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.
How does Fintool answer financial questions? Fintool orchestrates dozens of parallel operations for comprehensive context retrieval and accurate responses.
1. Context Retrieval
2. Response Generation
3. LLM Processing
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.
Processing multi-format (HTML, PDF, XBRL, DOCX) through Databricks Spark pipeline.
| Form Type | Format | Processing |
|---|---|---|
| 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-modelParse XBRL Financial Statements
completed·5m ago·xbrl-parserEarnings Call Sentiment Analysis
processing·1m ago·sentiment-analysis-modelFootnote Analysis
processing·30s ago·footnote-analyzerAdvanced 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.
LLM Agnostic Infrastructure Dynamic routing across multiple LLMs optimizes for performance, cost, and latency across different types of financial queries.
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?
consensus_response: Apple's R&D spending was $29.915 billion in fiscal year 2023, a 14% increase from $26.251 billion in 2022.
Real-Time Benchmarking and Accuracy Continuous monitoring and evaluation against finance-specific benchmarks to ensure high accuracy and reliability.
Pipeline Monitoring Dashboard
Privacy by Default Enterprise-grade data security and privacy principles built into every layer of our infrastructure.
Principles for data security & privacy
1Data isolation
Private docs are isolated between organizations. We don't mingle them in the same Postgres table or Elastic index. This offers guarantees that a user from firm A will never see data from firm B.
2Control
The org admin can change the configuration of the connector at any time. When that happens, we do a full sync to make sure Fintool only see what we're explicitly allowed to see.
3Access control
Each document, each data chunk is stored with its up-to-date access rights. Whenever we query a Postgres table or Elastic index, the user must provide his identity with the connector. In essence, if you can't access a document within your org's sharepoint, Fintool AI won't see it.
How we enforce these principles
1Organization Administrator Control
Each org has a single admin — the admin is the person that sets up the connectors
- id
- datasource: str — e.g. sharepoint
- admin_sub: str — e.g. 66d8fy7gvaf53e2c0aea7512e
- paths: List[str] — the absolute paths that Fintool can access
- auth: Dict — the auth information we need to create the connector — connector specific (e.g. tenant-id, …)
2Data Pipeline Artifacts
The data pipeline creates isolated storage artifacts for each organization
- Isolated data stores per organization
- Full document metadata for traceability
- User and group permission enforcement
- Quantized embeddings for efficient search
Want to know even more? Leading AI companies featured Fintool in their technical case studies.
Evaluating millions of LLM outputs
How Fintool used Braintrust to develop a repeatable evaluation workflow that scales to massive amounts of data while maintaining trust in high-stakes financial contexts.
Fast Inference with Custom LPUs Chips
How Fintool achieved 7.41x faster processing speed and 89% cost reduction by moving its LLM infrastructure to GroqCloud for financial analysis.
LLM Observability at Scale across Models
How Fintool identifies LLM performance bottlenecks in real-time across a multi-provider, multi-model LLM infrastructure.
Join Our Engineering Team
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