Powerful Financial LLM API

    Access comprehensive financial data and AI insights through our robust API. Build powerful applications with real-time market data, company financials, and more.

    PwCUBSJanusVaughan NelsonSilverpoint Capital

    Easy Integration based on OpenAI specs

    Get started quickly with our well-documented API. Simple authentication, clear endpoints, and comprehensive examples in Python and Javascript.

    python3- fintool_example.py
    from fintool.ai import ( CompanyCompletion, MultiQuestionRequest, Period, Question, multi_question_agent )# Initialize the clientclient = FinToolClient(api_key="your_api_key")# Create a multi-question requestrequest = MultiQuestionRequest( company=[ CompanyCompletion( id="AAPL", company_name="Apple Inc.") ], periods=[Period(latest=True)], questions=[ Question(text="What are the main revenue drivers?"), Question(text="Analyze recent performance trends") ] )# Execute the requestresponse = await client.analyze(request) print(response.content)# Example Response:{"content": {"revenue_drivers": "iPhone remains Apple's primary revenue driver, contributing approximately 52% of total revenue. Services segment is the second-largest contributor, followed by Mac, Wearables, and iPad.","performance_trends": "Recent quarters show strong growth in Services and Wearables segments. iPhone sales remain robust despite market maturity. Geographic expansion in emerging markets driving additional growth."}}

    Advanced Function Calling

    Define custom functions and let our AI automatically call them based on context. Compare footnotes across years of SEC filings, analyze earnings call sentiment trends, and extract specific financial metrics. Perfect for deep financial analysis, regulatory compliance, and automated research workflows.

    python3- function_calling.py
    from fintool.ai import FinToolClient client = FinToolClient(api_key="your_api_key")# Define available functionstools = [{"type": "function","function": {"name": "compare_footnotes","description": "Compare specific footnotes across multiple 10-K filings","parameters": {"type": "object","properties": {"ticker": {"type": "string","description": "Company ticker symbol"},"footnote_topic": {"type": "string","description": "Topic to compare (e.g., Revenue Recognition, Leases)"},"years": {"type": "array","items": {"type": "integer"}}}}}]# Analyze changes in revenue recognition policiesresponse = await client.analyze("Compare Tesla's revenue recognition footnotes between 2021-2023", tools=tools )# Example Response:{"function_call": {"name": "compare_footnotes","arguments": {"ticker": "TSLA","footnote_topic": "Revenue Recognition","years": [2021, 2022, 2023]}},"result": {"changes_detected": true,"summary": "2023 introduced new FSD revenue recognition policy. 2022 expanded service revenue details. No material changes in 2021.","risk_level": "medium"}}

    Enterprise Performance

    Built for enterprise-scale with industry-leading performance. Our infrastructure handles 10,000+ requests per second with 99.99% uptime SLA, sub-150ms response times, and unlimited tokens per minute for enterprise customers. Dedicated infrastructure ensures consistent performance during market hours and high-volume periods.

    99.99%
    Uptime SLA
    <150ms
    Average Latency
    10k+
    Requests/Second
    Unlimited tokens per minute for enterprise plans
    Dedicated infrastructure for enterprise customers
    24/7 infrastructure monitoring and auto-scaling
    Global CDN with edge caching for faster responses

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