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shashank ms
shashank ms

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Practical Applications of LLM for Text Classification and Clustering in Business

#product #oxlo #ai

Text classification and clustering remain two of the highest-impact applications of large language models in enterprise environments. Traditional machine learning pipelines for NLP require extensive labeled datasets, feature engineering, and ongoing maintenance as language evolves. Modern LLMs collapse this complexity into a single API call, enabling teams to classify support tickets, cluster customer feedback, or route documents using zero-shot prompts or embedding-based pipelines. For teams running these workloads at scale, inference cost and context limits determine whether a project is economically viable. Oxlo.ai offers a developer-first platform with request-based pricing and a broad model catalog that is particularly well suited to high-volume text analysis.

Moving Beyond Traditional NLP for Classification

Businesses rarely have perfectly clean training data. LLMs excel at zero-shot classification, where the model assigns one of several predefined labels to a piece of text without any task-specific fine-tuning. This is ideal for support ticket routing, compliance flagging, sentiment analysis, and document triage.

Because Oxlo.ai is fully OpenAI SDK compatible, you can drop the base URL into existing scripts and start classifying immediately. The example below sends a support ticket to Llama 3.3 70B and requests structured JSON output.

import os
from openai import OpenAI

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

ticket = "Refund not processed after return window closed, but item was defective."
categories = ["Billing", "Technical Issue", "Refund Request", "Account Access"]

response = client.chat.completions.create(
    model="Llama 3.3 70B",
    messages=[
        {"role": "system", "content": "You classify support tickets. Return JSON with keys: category, reasoning."},
        {"role": "user", "content": f"Categories: {categories}\n\nTicket: {ticket}"}
    ],
    response_format={"type": "json_object"}
)

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

For multilingual teams, Qwen 3 32B handles mixed-language inputs and agentic workflows, so a single classifier can operate across global support queues without separate models per region.

Clustering Unstructured Text with Embeddings

Classification requires predefined labels. Clustering, by contrast, discovers hidden structure in unlabeled text. The standard production pattern is to generate embeddings, cluster them with an algorithm such as KMeans or HDBSCAN, and then use an LLM to generate human-readable labels for each cluster.

Oxlo.ai provides dedicated embedding endpoints through models such as BGE-Large and E5-Large. The snippet below embeds a small set of support tickets, clusters them, and prints the groupings.

import os

from openai import OpenAI

from sklearn.cluster import KMeans


client = OpenAI(

    base_url="https://api.ox

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