Building LLM-Based Decision Support Systems

Decision support systems combine data, models, and human expertise to guide complex choices. Large language models extend these systems from static dashboards to interactive reasoning engines that can parse unstructured reports, invoke external tools, and explain their logic in natural language. Building a production-grade LLM-based decision support pipeline requires more than prompt engineering. It demands careful architecture, structured outputs, function calling, and an inference backend that remains predictable when context windows grow.

Core Architecture Patterns

A robust LLM decision support stack usually combines three layers: retrieval, reasoning, and action. Retrieval grounds the model in proprietary data through vector search or structured queries. Reasoning leverages chain-of-thought or multi-step agent workflows to break down complex decisions. Action uses function calling to interact with calculators, databases, or simulation APIs.

For multi-step workflows, agentic patterns work well. A controller model delegates tasks to specialized sub-models: one for data extraction, another for risk scoring, a third for generating recommendations. Each step can target a different model family depending on latency and capability requirements.

Implementation with Tool Use and Structured Outputs

Decision systems must return machine-readable outputs, not just conversational text. JSON mode and strict function schemas let you integrate LLM responses directly into downstream business logic.

Below is a minimal example using the Oxlo.ai API, which is fully compatible with the OpenAI SDK. The example defines a tool for querying a sales database and requests a structured recommendation.

import os
import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "query_sales_metrics",
            "description": "Retrieve QTD revenue and churn by region",
            "parameters": {
                "type": "object",
                "properties": {
                    "region": {"type": "string", "enum": ["NA", "EU", "APAC"]}
                },
                "required": ["region"]
            }
        }
    }
]

messages = [
    {"role": "system", "content": "You are a decision support assistant. Recommend pricing strategy based on regional sales data. Respond in JSON."},
    {"role": "user", "content": "APAC revenue is soft. Should we discount or bundle?"}
]

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=messages,
    tools=tools,
    tool_choice="auto",
    response_format={"type": "json_object"}
)

# The model may call the tool or return a direct JSON recommendation.
print(response.choices[0].message)

By combining tool use with JSON mode, the system can verify inputs against a schema before any database query executes. This reduces hallucinated parameters and simplifies auditing.

Inference Infrastructure and Cost Control

Decision support workloads often ingest long documents: regulatory filings, patient histories, or multi-year transaction logs. On token-based platforms, costs scale linearly with input length, making deep context prohibitively expensive for high-volume decision pipelines.

Oxlo.ai uses request-based pricing: one flat cost per API call regardless of prompt length. For long-context and agentic workloads that pass large retrieved documents into every step, this model is significantly more predictable than token-based alternatives. You can design systems that pass full context to each sub-agent without watching token meters accumulate on every turn.

Oxlo.ai also exposes 45+ models across reasoning, code, vision, and embeddings, all through a single OpenAI-compatible endpoint. There are no cold starts on popular models, so latency remains stable even when routing between different models within the same decision workflow.

Selecting Models for Decision Support

Different stages of a decision pipeline need different capabilities. Oxlo.ai offers several options without requiring separate provider accounts.

• DeepSeek R1 671B MoE: Deep reasoning and complex coding. Ideal for risk analysis or multi-factor optimization where step-by-step logic matters.
• Llama 3.3 70B: General-purpose flagship. A strong default for routing, summarization, and structured extraction.
• Qwen 3 32B: Multilingual reasoning and agent workflows. Use this when decisions involve non-English source documents or cross-lingual compliance checks.
• GLM 5 744B MoE: Long-horizon agentic tasks. Suitable for extended planning workflows with many tool calls.
• Kimi K2.6: Advanced reasoning with 131K context and vision. Helpful when decisions require reading scanned reports