Building Language Understanding Apps with LLMs

Language understanding applications, from intent classification and entity extraction to semantic search and document summarization, have become the default interface between unstructured data and structured systems. Building these apps requires more than prompting a large model. You need predictable latency, context windows that fit real documents, and pricing that does not explode when user inputs grow. Oxlo.ai provides a developer-first inference platform with request-based pricing and full OpenAI SDK compatibility, making it a practical backbone for production language understanding stacks.

Core Patterns for Language Understanding

Most production LLM services fall into four patterns. Each places different demands on inference infrastructure.

• Classification and routing: Assigning labels, intent detection, or spam filtering. These need low latency and high throughput.
• Structured extraction: Converting free text into JSON, database records, or function arguments. This requires reliable JSON mode and tool use.
• Summarization and synthesis: Distilling long documents into briefs, meeting notes, or action items. Long context windows are essential.
• Retrieval-augmented generation: Combining embedding search with generation to ground answers in private data. You need both embedding and chat endpoints from the same provider.

Oxlo.ai supports all four patterns through a single API. The chat completions endpoint handles classification, extraction, and summarization, while dedicated embeddings and audio endpoints cover retrieval and transcription. Because the platform is fully OpenAI SDK compatible, you can switch existing pipelines to Oxlo.ai by changing the base URL.

Architecture and Latency Considerations

Language understanding workloads are often input-heavy. A customer support classifier may ingest thousands of tokens per ticket, and a legal discovery pipeline may process entire briefs. Under token-based pricing, costs scale linearly with document length, which encourages aggressive truncation and brittle chunking strategies that hurt accuracy.

Oxlo.ai uses flat per-request pricing. Whether you send a one-sentence query or a full research paper, the cost is the same per API call. Unlike token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, cost does not scale with input length. For long-context and agentic workloads, this can be 10-100x cheaper. You can see the exact plan structure on the Oxlo.ai pricing page.

Latency matters when these apps serve live users. Oxlo.ai offers no cold starts on popular models, so p99 response times stay stable even during traffic spikes. Streaming responses let you render partial results in the UI while the model finishes generation, which is especially useful for long summarization tasks.

Implementing a Document Understanding Pipeline

The following example uses Python and the OpenAI SDK to extract structured financial data from an earnings call transcript. The code targets Oxlo.ai by setting the base URL to https://api.oxlo.ai/v1.

import openai
import json
import os

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

document = """
Acme Corp Q3 Earnings Call Transcript

CEO: Revenue grew 14 percent year over year, driven by enterprise
adoption of our API platform. Headcount increased by 120 employees
to support the expansion. The primary risk we see is tightening
IT budgets in Europe, which may slow Q4 contract velocity.
CFO: Operating margin improved to 22 percent. We are raising
full-year guidance to 1.2 billion in ARR.
"""

completion = client.chat.completions.create(
    model="llama-3.3-70b",  # use the Oxlo.ai identifier for Llama 3.3 70B
    messages=[
        {
            "role": "system",
            "content": (
                "You are a financial analyst. Extract the following as JSON: "
                "revenue_growth_pct (number), headcount_change (integer), and "
                "top_risk_factors (array of strings). Respond with valid JSON only."
            )
        },
        {"role": "user", "content": document}
    ],
    response_format={"type": "json_object"}
)

result = json.loads(completion.choices[0].message.content)
print(json.dumps(result, indent=2))

For even longer inputs, such as annual reports or codebases, you can switch to a model with an extended context window. DeepSeek V4 Flash on Oxlo.ai supports a 1 million token context and efficient MoE inference, letting you analyze large corpora in a single request without chaining multiple calls.

If you want to stream the response to the client, add stream=True:

stream = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "Summarize the document in two sentences."},
        {"role": "user", "content": document}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Cost Control with Request-Based Pricing

When you build language understanding apps on token-based platforms, every extra sentence in a user prompt or every page of a PDF adds metered cost. This creates a tension between accuracy, which benefits from full context, and budget, which benefits from truncation.

Oxlo.ai removes that tension. Because you pay per request, not per token, you can send the full document, maintain longer conversation histories, and build agentic loops that call tools multiple times without watching a token meter spin. The pricing structure is straightforward: the Free plan offers 60 requests per day across more than 16 models with a 7-day full-access trial, while paid plans provide higher daily volumes and priority queue access. For exact tiers, see the pricing page.

This predictability also simplifies capacity planning. If your app processes ten thousand documents per day, your inference cost is fixed regardless of whether those documents are short emails or hundred-page contracts.

Selecting the Right Model on Oxlo.ai

Oxlo.ai hosts more than 45 open-source and proprietary models across seven categories. For language understanding tasks, the following are strong starting points:

• General extraction and classification: Llama 3.3 70B is the general-purpose flagship. It balances latency and accuracy for structured JSON extraction and summarization.
• Long-document analysis: DeepSeek V4 Flash supports a 1 million token context and efficient MoE inference, making it ideal for analyzing books, codebases, or large conversation histories in one shot.
• Multilingual and agentic workflows: Qwen 3 32B handles multilingual reasoning and agent workflows, which is useful when your pipeline spans multiple languages or calls external APIs via function calling.
• Advanced reasoning and coding: DeepSeek R1 671B MoE, Kimi K2.6, and Kimi K2 Thinking provide deep chain-of-thought reasoning for complex interpretation tasks, such as parsing regulatory text or generating structured logic from natural language.
• Vision documents: If your inputs include scanned PDFs or images, Gemma 3 27B and Kimi VL A3B offer vision capabilities through the same chat completions endpoint.
• Retrieval and semantic search: BGE-Large and E5-Large embedding models feed vector search layers that supply context to Oxlo.ai LLMs in RAG pipelines.
• Audio understanding: Whisper Large v3, Whisper Turbo, and Whisper Medium transcribe audio into text that downstream LLMs can process.

All of these models share the same base URL and SDK interface, so you can experiment with different backends without rewriting client code.

Putting It into Production

Building reliable language understanding apps comes down to three decisions: choosing the right model, structuring outputs with JSON mode and function calling, and controlling costs as inputs scale. Oxlo.ai gives you a fully OpenAI-compatible API with no cold starts on popular models, so you can drop it into existing Python or Node.js codebases without rewriting client logic.

Because pricing is per request rather than per token, you can design for accuracy first and compression second. Point your OpenAI SDK client to https://api.oxlo.ai/v1, select a model that matches your context and reasoning requirements, and run your first extraction pipeline today.