Leveraging LLMs for Text Classification and Clustering in Business

Text classification and clustering remain foundational tasks for business intelligence, customer support, and content operations. Traditional machine learning pipelines require extensive feature engineering, labeled datasets, and ongoing retraining. Large language models collapse that complexity into API calls, turning unstructured text into structured decisions and coherent groupings without managing model weights or training loops. For teams running these workloads at scale, the inference platform matters as much as the model itself.

Why LLMs for Text Classification and Clustering

Classical approaches like TF-IDF with logistic regression or LDA for topic modeling demand domain-specific preprocessing and stopword lists. LLMs generalize across domains because they are pretrained on broad corpora. A single model can classify legal contracts, medical notes, or customer feedback with only a prompt change. For clustering, embedding models map text into dense vector spaces where semantic similarity replaces lexical overlap, capturing paraphrased concepts that keyword methods miss.

The shift is architectural, not just incremental. Instead of maintaining separate models per task, teams can route text through a unified inference endpoint. Oxlo.ai provides 45+ open-source and proprietary models across seven categories, including LLMs, embeddings, and vision models, all through a fully OpenAI SDK compatible API. This means existing Python scripts need only a base URL change to run on Oxlo.ai.

Zero-Shot Classification with Structured Outputs

Zero-shot classification removes the need for training data. You describe the labels in the prompt and constrain the output format. Oxlo.ai supports JSON mode, which lets you enforce valid JSON schemas rather than parsing freeform text.

Below is a minimal example using the OpenAI SDK. Replace MODEL with any LLM from the Oxlo.ai catalog, such as Llama 3.3 70B or Qwen 3 32B.

from openai import OpenAI
import os

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

CLASSIFICATION_PROMPT = """Classify the following support ticket into exactly one category: Billing, Technical, Account, or General.
Respond with valid JSON in this format: {"category": "...", "confidence": "low|medium|high"}"""

def classify_ticket(text: str) -> str:
    response = client.chat.completions.create(
        model=MODEL,  # e.g., Llama 3.3 70B, Qwen 3 32B, or DeepSeek V3.2
        messages=[
            {"role": "system", "content": CLASSIFICATION_PROMPT},
            {"role": "user", "content": text}
        ],
        response_format={"type": "json_object"},
        max_tokens=128,
        temperature=0.1
    )
    return response.choices[0].message.content

ticket = "My invoice shows duplicate charges for March and the refund link is broken."
print(classify_ticket(ticket))

Running this against a long ticket history does not require provisioning GPUs or managing batch queues. Oxlo.ai offers no cold starts on popular models, so latency stays predictable even when processing thousands of tickets in a loop.

Clustering with Embedding Models

When categories are unknown, clustering discovers structure. The standard pipeline is simple: embed documents, reduce dimensionality if desired, and apply an algorithm such as KMeans or HDBSCAN. Oxlo.ai hosts embedding models including BGE-Large and E5-Large, which are optimized for semantic retrieval and grouping tasks.

The following example fetches embeddings through the Oxlo.ai API and clusters them with scikit-learn.

from openai import OpenAI
import numpy as np
from sklearn.cluster import KMeans

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

EMBED_MODEL = "your-embedding-model"  # BGE-Large or E5-Large from Oxlo.ai

def embed_texts(texts: list[str]) -> np.ndarray:
    response = client.embeddings.create(
        model=EMBED_MODEL,
        input=texts
    )
    return np.array([d.embedding for d in response.data])

documents = [
    "Quarterly revenue exceeded expectations due to enterprise renewals.",
    "The new API gateway reduced P99 latency by forty percent.",
    "Customer churn spiked in the freemium tier after the pricing change.",
    "Deploying the model on Kubernetes improved autoscaling response."
]

vectors = embed_texts(documents)
kmeans = KMeans(n_clusters=2, random_state=42, n_init=10).fit(vectors)

for doc, label in zip(documents, kmeans.labels_):
    print(f"Cluster {label}: {doc}")

Because the Oxlo.ai API is fully OpenAI SDK compatible, you can reuse existing embedding utilities, vector store connectors, and LangChain components without rewriting integration logic.

Business Applications and Workflows

Classification and clustering are not research exercises. They power operational workflows.

• Support ticket routing. Instantly assign incoming messages to the correct team using zero-shot classification.
• Sentiment and intent analysis. Tag CRM records with structured attributes derived from call transcripts or chat logs.
• Document organization. Cluster internal wikis, legal briefs, or RFP responses to surface duplicate content and knowledge gaps.
• Compliance scanning. Classify communications into risk categories without maintaining brittle regex rules.

For workflows that combine steps, such as classifying a document and then summarizing it for a dashboard, request-based pricing keeps costs transparent. You know the cost per step, and that cost does not balloon when the input grows from a hundred tokens to ten thousand.

Cost and Scaling Considerations

Token-based pricing penalizes long-context tasks. A classification request that includes a full customer conversation history or a lengthy legal excerpt can consume thousands of input tokens. On token-based providers, that directly multiplies the bill. Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context classification and large batch embedding jobs, this can be significantly cheaper than token-based alternatives.

See the Oxlo.ai pricing page for plan details. The Free tier includes 60 requests per day and access to more than 16 models, including DeepSeek V3.2, which is sufficient for prototyping. Production workloads can scale through Pro, Premium, or Enterprise tiers, with the Enterprise plan offering dedicated GPUs and a guaranteed 30 percent reduction versus your current provider.

Implementation Checklist

Before moving to production, validate the following:

1. Label stability. Run the same prompt across diverse samples and verify that the model respects your JSON schema.
2. Embedding quality. Compute silhouette scores for clusters to confirm that the chosen embedding model separates your domains effectively.
3. Latency budgets. Measure end-to-end time for your longest expected input. Oxlo.ai avoids cold starts on popular models, but you should still benchmark against your SLA.
4. Cost projection. Estimate daily request volume and compare request-based pricing to token-based alternatives for your typical input lengths.

Conclusion

LLMs turn text classification and clustering from pipeline engineering into API design. By using structured outputs for classification and embedding endpoints for clustering, teams can ship faster without sacrificing accuracy. Oxlo.ai supports this transition with a broad model catalog, OpenAI SDK compatibility, and request-based pricing that favors long-context business workloads. If you are evaluating inference providers for your next text analytics project, the flat per-request model on Oxlo.ai is a relevant option worth testing.