Verifiable
legal reasoning.
Silo came from a simple frustration: frontier models could write about a case, but not show a reasoning trail I would trust in one. So I built the system I wanted as a practicing lawyer: structural, inspectable, and tied to evidence.
Silo is built on the bet that legal reasoning is structural at every layer that matters: within a single case, across the body of case law, and across the legislation and doctrine that ground them. Vector retrieval finds passages that look similar to a query. It cannot answer the questions a litigator actually asks: which precedents bind this court, which decisions overrule which, which arguments tend to win when the panel includes these justices.
Better than an LLM-only workflow
The point is not to replace frontier models with a closed stack of our own. It is to give them the legal memory, structure, and case context they do not have by default.
Traceability before fluency
If an answer cannot be inspected by another lawyer, it is not ready. Every output has to show what was retrieved, what was derived, and what was assumed.
One product, specialized gears
Externally there is one product: Silo. Internally there are specialized gears because the workflow demands them: interface, intelligence engine, legislative grounding, document intelligence, and evaluation.
Utility before feature volume
Features do not earn their keep by sounding intelligent in a demo. They earn it by shortening a real legal workflow and surviving repeated use.
A practicing lawyer, building the workflow he wished existed.
Silo is built by Diego Sens, a Brazilian corporate and litigation attorney writing the system from inside a working firm.
I am building it from inside the kind of practice it is meant to serve, which keeps the product close to real legal work and far from demo logic. The longer version is on /about.
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