LLM & RAG data pipelines

Financial Data for LLMs and RAG Pipelines

Build financial AI applications that retrieve and reason over real data: structured fundamentals, full-text filings, earnings transcripts, and news feeds accessible via a clean API, ready to slot into retrieval-augmented generation and LLM workflows.

Structured, text & time-series

Retrieval-Ready Data

Financial statements, earnings transcripts, SEC filings, financial news, and economic data: a diverse corpus of structured and unstructured financial content for indexing into vector stores and retrieval pipelines.

Live quotes, news, calendars

Real-Time Data Grounding

Real-time quotes, financial news feeds, earnings calendars, and economic events: live data to ground LLM responses in current market conditions rather than stale training data.

What you can build

LLM and RAG data pipelines powered by FMP.

Document corpus for RAG

Earnings transcripts, 10-K and 8-K SEC filings, press releases, and financial news: rich source documents for indexing into vector databases and retrieval pipelines.

Structured data for tool use

Financial statements, ratios, quotes, analyst estimates, and economic indicators as structured tool outputs for LLM agents that reason over live financial data.

Real-time grounding

Live quotes, news, and market events to keep LLM responses anchored in current market conditions, preventing AI answers based on outdated context.

Historical context for fine-tuning

Multi-year archives of financial documents, transcripts, and structured data for building domain-specific training sets and evaluating financial AI model performance.

API-First, Pipeline-Friendly

Clean, consistently structured REST API responses across all data types, minimising the preprocessing work between data retrieval and embedding or prompt construction.

Consistent formatting

Programmatic access to both historical archives and real-time feeds, covering the full data freshness spectrum that financial AI applications require.

Historical & live

Financial LLM applications need more than a language model. They need accurate, retrievable financial data. FMP provides the data layer that makes AI responses grounded and trustworthy.