Hedge Funds
AI that mines your process, leverages your talent, and compounds your IP — an architecture no other fund can buy.
See a DemoThe Problem
A simple neural net can replicate 71% of active fund managers' trades. The managers worth paying for are the ones AI cannot predict. Using the same AI tools as everyone else makes you more predictable, not less.
Bloomberg rolls out the same features to every terminal. Rogo and Hebbia search the same documents for every subscriber. Same logic, same outputs. That's not edge.
SaaS tools encode the vendor's research process. Your team's way of building conviction, testing theses, sizing positions — none of that is in the system.
Your best analysts' frameworks, your PM's pattern recognition, years of institutional judgment — locked in disconnected files. Used once, then forgotten.
What Kith Does
Kith OS connects Claude Code to your entire research layer. The architecture learns how your firm builds conviction — and applies it at a scale no team can match.
How your PM evaluates management teams. How your analysts decompose a thesis. How your firm sizes positions. The architecture executes using your logic, not a generic template.
Hunting parties run against your coverage universe — identifying what consensus believes, decomposing the assumptions, attacking them with evidence. The PM picks the cracks worth pursuing.
One analyst's deep dive on semis automatically becomes context for another's work on enterprise software supply chains. The system connects what your team already knows.
Every thesis, every interrogation, every position accumulates as context. Six months in, the architecture surfaces patterns your team hasn't articulated yet. That's not a feature. That's an asset.
Why It Works
The edge isn't which AI model you use — every fund has access to the same ones. The edge is what you connect it to. Kith builds the architecture around your research process. Better models make it smarter. Your firm's IP makes it sharper. Both compound together.
In Practice
Automated hunting parties identify where consensus is vulnerable. Output: a thesis holds, or it's cracked. The PM decides what's worth pursuing.
Adversarial dialogue pressure-tests the crack from every angle. The system provides surface area. The PM provides judgment.
Surviving theses become trade maps, position sizing, research notes — connected directly to your systems. No copy-paste, no stranded deliverables.
Every system with an API, every file on a drive. The model figures out the interface at runtime. No connectors to build.
How You Start
We build a simulated version of your firm's research environment — realistic coverage universe, your analytical frameworks, your deliverable formats. You see the architecture working before you commit production systems.
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