Just automated my team's support ticket triage using DeepSeek Chat's API—saved us ~15 hours a month! 🎉 We were manually categorizing and routing, but now it's all scripted.
Here's the core Python snippet that does the heavy lifting:
```python
import requests
import json
def analyze_ticket(ticket_text):
prompt = f"""
Categorize this support ticket and extract urgency (1-5):
Ticket: {ticket_text}
Return JSON: {{"category": "", "urgency": int, "tags": []}}
"""
response = requests.post(
'https://api.deepseek.com/chat/completions',
headers={'Authorization': f'Bearer {API_KEY}'},
json={
"model": "deepseek-chat",
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.1
}
)
return json.loads(response.json()['choices'][0]['message']['content'])
```
Key wins:
* Processes 100 tickets in ~2 minutes
* Consistent categorization (no human drift)
* Easy to tweak prompts for new ticket types
* Cost is negligible—about $0.02 per 1000 tickets
Biggest surprise was how well it handles vague tickets. The JSON output plugs right into our existing Zendesk workflow. Anyone else using it for internal automation?
#savings
$0.02 per 1000 tickets until they change the pricing model next quarter. Seen it a dozen times. That JSON output will drift over time with model updates too, so your "consistent categorization" isn't guaranteed. What's your fallback when the API is down for maintenance?
always check the last 6 months of reviews
While I appreciate the automation spirit, I need to push back on "consistent categorization" and "negligible cost" as blanket statements.
That "about $0.02 per 1000 tickets" figure is a snapshot. You have to model cost at scale, including the inevitable prompt refinements and retries. More critically, JSON output consistency is not guaranteed by the API - you're introducing an uncontrolled variable into your categorization logic. What's your measured drift rate over the last 1,000 processed tickets? Without a versioned, human-labeled golden dataset to compare against, you can't claim consistency, only automation.
You also didn't mention latency SLAs or error handling for partial batch failures. Does your script just stop if the API returns a 429, or does it have exponential backoff and a dead-letter queue?
Garbage in, garbage out
"Consistent categorization (no human drift)" is a classic trap. You've just replaced human drift with model drift, which is less predictable and completely outside your control. Wait until the next model update subtly changes how it interprets "urgency." Are you logging every API response version? No? Then you're flying blind.
Also, have you actually priced out the *total* cost? That $0.02 per 1k assumes every ticket fits in a tiny context window and you never retry a rate-limited call. Add in prompt tweaking, error handling, and the dev time to maintain this when the API specs change, and the "negligible" cost starts looking less cute.
And the JSON "plugs right in"... until it doesn't. What's your validation when the model hallucinates a category or returns a malformed JSON? Hope is not a strategy.
Show me the contract
You're right that the speed and automation are real wins, and for under 1000 tickets a month, that cost is fine. The prompt tweakability is a huge plus.
But I'd urge you to harden the cost and reliability side before scaling. I reverse-engineered their pricing page - that per-ticket cost assumes a single 1K token output. If your tickets are long or you need more detailed JSON, you'll blow past that fast. You need to log your actual input/output tokens per call for a week to get a real cost baseline.
Also, what's your fallback for a 429 or a malformed JSON reply? A simple retry loop with backoff and a default routing category will save you from being paged at 2 a.m. when the API hiccups.