Two Brothers Put €1.5M of Their Own Money to Build AI That Brings Hedge Fund Intelligence to Everyday Investors

Access to professional-grade investment research has always come at a price most people can’t afford. Bloomberg and FactSet terminals cost between $30,000 and $60,000 a year — tools built for institutional desks, not the tens of millions of self-directed investors who are now active on the markets. Two brothers, Peter Pavlov and Miroslav Pavlov decided to change that. Over the past six months, the two co-founders have committed €1.5 million of personal capital to build Edge Hound: an AI platform that distills the kind of multi-signal, narrative-driven intelligence that hedge fund analysts spend their careers developing, and puts it in the hands of anyone with a trading account.

The results are early but promising. Since launching in October 2025, Edge Hound has onboarded more than 6,000 users, generated over 1,700 AI-driven trade ideas daily, and achieved a 76% win rate on tracked ideas.

The conviction for bringing agentic AI to the capital markets

Edge Hound was not born in a boardroom. Peter and Miroslav Pavlov are brothers who have lived and worked together for years — building systems and products while also being active retail investors themselves. They kept running into the same problem: too much information, and too little time to make sense of it.

“We built this because we experienced the problem firsthand,” said Peter Pavlov, Co-Founder and CEO. “As retail investors, we were drowning in data with no coherent way to act on it. We knew intelligence existed; it just lived behind walls most people could never afford or would need weeks to understand. So we decided to build the thing we wished we’d had.”

Peter brings a background in data management, enterprise analytics, and large-scale digital transformation — most recently leading a 90-person team at Adastra across the DACH region. Miroslav previously built a performance marketing company from scratch, scaled it to 50 people, and sold it to a private equity firm in 2021 for an eight-figure sum.

Now they’re fully focused on bringing Edge Hound’s proprietary AI architecture to market. Essentially, the idea is that Edge Hound Oracle runs a network of specialised agents that continuously ingest news streams, SEC filings, earnings calls, technical price data, and social sentiment. A master agent synthesises these signals into a single, explainable output: a trade idea with entry point, price target, stop-loss, confidence score, and the narrative reasoning behind it.

Why Now

The market is ready for a new decision layer. Retail participation in financial markets has grown significantly, with more than 140 million trading accounts globally — a third of which are managed without an advisor. At the same time, advances in artificial intelligence now make it possible to interpret complex financial data, not merely generate content from it.

The competitive pressure is already visible. Shares of major US brokerages, including Charles Schwab, LPL Financial, and Raymond James, suffered sharp declines in February 2026 following the release of AI-powered advisory tools. Trading platforms that do not embed intelligent interpretation into their products could face a straightforward choice: adopt AI or be replaced by it.

FX: Taking AI Intelligence Into the One of the World’s Biggest Markets

The expansion into foreign exchange represents one of Edge Hound’s most significant milestones to date. With an estimated $7.5 trillion traded daily, FX is the world’s largest financial market — and one where retail participants have historically been most disadvantaged by their lack of access to macro intelligence, narrative context, and institutional signal flow.

Shaping the quality benchmark for this expansion is Paul Bakunowicz, who has joined Edge Hound as Product and Strategy Advisor. Paul spent more than 30 years in institutional FX markets, most recently at Citi, where he served as Head of EMEA FX Trading — overseeing teams across 34 countries where high-stakes decisions depended on disciplined risk management, pricing, and decision-support tools. His experience and domain knowledge directly inform Edge Hound’s approach, bringing institutional-grade interpretation into a system designed to work for every investor.

From Retail to Institutions: Built to Scale Both Ways

Edge Hound’s initial focus on retail investors was always strategic. Consumer adoption serves a dual purpose: validating the product and training the underlying models on real behavioural data — building a proprietary foundation that improves decision quality over time.

The platform is now actively expanding into B2B, with first pilot projects already secured. Its institutional offering provides banks, brokers, and investment advisors with a white-label intelligence layer that can be embedded directly into their own platforms via API. The commercial logic is straightforward: clients don’t leave their brokers because of poor execution. They leave because their broker could not help them understand the market.

The B2B offering adds narrative detection, sentiment shift analysis, cross-asset signal reasoning, and macro disruptor alerts to existing institutional workflows. A full API rollout is planned for Q3 2026.