AI Disclosure Assistant.

The problem

Every residential sale involves tens to hundreds of pages of disclosures, inspections, HOA docs, and title reports. Before submitting an offer, buyer agents must download, read, and interpret these dense, unstructured packets under intense time pressure — then summarize findings for buyers.

Critical language about repairs, permits, water damage, or HOA restrictions is often buried across lengthy documents, increasing cognitive load and the risk that important details are overlooked. Agents lack a standardized way to organize findings, producing slower decisions and inconsistent review quality across transactions.

The solution

AI Disclosure Assistant turns dense disclosure packets into a structured, conversational review workspace. After a packet is uploaded, it generates a one-page executive summary, extracts and highlights key disclosure topics, produces a review checklist, and answers natural-language questions — every insight linked back to source with page-level citations.

The governing principle is "assistant, not assessor." The system surfaces and organizes disclosure-related language for agent review but never makes legal conclusions, determines property risk, or replaces professional judgment — a deliberate stance that reduces liability in a highly regulated domain.

How it works

An uploaded packet is chunked and embedded, then the system generates summaries, categorized highlights, and plain-English explanations of technical terms. Conversational Q&A answers questions like "Any mentions of water intrusion?" by surfacing relevant excerpts with document and page references.

The interface is a ChatGPT-style three-panel workspace: disclosure categories and summary cards on the left, conversation in the center, and source citations with an expandable document viewer on the right. Output quality is defined across six dimensions — clarity, grounding, citation integrity, hallucination avoidance, neutral non-alarmist tone, and actionability — with a hard rule that if a claim cannot be cited, it must not be stated as fact, and conflicting sources are shown side by side rather than resolved.

Who it's for

The primary end users are real estate buyer agents, who upload and review disclosure packets to accelerate pre-offer due diligence, alongside transaction coordinators ensuring documentation completeness and operations managers overseeing workflow.

The product is B2B SaaS, sold to brokerages on a per-agent, per-month basis with optional usage-based pricing tied to document analysis. The economic buyers are brokerage owners and leadership teams focused on productivity, risk reduction, and standardized workflows; high-volume agents are the most revenue-impacting through usage and time savings.

Why it matters

Global real estate software is projected to grow from roughly $12.8B in 2025 to nearly $32B by 2033 (~12.2% CAGR), with the U.S. brokerage and agent-tool segment at ~10.6% CAGR. Legal, finance, and healthcare have already embraced document intelligence, and real estate is following as rising E&O insurance costs push brokers toward tools that reduce liability.

The differentiator is a new category of AI operational intelligence: existing tools like Dotloop and SkySlope store transaction documents but do not interpret their contents. Scoped as a lean MVP focused on comprehension and workflow organization, the product defers MLS and CRM integrations, advanced risk scoring, and brokerage analytics to later releases.

At a glance

Project

AI Disclosure Assistant

Built by

Kenneth Chiu

One-liner

AI Disclosure Assistant helps residential real estate agents review large disclosure packets faster and with source traceability.