Alex Roessner 罗轩阳
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Engine

Operational substrate for a regulatory-tech law practice.

In production for a regulated client. Python 1.7 MB active 6h ago

Origin

A law firm at the intersection of compliance, entity structuring, and tokenization runs into a documents-versus-data problem on day one. Most legal tech treats matters as folders of documents and then sells search on top. That works at scale for litigation; it fails at the scale where the same opinion needs to inform a different transaction six months later.

Problem

What a regulatory practice actually wants is a graph: matters connect to entities, entities to opinions, opinions to compliance findings, findings to filings, filings back to matters. A folder hierarchy can't represent that. A vector store can search for it but can't enforce structure on it. The substrate has to know the difference between a matter and an entity, and between an opinion and an artifact.

Approach

Postgres-first. Matters, entities, opinions, and compliance findings are first-class rows with explicit relationships. The graph is the model; documents hang off it. AI assistance is a thin layer on top via MCP — assistants navigate the graph, never the raw filesystem; humans stay in the loop on every legal artifact.

Methodology

Versioning is structural, not file-based. An opinion has a row; the row has a history; the history is queryable. A compliance finding has explicit linkage to the regulation it cites and the matter it informed. AI calls into the graph through scoped MCP tools — generate-summary, find-related-opinions, surface-conflicting-findings — and never returns a legal conclusion as its own.

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Stack

PostgreSQLSupabaseTypeScriptMCPedge runtime
To dig in — alex.roessner@landseed.earth