Leveraging LLMs for Decision Support

#aiinfrastructure #oxlo #ai

Decision support systems do not run on isolated prompts. They consume large corpora of policy documents, historical logs, and real-time telemetry, then reason across multiple turns and external tools before returning a structured recommendation. That volume of context and interaction exposes a hidden cost in token-based inference: every additional paragraph of background and every follow-up question expands the bill. Oxlo.ai removes that variable with request-based pricing, charging one flat cost per API call regardless of how much context the model must read.

Structured Reasoning for Complex Decisions

For high-stakes decisions, raw text generation is insufficient. You need transparent reasoning and verifiable outputs. Modern open-source models expose chain-of-thought reasoning that can be inspected before the final answer is returned. Oxlo.ai hosts several options built for this pattern, including DeepSeek R1 671B MoE, Kimi K2 Thinking, Kimi K2.5, and DeepSeek V4 Flash. These models break problems into intermediate steps, which is useful when the system must justify a loan approval, a medical triage suggestion, or a supply-chain re-routing.

Structured output is equally important. Oxlo.ai supports JSON mode on chat completions, so you can constrain the model to emit schema-valid objects rather than free-form prose. When combined with multi-turn conversations, this lets a decision-support agent ask clarifying questions, receive answers, and ultimately return a structured decision record.

Tool Use and Agentic Workflows

Real decisions rarely live inside a single context window. They require calculators, databases, and simulation APIs. Oxlo.ai supports function calling and tool use across its LLM suite, including GLM 5, Minimax M2.5, and Qwen 3 32B. A decision pipeline can delegate arithmetic to Python, fetch live inventory from an internal API, or run a risk model before the LLM synthesizes the final recommendation.

Because Oxlo.ai is fully OpenAI SDK compatible, you can drop the same tool-use patterns from OpenAI into Oxlo.ai with only a base URL change. Streaming responses are also supported, so a decision-support dashboard can show reasoning as it happens rather than blocking on a full generation.

The Cost of Context in Decision Support

The biggest operational surprise in production decision systems is context bloat. A single decision might require fifty pages of regulatory text, ten prior chat turns, and several tool results. On token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, longer inputs directly multiply cost.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For decision-support workloads that ingest long documents or maintain extended agentic sessions, this can be 10-100x cheaper than token-based billing. You do not need to truncate source material or compress history to save money. You can send the full context that the model actually needs to decide correctly.

Exact plan details are available at https://oxlo.ai/pricing. The Free tier offers 60 requests per day and includes more than 16 models, with a 7-day full-access trial to evaluate longer pipelines. Paid plans scale from 1,000 to 5,000 requests per day, and Enterprise customers can move to dedicated GPUs with unlimited volume.

Implementing a Decision Pipeline

Below is a minimal Python example that uses Oxlo.ai to perform a structured decision. It reads a long policy document, asks the model to reason, and returns JSON. Because pricing is per request, the length of policy_text does not affect the cost.

import os
from openai import OpenAI

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

policy_text = """... potentially thousands of tokens of regulatory text ..."""

response = client.chat.completions.create(
    model="deepseek-r1-671b",
    messages=[
        {
            "role": "system",
            "content": "You are a decision-support assistant. Reason step by step, then emit a JSON object with keys: decision, confidence, rationale."
        },
        {
            "role": "user",
            "content": f"Based on the following policy, should we approve this claim?\n\n{policy_text}"
        }
    ],
    response_format={"type": "json_object"},
    stream=False
)

result = response.choices[0].message.content
print(result)

The same pattern extends to tool use. Define your functions in the OpenAI SDK format, pass them to the chat completions endpoint, and the model will request the tools it needs before producing its final structured answer.

Model Selection for Decision Tasks

Oxlo.ai carries more than 45 models across seven categories. For decision support, the most relevant are:

DeepSeek R1 671B MoE and DeepSeek V4 Flash: deep reasoning, complex coding, and long-context analysis.
Kimi K2.6: advanced reasoning, agentic coding, vision, and 131K context for multimodal decision inputs.
GLM 5: 744B MoE built for long-horizon agentic tasks that span many steps.
Qwen 3 32B: multilingual reasoning and agent workflows for global policy environments.
Llama 3.3 70B: general-purpose flagship when you need a balanced default.
DeepSeek V3.2: strong coding and reasoning; available on the free tier for prototyping.

If your pipeline also requires embedding retrieval, Oxlo.ai offers BGE-Large and E5-Large through the embeddings endpoint. For multimodal decisions, Kimi VL A3B and Gemma 3 27B accept image inputs.

Getting Started

You can migrate an existing decision-support prototype to Oxlo.ai in minutes. Swap the base_url to https://api.oxlo.ai/v1, keep your existing OpenAI SDK code, and run. There are no cold starts on popular models, so latency remains predictable under load.

Start with the Free plan to test JSON mode, function calling, and long-context behavior without a credit card. When you move to production, request-based pricing keeps costs flat even as your context windows grow. Visit https://oxlo.ai/pricing to compare plans, or contact the team for Enterprise dedicated GPU deployments.

DEV Community