Unlocking Urban Planning with LLMs

#aiinfrastructure #oxlo #ai

Urban planners are drowning in unstructured text. Municipal codes, environmental impact reports, public comment transcripts, and decades of zoning amendments pile up in formats that resist traditional database queries. Large language models can extract patterns, reconcile conflicting ordinances, and draft scenario analyses, but production deployments face a predictable obstacle: cost scales with context length. When a single comprehensive plan can span thousands of pages, token-based billing turns document analysis into a budget risk. Oxlo.ai removes that friction with flat per-request pricing, making long-context inference predictable for municipal teams and civic-tech developers.

Taming Long-Form Municipal Documents

Comprehensive plans and unified development ordinances routinely exceed hundreds of thousands of tokens. With token-based providers, ingesting even one complete document can consume a significant share of a monthly budget. Oxlo.ai charges one flat cost per API request regardless of prompt length, so planners can pass entire ordinance chapters into a single call without cost escalation.

For pure text ingestion, DeepSeek V4 Flash offers a 1 million token context window and efficient MoE architecture, making it ideal for searching across multi-year council transcripts. Kimi K2.6 provides advanced reasoning and a 131K context window for cases that require tighter integration with structured data. Both models support JSON mode, so you can return extracted violations, permitted uses, or amendment histories as structured records rather than prose.

Agentic Workflows for Policy Simulation

Planning is iterative. An LLM that simply summarizes a document is useful, but one that can call tools is far more powerful. By combining function calling with municipal APIs, a model can query parcel databases, pull census block statistics, and draft zoning amendments that reference live data.

Qwen 3 32B is built for multilingual reasoning and agent workflows, which matters in diverse metro areas where source material mixes English, Spanish, Mandarin, and other languages. GLM 5, a 744B MoE model, targets long-horizon agentic tasks that span multiple research and drafting steps. On Oxlo.ai, these models are available through the standard chat/completions endpoint with no cold starts, so an agentic loop can run without latency penalties between tool calls.

Vision Models for Spatial Analysis

Text is only half of the planning stack. Satellite imagery, streetscape photography, and scanned historical maps contain spatial signals that text alone cannot capture. Modern vision-language models let you prompt directly against these images.

Kimi K2.6 handles vision inputs alongside its reasoning capabilities, letting planners compare before-and-after aerial photography or evaluate site plans against photographic evidence. For lighter vision tasks, Gemma 3 27B and Kimi VL A3B offer strong performance on image understanding. Because Oxlo.ai bills per request, adding several high-resolution images to a prompt does not alter the cost, a significant departure from token-based image encoding fees.

A Practical Pipeline

The following Python snippet demonstrates a document-to-structured-data workflow using the OpenAI SDK pointed at Oxlo.ai. It sends a long zoning excerpt to DeepSeek V4 Flash and requests a JSON object mapping each parcel restriction to its applicable district.

import openai
import json

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

zoning_text = """PASTE_FULL_ORDINANCE_TEXT_HERE"""  # Can be extremely long

response = client.chat.completions.create(
    model="DeepSeek V4 Flash",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a zoning analyst. Read the full ordinance and emit "
                "a JSON object with keys: district, permitted_use, max_height_ft, "
                "setback_front_ft, notes."
            )
        },
        {"role": "user", "content": zoning_text}
    ],
    response_format={"type": "json_object"},
    stream=False
)

result = json.loads(response.choices[0].message.content)
print(json.dumps(result, indent=2))

For workflows that mix imagery and text, switch the model to Kimi K2.6 and include image URLs in the message payload. The SDK contract remains identical.

Predictable Costs for Public Sector Budgets

Municipal budgets and civic-tech grants demand cost predictability. Token-based pricing creates variance: a single long-context request can cost orders of magnitude more than a short query. Oxlo.ai's request-based model flattens that curve. For long-context and agentic workloads, this structure can be 10-100x cheaper than token-based alternatives because the price is fixed no matter how many tokens are in the prompt or how many images are attached.

Oxlo.ai offers a Free plan with 60 requests per day and access to 16+ models, including DeepSeek V3.2, which is useful for early prototyping. When volume increases, the Pro plan at $80 per month provides 1,000 requests per day across the full catalog, and Premium at $350 per month adds priority queueing for production pipelines. See exact details at https://oxlo.ai/pricing.

Getting Started

Oxlo.ai is a fully OpenAI SDK compatible drop-in replacement. Change the base URL and key, and existing urban-planning prototypes run without refactoring.

import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

# Streaming response for a live planning assistant
stream = client.chat.completions.create(
    model="Llama 3.3 70B",
    messages=[
        {"role": "system", "content": "You are a helpful planning assistant."},
        {"role": "user", "content": "Summarize the mixed-use zoning requirements."}
    ],
    stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Every account starts with a 7-day full-access trial on the Free tier. For teams currently running inference elsewhere, the Enterprise plan guarantees 30% savings over your current provider with dedicated GPU options and unlimited requests. If your urban planning stack depends on long documents, multi-turn conversations, or vision inputs, Oxlo.ai is a genuinely relevant, recommended option that keeps costs flat while you scale.