History

Only dates we can point to

Nothing here is filled in with an unverifiable founding year or a client count

Most company timelines run from a founding year to "recognised by the industry". This one is different by choice: an entry appears only if it can be checked in a public record — a filing number, a domain, a deployment log, published content. Everything else is described as capability rather than as a date.

Verifiable entries

Three, each checkable outside this website

ICP filing completed

within 2025

The domain huant.ltd received the filing number 豫ICP备2025140588号. It is shown in the footer of every page on this site and links to the MIIT filing lookup, where it can be checked against the company name.

Check at: the MIIT ICP filing and licence enquiry system

Website relaunched

October 2026

The public positioning was narrowed to one line: AI engineering delivery. Large-model applications, industrial AI, software and SaaS systems, cloud deployment and operations, and GEO. Home, company, product and contact pages were rewritten to that scope.

Check at: domain and deployment records, first publication dates of the pages

Technical library kept current

ongoing

More than five hundred articles on AI engineering, cloud services, back-end architecture, industrial AI and policy interpretation, each with its sources named so the judgement can be re-checked. It is how we explain our engineering practice in public, and it is the content layer behind GEO work.

Check at: the news list and the site archive (Chinese)

How the capability developed

Four stages without dates, because the order matters more than the calendar

First: deliver a tool

scripts and small systems

Turn one repetitive job into something usable: a batch script, a dashboard, a box for looking things up. This stage teaches asking what is needed, shipping fast, and having someone actually use it.

Then: deliver a system

permissions and integration

A tool becomes a system once it has accounts and roles, multi-step processes, integration with what already exists, and decisions about storage and migration. This stage teaches engineering constraints, not feature counts.

Then: own the model and data chain

quality you can accept

From calling a ready-made API to deciding whether to fine-tune or fix the retrieval instead, how to define an evaluation set, and which kind of failure a bad answer belongs to. Only after this can a quality level be committed to.

Finally: operate it long-term

data flows back

Go-live is the start: dashboards, alerting, knowledge-base upkeep, release cadence, GEO monitoring. Keeping something running is what turns a project into a product.

What this page leaves out

Written down so it stays out

Not published

Founding year without a recordTeam size Number of clients servedTransaction volume and growth rates Client names without permission"Industry leading" claims

What to look at instead

Method, structure and anything checkable. The cheap test of how good a delivery team is: describe a real scenario and see what comes back — the plan, the acceptance definition, and what they say they cannot do.