Sozu maps what sits upstream of your positions, grades each causal link, and shows what the market has not priced.
Built for investment teams. The engine is the same across the four seats below. What lands on your screen, and what the chain ends in, is not.
An edge is one causal connection: this moves, therefore that moves. Most systems in this space will produce a confident chain of them about anything you ask. Here the grade decides whether an edge counts at all.
Every node with a traced path to what you hold, out to the hop where the trail goes cold.
Priced, observed, or asserted. Edges nothing can verify score zero rather than being dressed up.
A dated record of every source behind a claim, so later conclusions trace back to exactly what was known and when.
Run a root forward before it resolves. Which of your names it reaches, and how long you would have.
Read what changed each morning, interrogate a chain directly, connect through the API, or inspect the published method.
A daily read of what changed upstream of your book and why, organized by the instruments you actually hold — not by internal graph identifiers.
Interrogate any specific causal chain directly — how it formed, what confirmed it, what would invalidate it — rather than reading a static output.
For teams that want to pull the same record into their own systems directly, rather than through the workspace.
Inspect the definitions, grading rules, forward record, and failures behind the product.
Not a black box. Sozu states a probability on every root, and states where that number comes from — a debiased market price, or the graph's own evidence-driven estimate where no market exists. What's still being built out in the open is the calibration record proving the estimate adds value beyond the market alone. Where a chain is short and already fully priced, it says there is no lead time to sell rather than manufacturing one.
Sozu makes causal claims before outcomes resolve, tests them on unseen periods, and publishes the resulting record.
A named cause, a consequence, and a time window. Specific enough to be supported, challenged, or proven wrong.
Learn from an earlier period, then test on a later period the system has not seen. The bar is whether adding Sozu improves on the market alone.
Claims and test dates are published before resolution. Wins, misses, and corrections remain visible rather than being rewritten afterwards.
Not a data problem. The work is split across terminals, research, spreadsheets, memos, and chat, leaving no maintained record of the chain.
You know what moves each position. That is 240 things to watch, most outside the instrument you hold and several in someone else’s coverage. You watch the loudest eight and hope the ranking was right.
Something is being lifted and nothing on the tape explains it. By the time the chain is legible you have already been adversely selected, and the post-hoc explanation arrives after you have widened.
The chain that impairs the asset starts somewhere nobody is monitoring, moves over quarters rather than days, and reaches a covenant before it reaches anyone’s screen. Diligence mapped it once, at entry.
Coverage is deep and narrow by design. The chain that moves your names runs through policy and industrial territory owned by other desks, and there is no meeting where it gets handed across.
Hard truths on the left, then what has happened, then the branch points where somebody chooses, then what each choice does, then the combinations, then the outcomes that resolve. Click any node to trace its chain and read the full reasoning.
Every node carries its own reasoning. Outcomes carry either a live market price or a stated model read, and the two are never shown as the same thing.
A story starts somewhere: a regional forum, a trade publication, a cluster of accounts that all posted within the same hour. The engine surfaces it while it is small, classifies whether it grew or was built, and follows it through the graph to whatever it touches.
Not what is likely to happen. What happens to your book if it does. Only a traced structure can tell you which positions sit downstream of a root that has not resolved yet, how long you would have, and how much of that path is scored rather than assumed.
First establish the facts. Then trace what they can affect. Finally compare that consequence with what the market already reflects.
A dated record of what happened, where it came from, and when it became knowable. Later conclusions can always be traced back to the information available at the time.
A map from an event to the positions it can reach, the reason each connection should hold, and the time available before the consequence arrives.
A comparison between the traced consequence and current market expectations. If the market already reflects the path, there is nothing new to act on.
Not a feed of everything that moved. Only the hops that reached something you hold, with the path and the grade attached. Most of what the engine sees never reaches you.
An LLM will produce a confident chain about anything you ask it, and nothing in the output tells you which edges are real. The difference is the grade. An edge only carries confidence if something downstream can verify it, and every edge carries its firing history. Where nothing can verify a path, the readout says asserted and it contributes zero. A system that never says asserted is the one to worry about.
No. Factor exposure and causal exposure are different objects answering different questions. Your risk system decomposes what you own along axes that already exist. This decomposes what sits upstream of it. Most teams run them next to each other.
Yes. The substrate graph is shared. Anything built on top of it, including your positions, your own edges and your own scoring, stays isolated and never enters the shared graph. Every propagation is written with the time it occurred and the information it saw, and prior states are never overwritten, so any past state can be reconstructed for audit.