Utilities and telecommunications operators manage some of the most document-intensive, regulated, and geographically distributed infrastructures in the world. From substation schematics and FCC filings to real-time network telemetry and customer support transcripts, the sector generates enormous volumes of unstructured text. Large language models can automate compliance review, outage root-cause analysis, and field-service assistance, but token-based inference pricing often makes long-context and agentic workloads economically impractical at scale. Oxlo.ai offers a developer-first inference platform with flat per-request pricing, fully OpenAI SDK compatibility, and a broad catalog of open-source and proprietary models that are immediately relevant to utility and telecom engineering teams.
Long-Context Inference for Regulatory and Operational Documents
Utility and telecom workflows are buried in lengthy documents. NERC reliability standards, EPA environmental reports, FCC spectrum licenses, and decades of maintenance logs routinely exceed hundreds of thousands of tokens. On token-based platforms, submitting an entire document for summarization, clause extraction, or cross-reference checking can multiply costs with every additional page.
Oxlo.ai uses request-based pricing: one flat cost per API call regardless of prompt length. This means a 100-page regulatory filing or a full day of syslog data costs the same as a single-sentence greeting. For teams that need to reason over entire documents without aggressive chunking or pre-filtering, this pricing model removes the budget friction that typically discourages deep analysis.
Models such as DeepSeek V4 Flash support a 1 million token context window, making it possible to load large schematics or log dumps in one shot. Kimi K2.6 offers a 131K context with advanced reasoning and agentic coding capabilities, while GLM 5 is built for long-horizon agentic tasks that span multiple documents and tools.
Model Selection for Utility and Telecom Workloads
Oxlo.ai hosts more than 45 models across seven categories. The following are particularly relevant to infrastructure operators:
- DeepSeek R1 671B MoE: Deep reasoning and complex coding for root-cause analysis of cascading grid or network failures.
- Llama 3.3 70B: A general-purpose flagship for document Q&A, policy interpretation, and customer support automation.
- Kimi K2.6 and K2.5: Advanced chain-of-thought reasoning, vision, and coding for NOC automation and equipment inspection.
- Qwen 3 32B: Multilingual reasoning and agent workflows for international operations or diverse workforces.
- GLM 5 744B MoE: Long-horizon agentic tasks that coordinate across compliance, engineering, and vendor documentation.
- DeepSeek V3.2: Coding and reasoning for internal tooling, available on the free tier for prototyping.
- Qwen 3 Coder 30B and Oxlo.ai Coder Fast: Specialized code generation for infrastructure-as-code templates, SQL queries, and network automation scripts.
Agentic Workflows with Function Calling
Modern network operations centers and utility control rooms do not just need chatbots. They need agents that can query monitoring APIs, open trouble tickets, and synthesize alerts across SCADA, CRM, and GIS systems. Oxlo.ai supports function calling, JSON mode, streaming responses, and multi-turn conversations with no cold starts on popular models.
Because the platform is fully OpenAI SDK compatible, you can point existing agent frameworks directly at Oxlo.ai by changing a single environment variable. The example below uses the Python SDK to classify a fiber outage and automatically create a ticket via a defined tool.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ.get("OXLO_API_KEY")
)
tools = [
{
"type": "function",
"function": {
"name": "create_outage_ticket",
"description": "Create a network outage ticket",
"parameters": {
"type": "object",
"properties": {
"severity": {
"type": "string",
"enum": ["low", "medium", "high", "critical"]
},
"region": {"type": "string"},
"description": {"type": "string"}
},
"required": ["severity", "region", "description"]
}
}
}
]
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{
"role": "system",
"content": "You are a network operations assistant. Analyze the report and create a ticket if needed."
},
{
"role": "user",
"content": "Fiber cut detected in downtown Austin. 2,400 customers offline. Backbone ring 4 showing LOS."
}
],
tools=tools,
tool_choice="auto"
)
print(response.choices[0].message)
Running this against Oxlo.ai produces a structured tool call without cold-start latency, which matters when an outage is in progress. The same pattern scales to multi-step agents that retrieve equipment records, query weather APIs for storm impact, and draft preliminary root-cause summaries.
Vision for Field Operations and Asset Inspection
Telecom technicians and utility line workers increasingly rely on mobile devices to photograph equipment, corrosion, cable damage, or vegetation encroachment. Oxlo.ai supports vision input through models such as Kimi VL A3B and Gemma 3 27B, allowing field teams to upload an image and receive immediate guidance on part numbers, safety clearances, or required maintenance.
Because Oxlo.ai charges per request rather than per image token, a multimodal field report that includes multiple high-resolution photographs and a lengthy context paragraph costs the same flat rate as a simple text query. This predictability makes it feasible to roll out vision-assisted diagnostics to thousands of field workers without ballooning inference bills.
Embeddings and Retrieval-Augmented Generation
Decades of engineering drawings, environmental impact statements, and vendor manuals are difficult to search. Oxlo.ai provides embedding models including BGE-Large and E5-Large through a standard embeddings endpoint. You can index internal documentation in a vector store, retrieve relevant chunks, and synthesize answers with a chat model.
The retrieval step is handled by your vector database, while the generation step benefits from Oxlo.ai's flat per-request pricing. Even when the synthesized prompt contains dozens of retrieved chunks and detailed instructions, the cost remains fixed. This makes RAG pipelines economically stable for utilities that need to query massive historical archives on a daily basis.
Predictable Pricing from Prototype to Production
Oxlo.ai offers several plans that fit the project lifecycle of infrastructure operators:
- Free: $0 per month, 60 requests per day, 16+ free models, and a 7-day full-access trial for evaluation.
- Pro: $80 per month, 1,000 requests per day, all models included.
- Premium: $350 per month, 5,000 requests per day, all models, plus priority queue access.
- Enterprise: Custom contracts with unlimited requests, dedicated GPUs, and guaranteed savings over your current provider.
For long-context and agentic workloads, request-based pricing can be 10x to 100x cheaper than token-based alternatives because cost does not scale with input length. Exact plan details are available at https://oxlo.ai/pricing.
Getting Started
Oxlo.ai is a drop-in replacement for the OpenAI SDK. Change your base URL and API key, and existing utilities or telecom applications will work without refactoring.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ.get("OXLO_API_KEY")
)
completion = client.chat.completions.create(
model="deepseek-r1",
messages=[
{"role": "system", "content": "You are a utility engineering assistant."},
{"role": "user", "content": "Summarize the NERC CIP-002 reliability standard and list the top three compliance gaps for a mid-size municipal utility."}
]
)
print(completion.choices[0].message.content)
The platform requires no cold starts on popular models, so automation scripts, monitoring agents, and field-service apps receive immediate responses. Whether you are building a compliance chatbot, a network operations agent, or a vision-guided maintenance tool, Oxlo.ai provides the models, API compatibility, and pricing structure to deploy without token-based surprises.
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