“We’re not just predicting markets, we’re decoding their behavior,” said Peter Pavlov, CEO and Co-Founder of Edge Hound, as he introduced the company’s new AI-powered research platform. Launched in Bulgaria, the startup aims to carve a niche in the crowded field of automated trading tools by delivering thousands of daily trade ideas through a multi-agent AI architecture. However, Pavlov emphasizes that the platform’s real value lies not in the volume of ideas, but in their clarity.
As brokerages ranging from Interactive Brokers to regional firms accelerate their integration of AI features, Edge Hound is betting that both retail and institutional investors are seeking something different: AI that elucidates the “why” behind every trading signal. The platform processes a vast array of data sources, including news outlets, social media trends, filings, and macroeconomic events, to produce over 2,500 actionable trade ideas daily, with aspirations to increase that figure to 10,000 by the end of 2025, expanding coverage to include ETFs and markets in Europe and Asia.
Pavlov asserted in an interview with FinanceMagnates.com, “To be candid, we haven’t seen any publicly available tool that operates the way Edge Hound does.” He noted that while many platforms claim to utilize advanced natural language processing (NLP) or sentiment analysis, they often yield information-heavy, noise-dense dashboards rather than actionable insights.
The platform’s key features include a chat-driven investing interface, which acts as a “co-pilot” for research and stock discovery. “Buzz Talk” scans news and social conversations to identify hot topics and drivers behind price fluctuations, while near real-time sentiment analysis captures extremes in market optimism or pessimism. A multi-agent AI architecture weighs the judgments of various “virtual analysts,” using a Collective Oracle to reconcile differing opinions and surface the most compelling investment conclusions.
Edge Hound’s Discovery Bot connects macroeconomic events to sector shifts and specific trade signals. The platform claims that the theoretical cumulative return across all AI-generated ideas exceeded 1,200% in September, although it warns that actual results will vary based on individual usage, capital, and trading costs.
AI Competition Picks Up Momentum Across Brokers
Founded by the Pavlov brothers, Peter and Miroslav, who serves as Chief Business Officer, Edge Hound acknowledges a burgeoning competition in AI-driven trading tools. For instance, Interactive Brokers recently unveiled a knowledge graph-driven tool that enables users to identify thematic investment ideas without sifting through large amounts of data. This tool analyzes market relationships, products, and competitors, now covering every S&P 1500 company and easing research for countless traders globally.
Other players in the market, including CMC Invest and TradeStation, have integrated tools like TipRanks into their research offerings. Brokers such as Traders’ Hub have added Acuity’s AnalysisIQ to their portfolios, which provides machine-generated signals and rankings backed by human oversight. As sentiment-tracking features and explainable AI become essential, they are poised to enhance compliance and customer experience.
The Edge Hound team, which includes professionals with extensive backgrounds in applied mathematics, computer science, and trading, emphasizes the importance of real-world experience. “I personally lectured in computer science at the university level for eight years,” Pavlov stated, underscoring his and his partner’s combined expertise.
Pavlov believes that technical proficiency alone isn’t sufficient for a viable product. “The equilibrium comes from combining this technical excellence with real investing and trading experience,” he explained. The team also includes Dr. Dimitar Dobchev, an associate professor in nuclear physics, and Dr. Georgi Simeonov, who holds a PhD in mathematics and AI engineering.
The founders caution that users must engage with the platform responsibly. “AI is a tool, not a holy grail, and when your own capital is at stake, you have a responsibility to understand the decisions you’re making,” Pavlov noted. Following early testing with around 2,000 users, Edge Hound is working on video tutorials and best-practice guides to help users fully leverage the platform’s capabilities.
Pavlov highlighted, “We’ve already done the heavy lifting—aggregating, analyzing, and distilling vast amounts of information into a clean, digestible one-page summary for every stock—updated daily.” He added, “What we’re ultimately selling is time saved, but users still need to invest a bit of time to read, understand, and make informed decisions.”
Though Edge Hound remains bootstrapped with approximately $1.5 million, its strategy focuses initially on retail users for easier scaling. However, the founders expect institutional partnerships to become a significant revenue source and strategic asset. When institutional clients adapt their use cases into retail features, it enhances the platform for all users.
Looking ahead, Edge Hound plans to venture into cryptocurrency and Forex markets by the second quarter of 2026, with options analytics and broker integrations on the horizon. As for the broader implications of AI in investing, Pavlov asserted, “Absolutely, AI will fundamentally reshape the industry. But the transformation won’t come from generic sentiment tools or shallow automation. It will come from systems capable of analyzing businesses, markets, dependencies, and risk at a depth that no human alone can process. That’s exactly what we are building.”
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