Plain-Language Guide to the Yangtze Delta Token Operations Center: From Sign-up to First Call in Two Days

I spent two days testing the Yangtze River Delta (Jiaxing) Token Operations Center. This platform just launched, claiming "one enterprise integration, full network access" to over 100 large models. Honestly, when I first saw the news, my reaction was "another packaged gimmick." But after actually operating it, I found it did cut the barriers significantly.

Briefly, what does this platform do? Previously, if enterprises wanted to use AI, like integrating DeepSeek or Qwen, they had to find code, configure environments, and tune parameters themselves. Getting stuck at any step could take a week. This operations center is essentially a "Large Model Supermarket." You go in, select a task, and it matches you with the cheapest model, charging by Tokens (the unit for measuring AI text processing, think of it as "word count"). Pricing comes in three tiers: Universal Package, Pay-as-you-go, and Custom Exclusive—similar to mobile data plans.

Here's a walkthrough from start to finish. I'll try to write every step clearly, including the pitfalls I encountered.

Day 1: Registration and Enterprise Verification

Step 1: Open browser, visit official site

I found the URL by searching "Yangtze River Delta Token Operations Center"; it's zjt.xxx.cn (won't specify exactly, just search for it). The homepage is clean, with "Portal Website" in the top left and a big button saying "Connect Now" in the center.

Step 2: Register with phone number

Click "Connect Now," and a registration box pops up. Enter phone number, receive verification code, set password. No pitfalls here, just like registering for any website.

Step 3: Enterprise Verification (Pitfall Alert)

After registration, you choose between "Individual Developer" and "Enterprise User." If you're an enterprise, you MUST choose Enterprise User, otherwise you can't use APIs later (APIs are interfaces for programs to call models, like a "walkie-talkie"). I initially chose Individual Developer, entered, and found no API keys—only able to view the model list, unable to call.

Solution: Log out and re-register, choosing Enterprise User. Then upload business license, fill in company name and Unified Social Credit Code. Review took about 2 hours, showing "Verifying" in the backend. If you're in a hurry, call customer service to expedite; mine passed half an hour after calling.

Step 4: Obtain API Key

After verification passes, enter the console. Find "API Management" in the left menu. Click "Create Key," and the system generates a string like sk-xxxxxxxxxxxx. Copy it down and save it securely. This key is your "pass." If lost, others can use your account to call models and incur charges.

Day 2: First Model Invocation

Step 1: View Model List

There's a "Model Plaza" on the console homepage. Click it to see all available models, including DeepSeek, Qwen, Doubao, Kimi, etc., roughly 100+. Each model lists "Token Unit Price" and "Applicable Scenarios," such as "Text Generation," "Code Generation," and "Image Understanding."

Step 2: Choose Invocation Method

The platform offers two ways: Direct testing via web interface, or programmatic invocation via API. For beginners, start with the web interface.

Step 3: Try the Web Interface

Click "Online Experience" to enter a chat box. Select a model on the left, e.g., "DeepSeek-V3," and type "Explain what a Token is in one sentence" in the right input box. Send, and results return in about 3 seconds: "A Token is the smallest unit an AI model processes text with, understood as fragments of words or characters." Response was fast, about three times faster than my self-deployed DeepSeek.

Step 4: Invoke via API (One More Step)

Web interface is for testing only; real usage requires code. The platform provides Python sample code in the "API Documentation."

url = "https://api.zjt.xxx.cn/v1/chat/completions"
headers = {
    "Authorization": "Bearer sk-your-key",
    "Content-Type": "application/json"
}
data = {
    "model": "deepseek-v3",
    "messages": [{"role": "user", "content": "Hello"}]
}
response = requests.post(url, headers=headers, json=data)
print(response.json())

Copy-paste into a Python environment and run. If it returns {"choices":[{"message":{"content":"Hello! How can I help you?"}}]}, the invocation succeeded.

Pitfall: My first run errored with 401 Unauthorized. Checking revealed an extra space before the key. Removing the space fixed it. Note: Keys must not have spaces or newlines, or authentication fails.

One Week Later: Optimizing Workflow

After using it for a week, I summarized a few points:

1. Smart Routing Really Saves Tokens

The platform automatically matches models based on task type. For example, when I wrote a simple "translate English sentence" task, it auto-selected Qwen-turbo (cheapest model) instead of DeepSeek-R1 (expensive model). Token consumption saved about 30% compared to my manual selection. This feature is called "Smart Routing Engine" and can be enabled in "Task Management" in the console.

2. Transparent Costs

The backend has a "Cost Center" showing daily token consumption and fee details. I made about 5,000 calls, totaling over 80 RMB, roughly half the price of buying APIs directly from cloud providers. Because of "unified settlement," you don't need to calculate prices for each model yourself.

3. Security Auditing

As an enterprise user, I care about data security. The platform has a "Security Audit" feature recording each call's model, input, and output, with exportable logs. This is important for compliance.

What to Try Next

You've learned to invoke models. Next, try:

• Optimize Output with Prompt Templates: The platform provides preset templates like "Customer Service Dialogue" or "Code Review." Using them saves a lot of hassle.
• Integrate with Your Business System: Write the API key into your company's CRM or customer service system to let AI auto-reply to customer messages.
• Try Agent Building: The platform has an "Agent" feature allowing drag-and-drop configuration of simple AI assistants, like "Auto Email Reply," without coding.

The true value of this platform isn't "just another model supermarket," but enabling enterprises to run AI with the lowest cost and fastest speed. I've seen too many SMEs delay AI adoption due to high deployment barriers. Now, just buy a package and use large models like water—that might be the right posture for AI implementation.