Estate Planning AI | Services Automation

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Estate Planning Document Intelligence

Automated document extraction and structure mapping for complex family entities and trusts.

Services automation case study featuring document processing and ai implementation.

-70%

Document Review Time

99%

Clause Extraction Accuracy

Weeks Faster

Restructuring Speed

Overview

A boutique estate planning firm needed to analyze complex structures: trusts, LLCs, corporations, real estate holdings, insurance policies. The AI system reduced document review time by 70% with 99% accuracy.

Business Context

The estate planning firm specialized in complex multi-generational wealth structures for ultra-high-net-worth families, often involving dozens of trusts, LLCs, family limited partnerships, private foundations, and operating businesses across multiple states and sometimes multiple countries. Each client engagement began with reviewing thousands of pages of existing documents — trust agreements with dozens of amendments, corporate formations, partnership agreements, property deeds, insurance policies, prior planning memoranda, and correspondence files that contained important context about the family's goals and history. Attorneys and paralegals spent weeks manually reviewing these documents to understand entity relationships, identify beneficiaries and trustees across different instruments, extract key provisions, track amendment history, and map the overall structure before any substantive planning work could begin. This document review phase was a significant bottleneck that delayed substantive planning work by months, consumed expensive attorney time on mechanical tasks that didn't leverage their expertise, and created risk of human error in complex structures where a missed provision or overlooked amendment could have significant tax, liability, or family relationship consequences.

How We Built It

We built a sophisticated document intelligence platform specifically designed for estate planning complexity, understanding the unique vocabulary, structures, and relationships that characterize this practice area. The AI classification engine identifies document types with high accuracy — distinguishing between revocable trusts, irrevocable life insurance trusts, grantor retained annuity trusts, qualified personal residence trusts, charitable remainder trusts, family LLCs, limited partnerships, S corporations, and dozens of other entity types common in estate planning, including recognizing amendments and restatements as related to their base documents. The extraction layer pulls structured data including all parties (grantors, trustees, successor trustees, trust protectors, beneficiaries, remainder beneficiaries, managers, members, officers, directors), key dates (execution, funding, termination, review requirements), funding provisions, distribution standards with their specific language, and special provisions like spendthrift clauses, generation-skipping provisions, decanting powers, and powers of appointment. An entity relationship graph automatically maps how different structures connect — which trusts own which LLCs, how family members relate to different entities in different capacities, where assets are currently held, and how ownership and control would transfer under various scenarios including death or incapacity. The RAG-powered query engine allows attorneys to ask natural language questions about the documents — 'What are the distribution standards for the children's trusts?', 'Which entities hold California real property?', 'Who has removal power over trustees?', 'What happens to the family LLC if Mom dies?' — and receive accurate answers with citations to specific page and paragraph locations in source documents. The scenario modeling module helps attorneys explore restructuring options by visualizing how changes would affect the overall structure, tax treatment, and family dynamics. All documents are stored in a secure vault with complete version history, granular access logging, and attorney-client privilege protections that satisfy bar association requirements and can demonstrate chain of custody if privilege is ever challenged.

Challenges

Thousands of pages of trust documents

Entity relationships unclear

Manual extraction of beneficiaries, trustees, restrictions

High risk of human error during estate restructuring

What We Delivered

AI document classification (trust, will, corporate filings, tax docs)

Entity graph builder showing relationships across documents

RAG engine allowing attorneys to query documents semantically

Clause extraction module summarizing obligations, triggers, deadlines

Scenario modeling for restructuring

Secure vault with audit logging

Tech Stack

Document AI, OpenAI RAG, Neo4j/Graph DB, Custom API, React dashboards

Results

-70%

Document Review Time

99%

Clause Extraction Accuracy

Weeks Faster

Restructuring Speed

Strategic Impact

The 70% reduction in document review time transformed engagement economics, allowing the firm to complete the initial analysis phase in days rather than weeks and begin substantive planning discussions sooner. Attorneys now spend their expertise on creative planning, family dynamics navigation, and strategic tax optimization rather than document archaeology, improving both job satisfaction and client outcomes. The 99% accuracy in clause extraction exceeds what human reviewers typically achieve on complex documents even with careful reading, reducing the risk of missed provisions that could undermine planning strategies or create liability exposure for the firm. The entity relationship visualization has become a valuable client communication tool, helping families understand their current structure — often for the first time in clear visual form — before discussing changes, and enabling productive conversations with family members who aren't familiar with legal documents. Restructuring projects that previously took months of analysis now complete weeks faster because the foundational understanding is built automatically, allowing attorneys to focus on creative solutions rather than information gathering. The query capability has improved responsiveness during client calls — attorneys can answer specific questions immediately rather than promising to research and call back, impressing clients and their other advisors with the depth of preparation. The firm has been able to handle increasingly complex engagements, including multi-jurisdictional structures spanning common law and civil law countries, international families with members in different tax regimes, and situations involving dozens of related entities, because the technology handles the document complexity that previously would have made such engagements unprofitable. Several new clients specifically cited the firm's technological capabilities as a differentiator when choosing to engage them, and the firm has successfully recruited talented associates who are attracted to a technologically sophisticated practice.