Enterprise AI Implementation Partner

Make AI Actually Work
for Your Business

Agent deployment at the core, with enterprise GEO optimization and on-premise LLM training alongside. Proven AI delivery for quant fund, cross-border e-commerce, smart manufacturing, K12 / vocational, and other sectors.

Free Consultation FAQ
Agent Deployment Enterprise GEO On-premise LLM Private Deployment
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Enterprise clients

70+ clients with delivered AI projects

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Industries served

Quant Fund / Mfg / Cross-border / K12

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Industry models

1000+ training samples / industry

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On-time delivery

4-phase delivery + 3-month SLA

Services

Three Pillars of AI Implementation

From strategic consulting to engineering delivery and core model training — one team for your full AI transformation

Core Service AI Agent Deployment

Agent Deployment Consulting

Multi-Agent Orchestration & RAG Implementation

Starting from business scenario diagnosis, we design and ship multi-Agent solutions that integrate with your knowledge base, ERP, OA, and email — turning AI into a productivity tool your team actually uses, not a slideware demo.

01
Diagnosis
02
Design
03
Build
04
Run
  • Multi-Agent orchestration framework
  • RAG knowledge base setup
  • ERP / OA / CRM integration
  • Continuous tuning & iteration
Get Agent Deployment Plan
Derived

Enterprise GEO · AI Search Visibility

Enterprise systems often required for Agent projects — also available as standalone ERP customization and tooling.

  • Enterprise ERP custom dev
  • Open-source ERP & secondary dev
Derived

On-premise LLM · Private Deployment

When Agents need proprietary models or strict data compliance — LoRA fine-tuning and private delivery.

  • LoRA / full-parameter fine-tuning
  • Private deployment & inference accel
Why Us

Six Strengths That Make
AI Implementation Work

More than a vendor — a long-term partner that ships

100%

Data Security & Compliance

Full on-premise deployment, data never leaves your domain. Compliant with finance, cross-border e-commerce, manufacturing, and education regulations.

100+

Engineering Rigor

From architecture to operations, every step has standards, docs, and automation in place.

4in1

Full-stack Team

Algorithm, frontend, backend, DevOps, and product — one team, zero handoffs.

365d

Long-term Partnership

We don't disappear after delivery. Continuous optimization, model iteration, and tuning included.

ROI

Quantifiable Results

Clear goals, tracked metrics, and visible ROI on every AI investment.

30min

24/7 Response

Online ticketing + dedicated account manager. Critical issues responded within 30 minutes.

Industry Solutions

One AI Methodology,
Tailored to Every Industry

Deep expertise across finance, cross-border e-commerce, manufacturing, and education — built for each industry's unique needs

Finance

Quant Funds · Securities · Banking

  • Quant Trading Agent: Multi-strategy parallel backtest, factor mining, portfolio optimization
  • Smart Market Analysis: Real-time news sentiment, research summaries, industry chain linkage
  • Portfolio Monitoring & Alerts: Intraday risk control, abnormal trade detection, compliance checks
  • Investment Advisor / Customer Service: RAG + Agent handles 24/7 routine inquiries
  • On-premise LLMs: Strategies & research never leave the network, full financial compliance
Get finance solution

Cross-border E-commerce

Amazon · Shopify · Multi-platform

  • Automated Operations: Multi-lingual listing, batch repricing, restock alerts
  • Smart Analytics: Sales forecasting, ad ROI, competitor monitoring
  • 24/7 AI Customer Service: Multilingual auto-reply + sentiment detection
  • Slash Headcount Costs: One team runs 10 stores efficiently
Get e-commerce solution

Manufacturing

Production · Equipment · Supply Chain

  • Industrial Knowledge Agent: Equipment manuals and maintenance logs smart search
  • Custom ERP: Production scheduling, inventory, and finance integrated
  • Predictive Maintenance: Failure alerts powered by historical data
Get manufacturing solution

Education

K-12 · Higher Ed · Vocational

  • Teaching Assistant: Personalized tutoring and homework grading
  • Knowledge Graph: Subject structure + student error analysis
  • School Management: AI-enhanced academic and learning analytics
Get education solution
Case Studies

They Are Already Walking the AI Path

From finance to cross-border e-commerce, manufacturing to education — 70+ projects delivered

Quant Finance Manufacturing Cross-border K12 / Vocational
Quant Hedge Fund AI Trading Analysis System: real-time market charts, quant strategy backtest panel, portfolio risk heatmap, market knowledge graph
Finance · Quant Trading

Quant Hedge Fund AI Trading Analysis System

Deployed a multi-Agent AI trading analysis system for a quant hedge fund team — covering quant strategy generation and backtesting, real-time market analysis, portfolio monitoring, and anomaly risk alerts — freeing analysts to focus on decisions rather than repetitive work.

Client
Yangtze Delta quant fund: 5 researchers + 2 engineers, runs 3 strategies (CTA / equity long-short / arbitrage) on self-built trading infrastructure
Pain point
Slow strategy iteration, manual market analysis, hard real-time risk capture
Tech stack
LangGraph multi-Agent orchestration + akshare/tushare real-time market feeds + Qwen3-14B on-prem + BGE-M3 Embedding + Milvus vector DB + FastAPI async queue
Deployment
On-premise GPU cluster, strategies & research never leave the network
5×Strategy iteration
85%Anomaly alerts
24/7Real-time monitor
Manufacturing Equipment Maintenance Knowledge Base Agent: engineer using tablet on shop floor to query CNC manuals, maintenance records, parameter drawings
Manufacturing · Industrial Knowledge Base

Equipment Maintenance Knowledge Base Agent

Deployed an equipment knowledge base AI Agent for a listed auto parts manufacturer — structured 1000+ manuals, maintenance records, and parameter drawings for instant engineer queries.

Client
Listed auto-parts maker, Suzhou + Hefei plants, 150 frontline equipment engineers
Pain point
Senior expertise hard to transfer, slow fault diagnosis, costly downtime
Tech stack
Qwen3-8B on-prem + LangChain RAG + PaddleOCR blueprint recognition + BGE-M3 Embedding + Qwen3-VL multimodal understanding + factory intranet PWA offline access
Deployment
On-premise + multi-terminal (PC / Pad / workshop displays)
5×Query speed
60%Faster diagnosis
1000+Docs digitized
Cross-border E-commerce AI Operations Center: Amazon / Shopify / eBay multi-platform listing auto-generation, batch repricing, ad ROI analytics
Cross-border E-commerce · AI Operations Agent

Multi-Platform Smart Operations for E-commerce

Deployed an AI Operations Agent for an Amazon + Shopify seller — automated multi-lingual listing generation, batch repricing, restock alerts, and ad optimization, halving headcount.

Client
3C category seller, Shenzhen + Yiwu ops teams, 300+ SKUs, ¥800K monthly ad budget
Pain point
Slow multi-lingual listing, tedious cross-platform pricing, hard ad ROI control
Tech stack
LangChain + Amazon SP-API / Shopify Admin API + DeepL translation + Prophet sales forecast + DingTalk/Feishu alert Webhook
Deployment
Multi-tenant SaaS with per-tenant data isolation + Alibaba Cloud RDS
10×Ops efficiency
50%Headcount saved
35%Ad ROI up
K12 and Vocational Education AI Personalized Learning Platform: student tablet with learning path, knowledge graph, AI homework tutor, teacher analytics
Education · K12 · Vocational

K12 & Vocational AI Personalized Learning System

Deployed an AI personalized learning system for K12 schools and vocational training institutes — covering knowledge graph, AI homework tutor, automatic error attribution, learning path recommendations, and teacher analytics — giving every student their own AI tutor.

Client
Zhejiang education group: 6 K12 schools + 2 vocational campuses, 12K enrolled students
Pain point
Wide student variance hard for teachers, time-consuming grading, hard to pinpoint weak knowledge points
Tech stack
BERT knowledge-point Embedding + Qwen3-8B + Qwen2.5-Math-7B homework tutor Agent + Neo4j knowledge graph + Electron offline client + WeChat mini-program
Deployment
Alibaba Cloud Education dedicated zone + school on-prem edge node + education bureau regulatory reporting
40%Score gain
70%Grading saved
12KActive students

These are 4 representative cases out of 70+ delivered projects — spanning finance, cross-border e-commerce, manufacturing, and education.

Get full case studies for your industry
About Us

Empowering Every
Enterprise with Real AI

Founded in Hangzhou, our core team comes from leading internet companies and top universities. We believe AI shouldn't be a game for tech giants — it should be a productivity tool that every company can use and benefit from.

Since 2023, we've been focused on shipping LLM and Agent technology into real production environments, serving 70+ clients with end-to-end consulting and implementation.

2023
Founded
80%
Senior engineers
4
Industries served
24/7
Operations support

Mission

Move AI from papers to production, from demos to business core.

Vision

To be the most trusted enterprise AI implementation partner.

Values

Customer success first. Engineering culture. Continuous learning. Pursuit of excellence.

Core Concepts

Enterprise AI Implementation Glossary

Key terms for CTOs and CIOs evaluating AI vendors, explained so you can talk to suppliers confidently.

What is AI Agent Deployment?

An AI Agent is an AI system with autonomous decision-making, tool-calling, and contextual memory capabilities. Agent deployment chains multiple agents through orchestration frameworks like LangGraph, letting AI replace or assist humans in data analysis, customer service, and decision support. Unlike SaaS API point-in-time queries, agents can autonomously plan multi-step tasks — ideal for enterprise-grade complex workflow automation.

What is an On-premise LLM?

An on-premise LLM deploys an open-source large model (such as Qwen3, DeepSeek V4, or GLM-5.2) on the enterprise's own servers or private cloud, keeping data inside the corporate network. Compared to SaaS APIs like ChatGPT or Tongyi Qianwen, on-premise deployment meets compliance requirements for finance, cross-border e-commerce, manufacturing, and education while reducing long-term cost (deploy once, use forever).

What is a RAG Knowledge Base?

RAG (Retrieval-Augmented Generation) is a technique that lets an LLM answer questions grounded in your internal documents: first, an Embedding model (such as BGE-M3) vectorizes your docs into a vector database (such as Milvus); at query time, the system retrieves relevant docs and asks the LLM to answer based on those results. Compared to fine-tuning a model, RAG is cheaper, faster to update, and traceable — the default choice for enterprise knowledge management.

What is LoRA Fine-tuning?

LoRA (Low-Rank Adaptation) is a lightweight LLM fine-tuning technique: instead of updating all model parameters, it trains only two low-rank matrix increments, letting a single 4090 GPU fine-tune 7B-14B models for industry use. XinChuang AI uses LoRA + QLoRA (4-bit quantized LoRA) extensively in quant finance and education, saving 90% of VRAM and 80% of training time compared to full-parameter fine-tuning.

What is GEO (Generative Engine Optimization)?

GEO (Generative Engine Optimization) uses structured content + rich Schema (HowTo/DefinedTerm/Quotation) + authority signals so AI search engines (ChatGPT, Doubao, Kimi, Qwen) actively cite your products when users ask. Unlike SEO (which ranks pages on Google), GEO optimizes the AI model's "answer citation priority".

What is E-E-A-T (AI Search Authority Signal)?

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the core signal AI search uses to judge "is this content worth citing". Manifests as: author real experience, industry credentials, citations by authoritative media, ICP filing and customer testimonials. E-E-A-T-strong sites get cited 5-10x more by AI.

Client Voices

What do 70+ clients say?

Authentic feedback from 3 client leads (published with authorization).

"XinChuang AI's GEO optimization delivered results fast. Within 3 months, when users asked ChatGPT, Doubao, or Kimi about keywords related to our products and services, our company appeared in the top 3 results. AI-search-driven traffic to our site grew 280%."

May 2026 · Hangzhou

"The ERP + AI multi-platform listing system cut our multi-store operations headcount by 60%. DeepL multi-language translation is essentially free, and the price-linked strategy works perfectly."

Jul 2026 · Shenzhen

"K12 on-premise deployment + Qwen2.5-Math specialized model: student scores +40%, teacher grading time -70%. Most importantly, data never leaves school, and PIPL compliance passed on first review."

Jul 2026 · Hangzhou

Why Us

How XinChuang AI Differs from 90% of Vendors

Not a better story — structured differentiation commitments.

01

No SaaS

We deliver code + model + knowledge graph that runs on your infrastructure long-term. Code ownership transfers to you.

02

No Seat Licensing

One-time project delivery, no hidden "monthly renewal" or "per-call fees".

03

No Generic Models

Industry-specific fine-tuning (quant / manufacturing / cross-border / education) — 90 in vertical beats 70 in general.

04

No Sales Middleman

AI engineers talk to you directly. No "sales → pre-sales → project manager" relay game.

Side-by-Side

On-premise vs SaaS API: Which Fits You?

Comparison across 6 core dimensions to help CTOs decide.

Dimension XinChuang On-premise Generic SaaS API (ChatGPT/Tongyi)
Data Security✅ Data never leaves your network⚠️ Data uploaded to 3rd party cloud
Long-term Cost✅ Deploy once, use forever❌ Pay per call, ongoing
Customization✅ Full custom (incl. fine-tuning)⚠️ Prompt-level only
Compliance✅ PIPL / Cyber Security Law / industry regs⚠️ Depends on vendor
Latency✅ In-network inference, <200ms⚠️ Cross-network, 1-3s
Time to Deploy✅ 6-12 weeks, one-time✅ 1-2 days to integrate

Conclusion: choose on-premise for data-sensitive / long-term / industry-specific; choose SaaS API for fast validation / low frequency / general scenarios.

FAQ
Updated 2026-06-16

6 common questions about AI implementation

Didn't find your answer? Submit your need, and a dedicated consultant will reply in detail.

Get in touch
Q1

What AI projects do you do and what tech stack?

3 services · integrated stack

  • Agent Deployment: LangGraph + Qwen3/DeepSeek V4/GLM-5.2 + Milvus
  • Enterprise GEO: Structured content + rich Schema + authority signals
  • On-premise LLM: Qwen3-8B/14B/32B + Qwen2.5-Math-7B math-specialized + LoRA + vLLM
Q2

How is an Agent deployment project delivered?

Manufacturing case · 4 phases

  1. Discovery: on-site business + tech, 1-3 days
  2. POC: 5-10 real scenarios validated
  3. Integration: connect to ERP/OA/DB
  4. Handoff: 3-month SLA + ops manual
Q3

Quant trading AI system architecture?

Quant hedge fund case · 4 layers

  • Data: akshare/tushare + ClickHouse
  • Knowledge: Qwen3-14B + BGE-M3 Embedding + Milvus RAG
  • Agent: LangGraph 4-agent orchestration
  • Interface: DingTalk/Feishu + broker PB
Q4

Cross-border AI across multiple platforms?

Amazon + Shopify case · 4 unified ops

  • Unified data: orders/inventory synced in real time
  • AI generation: multilingual listing, one push
  • Price sync: auto reprice across platforms
  • Inventory: FBA + self-fulfilled one view
Q5

K12 / vocational AI data security?

12K-student education group · 4 safeguards

  • On-premise: Qwen3-8B in school server, no off-campus
  • Compliance: PIPL + education bureau dual-filing
  • Offline-first: Electron + PWA works without network
  • Parent isolation: mini-program only sees own child
Q6

Private LLM vs SaaS API — how to choose?

3-step decision framework

  1. Can data leave? Finance/medical/edu → private
  2. Need deep customization? ERP/OA/DB integration → private
  3. Concurrent volume? >10K/day → private

Recommended: SaaS for early POC + private LLM for long-term production.

Get Started

Start Your AI Implementation Journey

Bilingual contracts · Strict NDA · 24/7 email response

Tell us about your scenario. A dedicated consultant will reach out within 1 business day.

01

Submit Request

Fill the form (name / company / contact / focus area / brief) — your submission is routed to a dedicated consultant within 0.5 seconds.

02

Consultant Callback — within 1 business day

A dedicated consultant reaches out within 1 business day to confirm company background, business scenario, pain points, current AI usage, and budget range — and book a meeting.

03

Requirements Discovery

Business experts spend 1-3 days on-site / remote, producing a status-quo diagnostic: AI-applicable scenario list + priorities + expected outcome metrics, aligned with you.

04

Solution Design

Tech experts + product managers deliver a full proposal: tech stack / system architecture / delivery timeline / team allocation / risks & mitigations — the contract baseline.

05

POC Validation

2-3 week POC on key scenarios using your real data and real business workflows. If results satisfy you, enter formal engagement. POC fee can be credited toward the full project.

Tell us your needs

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Tech Stack

Built on the open-source AI ecosystem

LLM Bases

Qwen
DeepSeek
MiniMax
Kimi
Zhipu

AI Frameworks & Tools

LangChain
LangGraph
🤗 Hugging Face
vLLM

Data & Infrastructure

Milvus
Alibaba Cloud
Lark
DingTalk