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Equibles

Research US stocks — search SEC filings, hear earnings calls, and track 13F, insider & congress trades. Plus ALVIS, an AI analyst that cites every number.

Equibles is a US stock research platform that turns SEC filings, earnings calls, and regulatory disclosures into structured, verifiable data.

Every US stock page includes:

Financial statements and company-specific KPIs extracted from XBRL filings, with non-GAAP bridges
Earnings calls with audio playback and speaker-attributed transcripts
Insider transactions (Form 4), congressional trades, and 13F institutional holdings
Short interest, short volume, fails-to-deliver, and buyback programs
Revenue breakdowns by segment and geography, guidance history, and valuation multiples

ALVIS — an AI stock analyst

Ask questions in plain English. ALVIS answers with interactive charts and cites every figure back to the underlying filing.

Built for AI workflows

MCP server — connect Claude, ChatGPT, or any MCP client to 90+ finance data tools
Public REST API serving the same data as JSON
A stock screener across fundamentals, ownership, and price data

Data sources: SEC EDGAR, FRED, CFTC, CBOE, FINRA, and company investor-relations pages, refreshed continuously by automated pipelines.

Free tier available; Pro unlocks full depth and history. The platform's modular data foundation is open source on GitHub.

A startup from Portugal that is founded by Daniel Oliveira.

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Pricing:

Open Source
Freemium
$24.99 / Monthly (Pro — Founding Member price; $49.99 after promo)
Official Pricing

Platforms:

Web
SaaS

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Equibles Reviews and Details

This page is designed to help you find out whether Equibles is good and if it is the right choice for you.

#Financial Data APIs #Stock Market #Finance #Developer Tools #Fintech #Investing #Trading #Stocks #Finance Data API

Screenshots and images

Homepage //
2026-07-22
Landing page //
2026-07-22
Stock page — price, filings & events //
2026-07-22
Financials — chart any metric, KPI or segment //
2026-07-22

Features & Specs

AI Stock Analyst

ALVIS answers plain-English questions with interactive charts and cites every figure to the source filing
MCP Server

Connect Claude, ChatGPT, or any MCP client to 90+ US finance data tools
REST API

Public JSON API with API-key authentication covering the full dataset
SEC Filings Search

Full-text search and AI Q&A across 10-K, 10-Q, 8-K, proxies, and more
Earnings Calls

Audio playback, speaker-attributed transcripts, and AI-generated briefs
Financials & KPIs

XBRL-extracted statements, segment revenue breakdowns, non-GAAP bridges, and guidance
Ownership Data

13F institutional holdings, insider (Form 4) trades, and congressional trades
Short Data

Short interest, daily short volume, fails-to-deliver, and squeeze scores
Stock Screener

Screen US stocks across fundamentals, ownership, and price data
Macro Data

FRED economic indicators, CFTC positioning, put/call ratios, and VIX history

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Questions & Answers

As answered by people managing Equibles.

What makes Equibles unique?

Equibles extracts the answers raw data feeds skip — company-specific KPIs (subscribers, deliveries, ARPU…), forward guidance, and non-GAAP bridges — from SEC filings and earnings calls, and every figure is cited back to the source document.

The same dataset is available three ways: the website, ALVIS (an AI analyst that answers with charts and citations), and an MCP server + REST API so Claude, ChatGPT, or your own code can work with primary-source data directly.
Why should a person choose Equibles over its competitors?
Most market-data products stop at standardized statements and prices. Equibles adds the layer investors actually open filings for:
- Company-specific KPIs, guidance history, and non-GAAP reconciliations extracted from the source documents
- Earnings calls with audio and speaker-attributed transcripts
- 13F institutional, insider (Form 4), and congressional ownership data, cross-linked per stock
- A free MCP server so your AI assistant works with the same primary-source data
There is a usable free tier, and every number links back to the filing it came from.
What's the story behind Equibles?

Equibles started in 2025 from a simple frustration: AI assistants talk about stocks confidently while being disconnected from the primary sources. So we built the data layer first — pipelines that continuously ingest and structure SEC filings, earnings calls, and ownership disclosures — and then put ALVIS (an AI analyst) and an MCP server on top, so both people and their AIs get answers that cite the underlying document.
How would you describe the primary audience of Equibles?
- Individual investors and analysts researching US equities who want numbers they can trace to a filing
- AI-forward users who want Claude, ChatGPT, or another assistant grounded in primary-source financial data via MCP
- Developers building finance tools on a JSON REST API
Which are the primary technologies used for building Equibles?
- Backend: .NET / ASP.NET Core with EF Core and MassTransit background pipelines
- Database: PostgreSQL with pgvector (embeddings) and BM25 full-text search
- AI: LLM extraction lanes with independent validation passes, plus self-hosted embedding and transcription (Whisper) models
- Frontend: server-rendered MVC with Tailwind CSS; React for the ALVIS chat
- Infra: Docker Compose; the modular data foundation is open source on GitHub

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equibles.com

Is Equibles good? This is an informative page that will help you find out. Moreover, you can review and discuss Equibles here. The primary details have been verified within the last quarter. So they could be considered up to date. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.

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