IT service management platforms generate an endless stream of unstructured text. Incident tickets, change requests, chat logs, and email threads accumulate faster than most teams can process. Large language models offer a practical path through this noise, turning raw text into structured decisions, automated responses, and actionable knowledge. The challenge is not whether LLMs can help, but how to deploy them economically when real-world ITSM workloads routinely involve long ticket histories, attached logs, and extensive knowledge base articles.
Intelligent Ticket Triage and Routing
Manual ticket sorting is a persistent bottleneck. An LLM can classify intent, detect urgency, and extract entities from the first message, then route the ticket to the correct queue without human intervention. Because Oxlo.ai is fully OpenAI SDK compatible, you can drop this logic into existing automation with no client rewrite.
Function calling and JSON mode are particularly useful here. You can force a structured schema that your ITSM platform already understands, eliminating fragile regex parsing.
from openai import OpenAI
import os
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ.get("OXLO_API_KEY")
)
def classify_ticket(subject, body):
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{
"role": "system",
"content": (
"You are an ITSM classifier. "
"Analyze the ticket and call the route_ticket function."
)
},
{
"role": "user",
"content": f"Subject: {subject}\nBody: {body}"
}
],
tools=[
{
"type": "function",
"function": {
"name": "route_ticket",
"description": "Route the ticket to the correct team",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"enum": ["network", "database", "access", "hardware"]
},
"urgency": {
"type": "string",
"enum": ["low", "medium", "high", "critical"]
},
"summary": {"type": "string"}
},
"required": ["category", "urgency", "summary"]
}
}
}
],
tool_choice={
"type": "function",
"function": {"name": "route_ticket"}
}
)
return response.choices[0].message.tool_calls[0].function.arguments
result = classify_ticket(
"VPN timeout on bastion host",
"Users in APAC cannot connect to the production VPN since 09:00 UTC. "
"Authentication succeeds but the tunnel drops after 30 seconds."
)
print(result)
Long-Context Analysis and Root Cause Detection
Complex incidents spawn long threads. Performing root cause analysis often requires reading dozens of updates, system logs, and prior related tickets. This is where long-context models become essential. Oxlo.ai hosts options like DeepSeek V4 Flash, which supports a 1M token context, and Kimi K2.6, which offers a 131K context window with advanced reasoning and vision capabilities. You can feed an entire conversation history plus log attachments into a single request.
Because Oxlo.ai uses request-based pricing, that massive prompt costs the same as a one-line status check. For ITSM teams handling hundreds of verbose incidents daily, this removes the penalty on long context that token-based platforms impose.
Knowledge Base Generation and Semantic Search
Resolved tickets are knowledge gold mines that usually go untapped. An LLM can draft a concise KB article from a closed incident, standardizing the format and stripping out sensitive identifiers. For retrieval, Oxlo.ai provides embedding models including BGE-Large and E5-Large through the standard /embeddings endpoint. You can store vectors in your existing vector database and surface relevant historical incidents to agents before they write a single update.
Automated Change Risk Assessment
Change advisory boards spend hours reviewing requests for risk. Reasoning models such as DeepSeek R1 671B and Kimi K2 Thinking can analyze a change request against historical incident data, flagging high-risk windows, missing rollback procedures, or dependencies that human reviewers might overlook. On Oxlo.ai, these reasoning models are available through the same OpenAI-compatible endpoint, so adding chain-of-thought analysis to your ITSM pipeline requires no new SDKs or vendor-specific wrappers.
Implementation with Oxlo.ai
The code below shows a complete pattern: streaming a long-context incident summary using the OpenAI SDK pointed at Oxlo.ai. Streaming responses let your ITSM UI render results progressively, keeping the interface responsive even when analyzing lengthy ticket histories.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ["OXLO_API_KEY"]
)
incident_thread = """[2024-01-15 08:00] Alert: DB latency > 2s on shard-04
[2024-01-15 08:15] oncall: restarted connection pool, no effect
[2024-01-15 08:45] oncall: disk I/O at 95% on shard-04 primary
... (50 additional events) ..."""
stream = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{
"role": "system",
"content": "Summarize this incident thread, propose a root cause, and list preventive actions."
},
{
"role": "user",
"content": incident_thread
}
],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
This pattern works with any model on the platform, from general-purpose flags like Llama 3.3 70B and Qwen 3 32B to specialized coders like Qwen 3 Coder 30B when the incident involves configuration or script review.
Why Request-Based Pricing Fits ITSM
ITSM is inherently long-context. A single incident might include the original alert, twenty status updates from engineers, log snippets, and cross-references to previous tickets. Under token-based billing, every character in that history drives up cost. Oxlo.ai charges one flat rate per API request regardless of prompt length. Summarizing a hundred-turn ticket thread or analyzing a massive change request document costs the same as a minimal health-check ping.
You can see the exact plan structure on the Oxlo.ai pricing page. This predictability matters when your automation volume scales with incident frequency, not with word count.
Getting Started
You can start experimenting with ITSM automation on the Oxlo.ai free tier, which includes 60 requests per day across 16+ models and a 7-day full-access trial. For production workloads, the Pro plan offers 1,000 requests per day across all models, including long-context and reasoning options. Point your existing OpenAI client to https://api.oxlo.ai/v1, and you can begin routing tickets, summarizing incidents, and extracting knowledge with deterministic costs and no cold starts on popular models.
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