Koyfin
Professional-grade financial data and analytics at a fraction of the cost of a Bloomberg Terminal.

The AI-powered investment research platform for verified financial data and institutional-grade insights.
FinChat is a specialized AI platform engineered for the investment community, positioning itself as a vertically integrated alternative to general-purpose LLMs like ChatGPT. Its technical architecture utilizes Retrieval-Augmented Generation (RAG) mapped specifically to a curated, high-integrity financial database covering over 100,000 public companies globally. By 2026, FinChat has solidified its market position by solving the 'hallucination problem' in finance through mandatory source-tagging and direct links to SEC filings, earnings transcripts, and official press releases. The platform operates as a decision-support layer for analysts, providing the ability to parse complex segment data, track custom KPIs, and perform cross-company benchmarking in natural language. Its backend integrates real-time equity pricing and historical financial statements, allowing users to generate complex visualizations and valuation models instantly. Unlike horizontal AI tools, FinChat's domain-specific fine-tuning ensures it understands financial nomenclature (e.g., EBITDA, ROIC, organic growth) with the nuance required for institutional-grade due diligence. It effectively bridges the gap between raw data providers like Bloomberg and the conversational interface of modern AI.
Every numerical data point and claim is hyperlinked to the specific page and paragraph of the source document.
Professional-grade financial data and analytics at a fraction of the cost of a Bloomberg Terminal.
Real-time financial data infrastructure powered by a high-performance cloud data engine.
Institutional-grade alternative data for predictive consumer behavior and healthcare insights.
Specialized multi-step numerical reasoning for high-stakes financial analysis.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Algorithms that break down revenue and margins by geography, product line, or business unit from messy table structures.
Vector-based search across thousands of earnings call transcripts to find specific management commentary.
Dynamic generation of 'Comps' tables based on industry, market cap, and fundamental ratios.
Tracks non-standard metrics (e.g., Tesla deliveries, Netflix subscribers) over multi-year periods.
SQL-generation engine that allows users to find companies using complex logic (e.g., 'F-CF > $1B and debt/equity < 0.5').
Converts complex financial formulas into explainable natural language narratives.
Analysts spend hours listening to calls and reading transcripts to find key changes.
Registry Updated:2/7/2026
Fast assessment of a target company's historical performance and segment health.
Tracking how multiple competitors are discussing a new technology (e.g., Generative AI).