---
title: "DeedIQ case study"
description: "Making a manual deed-analysis workflow dependable enough for production."
canonical: "https://www.dataacrobat.com/work/deediq"
last-updated: "2026-10-17"
---
# DeedIQ

Document intelligence · Doma Technology

Making a manual deed-analysis workflow dependable enough for production.

## My contribution

I architected the system and led its delivery within Doma's applied-AI team.

## The problem

Deed analysis combines inconsistent documents, detailed extraction, and decisions that need to be checked. A useful system has to make its mistakes visible and give reviewers a way to correct them.

## How it works

1. Use LLM agents and neural search to extract the relevant information from a deed.
2. Run independent extraction teams and cross-validate their results rather than trusting one pass.
3. Keep human review in the workflow and feed corrections back into a continuously learning knowledge base.

## What changed

The published portfolio results are 99%+ production accuracy and a reduction in cost per deed from $12 to $0.20.

## Source and context

The Patents section describes the system, its filing, and the reported production results.

[Portfolio context](https://www.dataacrobat.com/#patents)

[Email Jay](mailto:jayozer@gmail.com)