My-Legacy.ai

Generates customized estate planning documents and recommendations by ingesting user-provided family structure, asset inventory, and personal preferences, then mapping these inputs through a knowledge graph of estate planning scenarios to produce tailored guidance. The system appears to use conversational AI to iteratively gather family and financial details, then synthesizes this context into structured estate plan recommendations rather than applying generic templates.

Implements a privacy-first data architecture that encrypts or locally processes sensitive financial and family information, preventing transmission of personally identifiable information to external legal databases or third-party analytics services. The system likely uses client-side encryption, differential privacy techniques, or local inference to ensure that detailed asset and family data never leaves the user's control in plaintext form.

Provides jurisdiction-aware estate planning recommendations by querying a knowledge base of state-specific probate laws, inheritance tax rules, and filing requirements. The system likely maintains a structured database mapping states to key legal variations (community property vs. common law, estate tax thresholds, probate exemptions) and uses this to contextualize recommendations, though the depth and currency of this database is unclear.

Provides a conversational interface for users to catalog their assets, identify beneficiaries, and map asset distribution preferences through guided questioning. The system likely uses a structured data model to organize asset types (real estate, investments, retirement accounts, digital assets) and beneficiary relationships, then visualizes or summarizes this information to help users identify gaps or conflicts in their estate plan.

Analyzes user profile (family structure, asset types, goals) and recommends a prioritized list of estate planning documents with explanations of why each is needed. The system likely uses a decision tree or rule-based engine to determine which documents are essential (will, healthcare directive) versus optional (trust, UTMA accounts) based on user circumstances, then ranks them by implementation priority and complexity.

Allows users to define estate planning goals (minimize taxes, ensure minor children are cared for, equitable distribution among beneficiaries) and models how different planning strategies would achieve those goals. The system likely uses scenario simulation to show outcomes of different approaches (trust vs. will, lifetime gifts vs. testamentary transfers) and helps users understand tradeoffs between competing objectives.

Provides guidance on healthcare directives, powers of attorney, and incapacity planning documents by asking about healthcare preferences, end-of-life wishes, and trusted decision-makers. The system likely uses a questionnaire format to gather preferences and then generates recommendations for healthcare planning documents and decision-maker selection, though it cannot provide medical advice or replace healthcare provider consultation.

Guides users through the process of selecting guardians for minor children and custodians for assets, with prompts to consider factors like values alignment, geographic proximity, financial responsibility, and willingness to serve. The system likely uses a structured questionnaire to help users think through guardian selection criteria and document their preferences, though it cannot validate that selected guardians will actually accept the role.

Manages disclaimers and compliance messaging to clarify that AI recommendations are not legal advice and cannot replace attorney consultation. The system likely includes prominent disclaimers, limitations statements, and guidance directing users to seek professional legal review before implementing recommendations, though the effectiveness of these disclaimers in protecting against liability is unclear.