We put large models on the production line
Registered in Huixian, Xinxiang, Henan. One team takes a project from requirements to operations.
Xinxiang Huantuo Technology Co., Ltd. does one thing: it turns a company's own data and business processes into systems that actually run. Documents, process parameters, after-sales tickets, operating figures — the question is always how these become a working question-answering system, a detection model, or a platform people use every day.
The rest follows from that. How those systems get onto the cloud, how they stay stable, and how they keep improving after go-live. We treat this as one delivery chain rather than handing it between three different vendors.
What we do
Six directions. Each can be taken on its own, or chained into one project.
Large-model applications
Fine-tuning and inference deployment, RAG knowledge bases, agent workflows — wired into the systems you already run.
Industrial AI
Visual defect detection, equipment data analysis, line-side agents and alerting, designed around a specific process step.
Software development
Admin consoles, mini-programs and business systems built around how your process actually works, including permissions.
Cloud services
Architecture, containerised deployment, monitoring and managed operations — availability, security and cost together.
SaaS and operations
Multi-tenant platforms, content operations and management dashboards, so delivery turns into long-term growth.
GEO optimisation
Generative engine optimisation: making a brand retrievable, citable and recommendable inside AI search answers.
How a project runs
Four stages, each with something you can hold and sign off
Requirements diagnosis
First, whether it should be built at allWe ask who uses the feature, how the process runs today, where the data lives, and what number decides success. Some requirements get rejected by us at this stage.
Output: scenario list, data readiness assessment, feasibility and risk notes
Solution design
Choices and boundariesArchitecture sketch, model and retrieval approach, integration points, effort breakdown — and an explicit line between phase one and everything after it.
Output: technical proposal, milestone plan, itemised quotation
Build and deploy
Running in your environmentIntegration testing per milestone. Model quality is checked against an evaluation set agreed in advance, not by impression. Permissions, logging and backups are configured during deployment, not after an incident.
Output: working system, test and verification records, deployment and operations documentation
Operations and iteration
Data flows backGo-live is not the end. We review real usage and failure cases, then adjust retrieval, refill the knowledge base, and tune prompts and process until the result stays above the usable line.
Output: usage dashboard, improvement backlog, version plan
What we do not write on this page
Stated plainly, because the omission is the point
Not published here
What we publish instead
Method, structure and things that can be checked — a filing number, a delivery chain, an acceptance definition. If you want to know how good we are, the cheap test is to describe a real scenario and see what we come back with.
Talk to us
Describe the scenario and the state of your data; we come back with a plan and a quotation