Urban planners and architects increasingly treat large language models as core infrastructure for regulatory analysis, generative design, and site simulation. These workflows demand more than simple chat completions. They require long-context ingestion of municipal codes, multimodal understanding of blueprints and aerial photography, and agentic loops that iterate across planning constraints. The inference platform powering these pipelines must handle massive prompts without unpredictable costs, support vision and tool use natively, and integrate cleanly into existing Python or Node.js stacks. Oxlo.ai meets these requirements with request-based pricing, a broad model catalog, and full OpenAI SDK compatibility.
Workload Requirements for Built Environment AI
Planning and architecture tasks differ from standard LLM use cases in three ways. First, context windows are enormous. A single municipal zoning ordinance can exceed hundreds of thousands of tokens, and building information modeling (BIM) metadata adds even more surface area. Second, reasoning must be multimodal. Site plans, facade photographs, and scanned historical maps must be interpreted alongside text. Third, the best results often come from agentic pipelines: an LLM proposes a design variation, a code-checking function validates it, and the model iterates.
Oxlo.ai supports these patterns natively. Models such as DeepSeek V4 Flash offer a 1 million token context window for ingesting entire code volumes in one request. Kimi K2.6 combines advanced reasoning with vision and a 131K context for analyzing drawings and regulations together. GLM 5, a 744B parameter MoE, is built for long-horizon agentic tasks that span multiple tool calls and planning steps.
Agentic Zoning Compliance with Function Calling
One of the highest-value applications is automated zoning review. A firm can feed an entire district ordinance into a model and ask it to extract setbacks, floor area ratios (FAR), and height limits, then validate a proposal against those rules. Because Oxlo.ai uses flat per-request pricing, passing a full zoning document as context does not inflate cost. You pay per request, not per token.
The following Python example uses the OpenAI SDK with Oxlo.ai to perform structured extraction and register a compliance-checking tool. You can drop this into any existing pipeline by changing the base URL.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
Example: a lengthy municipal zoning excerpt
zoning_text = """
SECTION 12. R-3 RESIDENTIAL DISTRICT.
A. Minimum lot area: 6,000 square feet.
B. Front yard setback: 25 feet.
C. Maximum building height: 35 feet.
D. Maximum FAR: 0.5.
...
"""
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{
"role": "system",
"content": (
"You are a zoning analyst. Extract rules into JSON. "
"If a user provides a proposal, call check_compliance."
)
},
{
"role": "user",
"content": f"Extract rules from this ordinance:\n\n{zoning_text}"
}
],
response_format={"type": "json_object"},
tools=[
{
"type": "function",
"function": {
"name": "check_compliance",
"description": "Validates a building proposal against extracted zoning rules",
"parameters": {
"type": "object",
"properties
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