category, then however fluent the AI's answers are, they are just well-phrased wrong answers built on wrong data.
This is not an AI problem. It is a data problem.
The starting point for Tianxia Gongchang AI was to solve the data problem first.
II. "Corporate Information" and "Factory Facts" Are Not the Same Thing
The most widely used business lookup tools in China — platforms like Qichacha and Tianyancha — are fundamentally commercial registration databases. They can tell you a company's registered capital, legal representative, ownership structure, and litigation history. These are administrative records for enterprises, and they have their uses. But they cannot answer a question that matters enormously to factory procurement and sales: is this company actually in production? What are they making?
A company with "mechanical equipment manufacturing" in its registered business scope might have been shut down for five years, or might be a shell entity set up for transfer purposes. The commercial registration system does not distinguish between these cases.
Platforms like 1688 take a different approach. They do have suppliers and product catalogs, but the platform does not impose a "must be a factory" threshold on seller identity. A large proportion of active vendors are trading companies, wholesale market stalls, and intermediaries — they sell "direct from factory" shipments without manufacturing anything themselves. For buyers or upstream sales teams that need to reach the actual production layer, this creates a level of information noise that is very difficult to eliminate.
Tianxia Gongchang has taken a different path from the beginning. Identifying real, actively operating factories has been the central objective from the day we started building the database — not an afterthought applied to an existing corporate information source.
III. 4.8 Million Factories — Not Scraped from Registration Data
Tianxia Gongchang's factory database currently covers 4.8 million real, actively operating factories. Behind that number is a continuously running identification process.
The core logic is that for a company to enter this database, it must show multi-dimensional "active production signals" — documented product category activity, verifiable traces of manufacturing operations, and business data that continues to be updated — not just a registration filing. A company that registered and then shut down is not among these 4.8 million. A small or medium-sized factory that is actively producing but has a broadly written business registration is included as long as there is operational evidence to support it.
This means the density and precision of this database are fundamentally different from corporate information platforms. It is not a subset of "all registered Chinese manufacturing companies." It is a continuously validated, dynamically updated snapshot of factories that are actually running.
On this base, Tianxia Gongchang AI can legitimately answer: "where are automotive interior component factories mainly located," "which factories have both injection molding and spray-painting capability," "which factories in Guangdong do ODM work" — these questions require factory facts, not commercial registration records.
Try Tianxia Gongchang AI — search by conversation across 4.8 million real factories
IV. Data Quality and AI Capability Must Both Be There
When AI is discussed in the context of search, the conversation usually focuses on model-level capabilities: can it understand ambiguous descriptions, can it convert natural language into effective retrieval intent, can it manage multi-turn clarification.
Tianxia Gongchang AI continues to develop all of these capabilities. Users can start a conversation with an incomplete description, and the system will actively clarify the key parameters — product category, capacity requirements, geographic preference, certification needs — rather than immediately surfacing a list of unrelated results. For ambiguous product terms, the AI confirms what the user is actually looking for before retrieving, avoiding the situation where "you searched for a long time and everything that came back was the wrong kind of factory."
But there is a point we want to make clearly: model capability and data quality must both be present. Neither one is sufficient on its own.
If the underlying layer is a corporate information database, then however well the AI clarifies needs, what it ultimately returns is commercial registration data. If the underlying layer is a trading platform catalog, then however intelligent the AI is, it cannot help you distinguish which vendor is a real factory and which is a reseller. Tianxia Gongchang AI made a deliberate product decision: establish the data foundation first, then build dialogue and verification capability on top of it — not "launch with the AI layer and improve the data over time."
V. Live Verification: What the AI Says Is Not Only from a Static Database
There is a practical problem with factory information: it changes. A factory adds a production line and begins taking orders for a product category it did not handle before. A factory that was highly active reduces capacity. Static databases are limited in their timeliness by design.
During the conversation process, Tianxia Gongchang AI cross-references live information against the database rather than reading a static snapshot and outputting conclusions from that alone. This is not about making answers look more comprehensive. It is about making information more credible — especially when users are making real procurement decisions or planning sales outreach, where accuracy matters more than volume.
Live information does not replace the database. It operates as a verification and supplementary layer. The 4.8-million-factory database provides the factual foundation. The live layer handles validation and freshness signals. The AI's conversational layer translates all of this into answers that users actually need.
VI. A Note on Timing
Tianxia Gongchang AI is not the first AI product in factory search and will not be the last. We chose to release at this point because we believe the underlying data has reached sufficient density to support a genuinely usable AI factory search experience — rather than shipping an AI interface and waiting for the data to catch up.
4.8 million real operating factories is a basic commitment we make to users: when you ask questions about factories here, the answers have factory facts behind them. That, in the factory search space, is still uncommon.