Unlocking LLM Potential in Finance

Financial institutions sit on terabytes of unstructured text. Annual reports, regulatory filings, earnings call transcripts, and legal contracts contain signals that traditional rule-based systems miss. Large language models can extract these signals, but production deployment in finance is often blocked by unpredictable inference costs and integration friction. When a single 10-K filing can span hundreds of thousands of tokens, token-based billing turns document analysis into a budget risk. A request-based pricing model removes that uncertainty, letting engineering teams scale LLM workloads by transaction count rather than by character.

The Long-Context Document Problem

Equity research, credit analysis, and legal due diligence all require reading documents that exceed the context windows of early LLMs. A typical annual report, supplemented with footnotes and auditor commentary, can push well past 100,000 tokens. Under token-based pricing, every paragraph inflates the bill before the model generates a single character of analysis.

Oxlo.ai uses flat per-request pricing. One API call costs the same whether you send a one-line prompt or an entire mortgage-backed security prospectus. This is particularly relevant for long-context models such as DeepSeek V4 Flash, which supports a 1 million token context window, and Kimi K2.6, which offers a 131K context window with advanced reasoning and vision capabilities. You can feed an entire filing plus a chain of analyst questions into a single request, knowing the cost is fixed.

For compliance teams, this means scanning ISDA agreements or Basel regulatory text without truncating content to save money. For quantitative researchers, it means submitting full historical strategy whitepapers as prompts. The economics shift from cost-per-token to cost-per-insight.

Agentic Research and Compliance Pipelines

Modern finance workflows are rarely single-shot. A research agent might retrieve a filing, extract named entities, cross-reference them against a sanctions list, summarize risk factors, and draft an email alert. Each step is a discrete LLM call with tool use.

Oxlo.ai supports function calling, multi-turn conversations, and streaming responses, so these agentic pipelines run without architectural hacks. Models such as GLM 5, a 744B parameter MoE built for long-horizon agentic tasks, and Qwen 3 32B, which is optimized for multilingual reasoning and agent workflows, can maintain coherence across dozens of tool calls. Because Oxlo.ai delivers no cold starts on popular models, an agent that wakes up every fifteen minutes to check for new SEC filings will not stall on the first request.

Compliance officers can build agents that parse transaction logs, flag anomalies via JSON mode, and invoke internal APIs to file tickets. The request-based model again protects the budget: an agent that loops through ten tool calls costs ten requests, not ten times the token overhead of repeating system prompts.

Quantitative Coding and Strategy Prototyping

Beyond natural language, LLMs are increasingly used to generate and debug the code that powers trading algorithms, risk engines, and portfolio optimizers. Oxlo.ai offers dedicated code models including Qwen 3 Coder 30B, DeepSeek Coder, Oxlo.ai Coder Fast, and Minimax M2.5, which is tuned for coding and agentic tool use. For deep reasoning tasks, DeepSeek R1 671B MoE and DeepSeek V3.2 can walk through complex statistical logic before writing the implementation.

Integration is straightforward. If you already use the OpenAI Python SDK, you point the client at Oxlo.ai and select a code-capable model.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

response = client.chat.completions.create(
    model="deepseek-r1-671b",
    messages=[{
        "role": "user",
        "content": "Write a Python function that calculates maximum drawdown from a pandas Series of daily returns. Include docstrings and input validation."
    }],
    stream=False
)

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

Because the endpoint is fully OpenAI SDK compatible, existing quant research notebooks need only a base URL change to experiment with open-source reasoning models. There is no need to rewrite wrapper logic or manage custom authentication flows.

Structured Output for Trading and Risk Systems

Trading desks and risk management platforms do not consume prose. They need schemas: ISIN codes, confidence scores, sentiment labels, or volatility predictions in JSON. Oxlo.ai supports JSON mode and function calling, so you can constrain model output to a predictable structure.

This matters for downstream pipelines. A sentiment analysis model can return a flat JSON object with fields for ticker, sentiment, magnitude, and cited text spans. A credit risk model can emit structured macro indicators. When combined with the flat request pricing, you can process high volumes of small structured extractions without watching token counters increment on every comma.

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{
        "role": "system",
        "content": "You are a financial entity extractor. Respond only with valid JSON."
    }, {
        "role": "user",
        "content": "Extract all company names, ticker symbols, and forward-looking statements from the following paragraph: ..."
    }],
    response_format={"type": "json_object"}
)

The same pattern works for embeddings. Oxlo.ai offers BGE-Large and E5-Large for building retrieval-augmented generation pipelines over internal research libraries. Chunk documents, embed them via the embeddings endpoint, and store vectors in your existing vector database. The RAG layer feeds concise, relevant context into the chat completions endpoint, keeping each request focused and efficient.

Multimodal Analysis of Financial Media

Not all financial