AI-Powered Real Estate Title Examination & Geospatial Platform

Title examiners and abstractors spent hours manually reviewing scanned land deeds, hand-keying legal descriptions, and tracing chain-of-title records. We built a multi-tenant SaaS platform that combines OCR document digitization, LLM structured field extraction, and PostGIS parcel mapping to automate title report production.

— The Challenge

The Problem We Solved

Traditional title examination relies on slow, paper-heavy workflows that delay real estate transactions and increase risk:

Examiners manually typed party names, parcel boundaries, and complex legal descriptions from scanned deeds into title software.
Tracing historical chain-of-title records across multiple deeds was labor-intensive and prone to human omission errors.
Reconciling legal description discrepancies and Public Land Survey System (PLSS) sections required manual cross-referencing across map books.
Fluctuating document volume created turnaround delays during peak real estate transaction cycles.

Challenges Identified

Every challenge was systematically addressed through tailored engineering and design — no workarounds, no compromises.

— Our Solution

How We Solved It

We designed and built an end-to-end title examination platform combining computer vision, AI language models, and geospatial mapping:

Magellan OCR processing pipeline that digitizes scanned deeds, legal descriptions, and survey plats into searchable text.

OpenAI LLM extraction engine parsing structured entities, parties, and parcel boundaries while maintaining a full revision audit log.

PostGIS geospatial integration mapping parcels, survey sections, and legal descriptions directly onto visual GIS map layers.

Multi-tenant workspace providing role-based permissions, document diffing tools, examiner review workflows, and report exports.

Containerized infrastructure deployment using Docker and Kamal on AWS EC2, consolidating servers from five to three instances to cut cloud hosting costs.

— Tech Stack

Technologies Used

Ruby on Rails

PostgreSQL

PostGIS

OpenAI API

Magellan OCR

Docker

Kamal

AWS EC2

— Impact

Results & Outcomes

50%: EC2 Infrastructure Savings
Automated: OCR & LLM Extraction
100%: Chain of Title Auditability

Accelerated title report generation turnaround by replacing manual keying with automated OCR and LLM field extraction.

Improved report accuracy and auditability by maintaining complete field-reference history logs and revision diffing.

Handled high-volume document submission spikes reliably through background queue processing.

Reduced annual infrastructure hosting spend by 50% through Docker and Kamal EC2 server consolidation.

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