The AI-native operating system for lean investment teams
Where Boutique Meets Breakthrough.
Empowering lean investment teams with the intelligence of top-tier hedge funds.
We meet boutique hedge funds, new launches, independent asset managers, and family offices where their data actually lives: in Excel, cloud storage, broker files, legacy scripts. We ingest it, reconcile it, and run AI agents on top. No rip-and-replace required.
Who it's for
Built for lean investment teams.
If you see yourself below, we should talk.
01 · Boutique
Boutique hedge fund
$100M–$2B AUM · 5–30 team
Runs on Excel and broker files. Every risk and investor question lands on the people who should be investing.
02 · Lean AM
Lean asset manager
$500M–$10B AUM
Client reports, RFPs, and investor questions pull the team off the portfolio. Enterprise platforms priced for firms 10× your size.
03 · Family office
Single / multi-family office
Multi-asset, 3–8 person team
Need institutional-grade risk and reporting without institutional-grade cost.
The sharpest fit: firms where the Excel layer runs everything, and nobody has the budget or time for an enterprise-scale deployment.
New launches: walk into due diligence with institutional-grade risk answers from day one.
The problem
Boutique funds run on Excel.
Enterprise tools don't fit.
Fragmented investment data
Investment risk, client reporting, and reconciliation live across 10–20 disconnected Excels, VBA, ad-hoc Python, and vendor exports. Every number the portfolio needs is a manual assembly job.
The "Three Numbers" problem
Ask "what's our China exposure?" and risk, reporting, and investment teams give different answers. Same data, three versions.
Priced out of enterprise tech
MSCI / Axioma / Clearwater: $100K–$500K+/year. Bloomberg PORT Enterprise: $200K+. Out of reach for firms under $5B AUM.
Your data. Amplified.
One platform.
Your book, the way your team sees it.
Large firms surround the desk with risk, reporting, and quant teams.
Medusa delivers the same institutional-grade answers without the headcount or the enterprise price tag, so you stay focused on investing.
AI inside the data, and on top of it.
AI agents
Conversational Risk · Optimizer · Reporting & Communications · Custom agents.
Reconciled data
Trades · Positions · Factor exposures · Market data. One version of truth, produced by AI-driven reconciliation.
Your existing stack
Your Excels · Cloud storage · Broker files · Market data feeds · Legacy scripts.
AI-native, not AI-bolted
Built around LLMs from the ground up, so it's economically viable to deliver institutional-grade infrastructure at lean-team price points.
Provider-agnostic
We work on top of Bloomberg, MSCI, Axioma, Enfusion, or whatever you already have. No rip-and-replace.
One reconciled data layer
The structural answer to the "Three Numbers" problem. Every agent reads one view of the book, held in your own terms, so risk, reporting, and reconciliation outputs always agree.
Why Medusa
Four structural advantages
Hedge-fund-insider-turned-builder
11 years inside hedge funds (7 at Pictet). We know your workflows because we lived them.
AI-native architecture
Built around LLMs from the ground up: enables lean-team pricing at 70–90% below enterprise incumbents.
Built on your stack
Works on top of Bloomberg, MSCI, Axioma, Enfusion. No rip-and-replace. Orchestration-first.
Your book, as your team sees it
Your sector definitions, your model mix, your conventions. Reconciled underneath, so the numbers hold up.
Our thesis
Quantamental 2.0: context is the new edge.
Generative AI is collapsing the divide between fundamental and quantitative investing. As model capabilities commoditize, durable edge shifts away from the models themselves and toward context, the judgment, institutional knowledge, and workflows a firm encodes into its AI systems.
That is the thesis Medusa is built around. The reconciled data layer, the agent architecture, the forward-deployed engagement model, all of it exists to turn your firm's context into compounding advantage. It is also why Medusa is model-agnostic by design: the underlying model is swappable, and you are never locked to one AI lab.
From the blog
Why the next decade of alpha belongs to context, not models.
Why the next decade of investment alpha will be won by firms that systematically encode their context into AI-enabled workflows, not by those chasing the next model.
Read the full post →How we engage
Two engagement paths. Usually both.
TRACK B: TODAY'S ENTRY POINT
Forward-deployed engagement
Founder-led. We ingest your fragmented investment data, automate your highest-pain workflow, and deploy conversational agents on top.
- • From CHF 10K pilot + CHF 3.5K/month platform fee, replaces $100K–$500K/year of enterprise risk analytics
- • Delivered in about six weeks. Pilot fee credits toward Q1 platform fees on conversion.
- • Larger firms (growth / enterprise): scoped on a fit call
"My positions are in 17 Excels and I want to talk to them."
TRACK A: SCALING PATH
Platform subscription
Standard modules: Risk, Reporting & Communications, Reconciliation, Optimizer, available on a monthly subscription as your custom work standardizes.
- • From CHF 3.5K/month, full platform access at boutique scale
- • Priced for lean teams, not enterprise budgets.
- • Larger deployments (multi-desk, custom integrations): scoped on a fit call
"I want the Risk module turned on."