# structured memo validation Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/structured-memo-validation
> Markdown URL: https://aitinkerers.org/technologies/structured-memo-validation.md
> Technology record last updated: 2026-06-24T10:47:42Z
> Generated: 2026-09-21T15:39:11Z

An AI-driven verification technology that analyzes specific legal passages and generates structured memos detailing how subsequent courts have treated those exact rules.

Traditional legal research tools flag entire cases, leaving lawyers to manually hunt through pages of text to see if a specific rule still holds. Structured memo validation, pioneered by Filevine's LOIS Legal Research, flips this model: legal professionals highlight a precise passage, and the AI runs a semantic search to locate any ruling that has weakened, eroded, or contradicted that exact proposition. The system then outputs a structured validation memo classifying the treatment of the holding (even if subsequent cases never cite the original by name) to ensure litigation arguments stand on bulletproof precedent.

- Official technology site: https://www.filevine.com
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From AI Prospect Memos to a Scored Warm-Intro Queue](https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA)

I built a dogfood workflow on top of graph.one that connects AI-assisted company qualification to a relationship-graph work queue. I'm the founder of graph.one, but this demo is about the internals of the build, not a product pitch. It starts in Prospector: a Node CLI pipeline that creates frozen public-source packets, runs AI-generated prospect memos, validates structured outputs, and stores decisions in append-only JSONL ledgers. The handoff is a qualified-company record. Intro Queue then takes a relationship-graph org-path export with organization identity, relationship owner, optional connector, target person/role, and strength fields. It caches the export in SQLite, collapses duplicate and noisy paths into scored route options, groups selected routes by connector, tracks per-company outcomes, and surfaces fallback routes as a human action queue. In the demo I'll follow one fully synthetic target company from source packet to memo to graph path row to scored route option to failed route/fallback. The queue output is a person-centric ask docket for a human operator; there is no send pipeline. I'll use fully synthetic graph data and redacted schema/log excerpts only.

- Event context: AI Tinkerers Warsaw: GTM Engineering Track — 2026-06-24 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA

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