Medusa Investment Partners

We give lean investment teams the intelligence of top-tier hedge funds: boutique hedge funds, new launches, independent asset managers, and family offices underserved by enterprise platforms.

Medusa is a three-layer platform: your existing stack (we meet you where your data lives: Excel, broker files, your risk systems, market data feeds, legacy scripts), one view of your book (your positions and prices, your sectors, limits, risk metrics, model mix and overrides, built from your files and systems when we onboard you), and AI agents on top (monitoring and alerts, answers and stress tests, portfolio construction, the firm-wide view, custom agents).

We work on top of your existing stack. No migration.

Risk is the entry point. Once Medusa understands the book, the same agent can work across the firm and, in time, preserve the firm's investment context: what was decided, why, and what the book looked like at the time.

Why We Exist

Tens of thousands of lean investment teams are underserved by enterprise platforms. MSCI / Axioma / Clearwater typically charge $100K to $500K+ a year, and the book has to live on their platform, in their model of the portfolio. That leaves most boutique funds running investment risk and client reporting on 10–20 interconnected Excel files, held together with VBA, ad-hoc Python, and human memory, with severe key-person risk and end-of-month surprises.

We built Medusa because AI finally changed the economics. LLMs made it viable to ingest a fund's messy Excel sprawl without writing custom ETL per client, a capability that didn't exist 18 months ago. Combined with 22 years at the intersection of finance, technology, and data science, that unlocks institutional-grade infrastructure at lean-team price points for the first time.

The Thesis Behind the Product

GenAI is collapsing the long-standing divide between fundamental and quantitative investing. The "translation tax", the hidden cost of humans shuttling between PMs, risk teams, and systems, disappears when LLMs handle semantic bridging natively. Edge shifts from model to context. A new role emerges: the "Context Engineer": part librarian, part system designer, part investor. It is also why Medusa is model-agnostic by design: the underlying model is swappable, and you are never locked to one AI lab.

Large firms (Pictet, Millennium, Citadel) have the resources to staff the engineering Quantamental 2.0 requires. Boutique and mid-size firms don't, which is the specific gap Medusa is built to fill.

Quantamental 2.0 is the thesis. Medusa is the product that makes it reachable for lean teams.

Read the full thesis on our blog →

Why we're different

Hedge-fund-insider-turned-builder

11 years inside hedge funds (7 at Pictet). Built the workflows we're now automating.

AI-native architecture

~10× cost collapse in ingestion and implementation. Institutional-grade tech at lean-team prices becomes viable for the first time.

Built on your stack

Provider-agnostic. Works on Bloomberg / MSCI / Axioma / Enfusion / Excel, whatever you already have. No migration.

Your book, as your team sees it

Your sector definitions, your model mix, your conventions. Reconciled underneath, so the numbers hold up.

How we engage

Two ways to work with us, often at the same time:

Forward-deployed engagement (today's entry point)

Founder-led. We ingest your investment data across 5+ sources, automate your highest-pain manual workflow, deploy conversational agents on top.

Platform subscription (scaling path)

Medusa on a monthly subscription, in two tiers (Medusa and Medusa Firm-wide), priced per firm, as your custom work standardizes.

Pricing: Priced per firm, scoped on a call after the demo. Pilot fee credits toward Q1 platform fees on conversion.

Aligned incentives: As custom work standardizes, it moves into the platform subscription and the custom retainer declines over time. Our incentives sit with delivering value, not with keeping bespoke work perpetual.

Founder-led, always. Petr is in every engagement personally.

Our Team

Petr Merkuryev | Founder

Petr brings 22 years of experience at the intersection of finance, technology, and data science.

4 years as portfolio manager at a quant equity hedge fund. 7 years at Pictet's hedge fund division, one of Switzerland's most prestigious private banks, running quant research and managing risk on a multi-strategy hedge fund platform.

Author of the Quantamental 2.0 thesis. Conference speaker on AI in Investment Management.

Speaking

Petr's vision for Medusa: give lean investment teams the capability of a large firm, without the headcount or the enterprise price tag.

petr.merkuryev@medusaip.ai linkedin.com/in/merkuryev

Balki Kodarapu | Fractional CTO

Fractional CTO and engineering leader. Helped scale multiple SaaS startups through hyper-growth, including two exits. Has deep experience in fintech, security, and compliance across highly regulated environments. Reviewed Medusa's architecture, security posture, and technical roadmap.

linkedin.com/in/balki

We are looking for a technical cofounder and senior AI engineers. Join us

Advisors

Sven Bouman

Advisor, Hedge Fund Leadership & Strategy

Sven is an advisor to Privium Fund Management Ltd (UK) and Cura & Senectus Investment AG. He is responsible for Prime-Port, which provides solutions to investment managers looking to undertake fund distribution activities in Europe.

Between 2008 and 2022, he was the Founder and former CEO of Saemor Capital, which he founded with the backing of insurance company Aegon as a cornerstone investor. Saemor managed a European equities market-neutral long-short hedge fund with a systematic/quantitative approach to investing. Prior to that, he was Head of Equities at Aegon Asset Management in The Netherlands, responsible for over 15bn AuM.

Sven started his career in the financial industry as a PM at ING IM in 1995. He managed several equity funds (Global, Europe, Japan, Emerging Markets and ESG) and advised on multi-asset mandates.

He is a certified financial analyst (CEFA) and certified institutional investment advisor (MiFID II Stay Compliant Program of CFA Society VBA Netherlands). He holds a master's degree in Economics from the University of Amsterdam. His research on seasonalities in stock returns has been published in academic journals including The American Economic Review.

Koye Somefun, PhD

Advisor, Quantitative Research & AI

Koye has more than 25 years of experience in the industry working in multiple quantitative roles within the multi-asset investment space and at multiple locations. He also worked for the Dutch National Institute for Mathematics and Computer Science as a researcher, publishing several articles on automated trading strategies powered by AI algorithms.

Koye holds a PhD in computational economics and a master's degree in applied mathematics from the University of Notre Dame in the US. He completed his undergraduate studies at the Erasmus University in Rotterdam in the Netherlands. In 2007, he obtained the Certificate in Quantitative Finance (CQF) in London. Koye is based in Amsterdam.