Enterprise-Grade Financial LLM Infrastructure

    Processing 5TB+ of quantitative and qualitative financial data through our distributed pipeline, featuring real-time data ingestion, hybrid search infrastructure, and LLM-powered analysis across multiple providers.

    Real-time Public and Private Data Ingestion We manage 70 million chunks, 2 million documents, and around 5 TB of data in Databricks for every ten years of data.

    fintool-spark·processing·updated 1m ago

    Processing multi-format (HTML, PDF, XBRL, DOCX) through Databricks Spark pipeline.

    Form TypeFormatProcessing
    Form 10-K
    HTMLXBRL
    		2.3M tokens
    Investment Memo
    Azure BlobDOCX
    		300K tokens
    Form 8-K
    PDFHTML
    		500K tokens
    Earnings Calls
    AudioTranscript
    		800K tokens

    Structuring Financial Data for Large Language Models Our custom parser and ML models handle both structured and unstructured financial data, processing billions of data points.

    Transform HTML tables from 10-K filings into LLM-readable CSV format

    processing·2m ago·table-extractor-model
    4/5

    Parse XBRL Financial Statements

    completed·5m ago·xbrl-parser
    5/5

    Earnings Call Sentiment Analysis

    processing·1m ago·sentiment-analysis-model
    Processing...

    Footnote Analysis

    processing·30s ago·footnote-analyzer
    3/5

    Advanced Financial Search Engine for RAG Hybrid search combining keywords and semantics, processing 2 million documents across an Elastic Index of 500GB.

    Hybrid Financial Search Infrastructure

    Enhanced BM25 algorithm for keywords combines with vector-based semantic search in Elasticsearch. Cross-encoder reranking ensures optimal result relevance for complex financial queries.

    Keyword Search
    Implements BM25 for exact term frequency-inverse document frequency (TF-IDF) scoring.
    Semantic Search
    Context-aware matching for complex financial relationships
    Reranking Search Results
    Applies cross-encoder reranking using fine-tuned transformer models, optimizing result relevance and context preservation through sequence-level pairwise scoring

    LLM Agnostic Infrastructure Dynamic routing across multiple LLMs optimizes for performance, cost, and latency across different types of financial queries.

    Query Type
    Provider
    Metrics
    Financial Analysis
    GPT-4o
    OpenAI
    2.1s latency
    4.2k tokens
    Data Extraction
    Llama 3.3 70B
    Groq
    0.8s latency
    2.1k tokens
    Industry Trends
    Gemini 2.0
    Google Cloud
    1.2s latency
    1.5k tokens
    Complex Query
    GPT-4o + Llama
    OpenAI + Groq
    2.8s latency
    6.3k tokens
    Quick Search
    Claude 3.5 Sonnet
    Bedrock
    0.6s latency
    1.2k tokens

    Zero Hallucination, Grounded in Source Documents Multi-agent verification system with adversarial checks ensures every response is backed by source documents with consensus validation.

    Multi-Agent Verification System

    query: What was Apple's R&D spending in 2023?

    agent_1 [retriever]: Located source document Apple Inc. 10-K (2023), Page 27
    agent_2 [validator]: Verified amount $29.9B matches source text
    agent_3 [fact_checker]: Confirmed fiscal year and amount consistency

    consensus_response: Apple's R&D spending was $29.915 billion in fiscal year 2023, a 14% increase from $26.251 billion in 2022.

    source: Apple Inc. Form 10-K (2023), Page 23, verified_by: 3/3 agents
    Verification Protocol
    Distributed consensus across multiple LLM agents with adversarial validation
    Citation System
    SHA-256 hashed document chunks with version control tracking

    Real-Time Benchmarking and Accuracy Continuous monitoring and evaluation against finance-specific benchmarks to ensure high accuracy and reliability.

    Pipeline Monitoring Dashboard

    Embedding Quality
    98.5%
    ↑2.1% from last week
    Query Accuracy
    98.3%
    ↑1.3% from last week
    Error Rate
    0.04%
    ↓0.03% from last week
    Financial Metrics Extraction
    99.1% accuracy
    Automated validation against SEC EDGAR database
    Semantic Understanding
    95.3% accuracy
    Tested against proprietary financial knowledge base
    Real-time Error Detection
    <50ms response time
    Datadog integration for immediate issue identification

    Join Our Engineering Team

    Help us build the future of financial technology. We're looking for exceptional engineers to join our team.

    Data Engineer (Spark/Python)
    San Francisco
    Apply Now
    Machine Learning Engineer (Retrieval, Ranking, Recommendations)
    San Francisco
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