Unlocking the Power of Text Analysis with LLMs

Moving Beyond Keyword Search

Text analysis has outgrown regex and bag-of-words pipelines. Modern workloads require entity extraction from lengthy legal contracts, sentiment classification across multilingual support tickets, and structured summarization of research papers. Large language models handle these tasks zero-shot, but the infrastructure choices behind them determine whether your pipeline is cost-effective or whether long inputs destroy your budget.

Structured Extraction with JSON Mode

One of the most reliable patterns in LLM text analysis is structured extraction. Instead of parsing freeform prose, you constrain the model to return valid JSON. This turns unstructured documents into database-ready records without brittle postprocessing.

Oxlo.ai supports JSON mode across its chat and reasoning models, including Llama 3.3 70B, Qwen 3 32B, and DeepSeek V3.2. Because the platform is fully OpenAI SDK compatible, you can enforce a schema with the same Python client you already use.

from openai import OpenAI

import os

client = OpenAI(

    base_url="https://api.oxlo.ai/v1",

    api_key=os.environ["OXLO_API_KEY"]

)

response = client.chat.completions.create(

    model="<model-id>",  # e.g., Llama 3.3 70B

    messages=[

        {

            "role": "system",

            "content": "Extract entities and sentiment as JSON with keys: entities, sentiment, summary."

        },

        {

            "role": "user",

            "content": "Paste your long document here..."

        }

    ],

    response_format={"type":