How TitleTrace works
From fragmented project files to verified, citable answers.
TitleTrace is not a document search tool or a PDF chatbot. It is a reasoning engine that maps relationships between documents and enforces your firm's standards against them. Here is exactly what happens when you use it.
One environment. Three panels. Everything connected.
The core TitleTrace experience is a three-panel workspace: Files on the left, Canvas in the center, AI on the right.
Files is your project document library — the deeds, prior instruments, survey records, closing instructions, and supporting files that make up a single title project. Everything uploaded to a project lives here, with processing status, page count, and extraction metadata visible at a glance.
Canvas is where TitleTrace shows its work. After your documents are processed, the engine extracts every named entity — Grantors, Grantees, legal descriptions, instrument numbers, recording details, signature blocks — and maps them into a visual relational graph. Nodes represent entities. Edges represent the relationships between them. When a mismatch exists, you see it on the canvas before you see it in an answer.
AI is the reasoning panel. Ask it anything about your project in plain language. It does not search for keywords and return snippets. It traverses the graph, locates the relevant nodes, retrieves the source text linked to each one, and assembles a verified answer with full citations.
All three panels operate simultaneously. You do not navigate between tools. You work in one place.
Workspace screenshot placeholder — high-fidelity mockup will replace this
What actually happens when you upload a project.
Ingest
You upload the files that make up a title project. PDFs, DOCs, scanned instruments, audio files. TitleTrace accepts them all. For difficult formats — handwritten or scanned documents, multi-column layouts, non-standard recording forms — the system surfaces adjustment prompts so you can guide the extraction rather than accepting automated results blindly.
Define
You tell TitleTrace what matters in this project. You can upload your firm's internal checklist, your underwriting standards, or your standard operating procedures for this project type. These are not static reference documents — they are ingested into the graph as a compliance layer that runs against every document in the project. What TitleTrace looks for is defined by you, not by a generic model assumption.
Map
TitleTrace builds a property graph of your entire project. Every entity across every document is extracted and placed as a node. Relationships between entities — across instruments, across time, across parties — are defined mathematically. The Grantor on Deed A is linked to the Grantor on Prior Instrument B. The legal description in the draft is linked to the legal description in the recorded plat. The graph is the proof layer.
Verify
The engine cross-references your project graph against your ingested rules. Every mismatch is flagged: name discrepancies, absent signature blocks, recording requirement gaps, broken chains of title, missing acknowledgments. Each flag is not an inference — it is a graph-level comparison between two nodes that should match and do not.
Answer
You query the AI panel in plain language. "Does the Grantor name match across all instruments?" "Are all required signatures present?" "What are the recording requirements for this jurisdiction and are they met?" The engine traverses the graph, retrieves the source text from each relevant document, and returns a cited answer — with the exact page, paragraph, and location in the source document attached.
This is not a PDF chatbot.
Most AI document tools work like a search engine: you ask a question, the model retrieves chunks of text that seem relevant, and it generates an answer based on those chunks. The problem is that "seems relevant" is probabilistic. For title work, probabilistic is not acceptable.
TitleTrace works differently at the architecture level. Every answer is produced by traversing a structured graph of relationships — not by guessing which text chunks might be useful. The graph enforces constraints. If the Grantor name on the draft deed does not match the Grantor name on the prior instrument, that is not a "possible concern" — it is a broken node relationship, flagged deterministically.
The result is a different kind of output. Not "here is some text that might answer your question." But "here is the mismatch, here is the source document it came from, here is the exact location on the page."
Graph-Relational Reasoning
Entities extracted from your documents are mapped into a property graph with a shared ontology. Relationships are mathematically defined. Mismatches are detected by graph comparison, not language model inference.
Dual-Channel Retrieval
Every answer uses two channels simultaneously: vector semantic search to identify relevant concepts across the graph, and graph traversal to enforce constraints and dependencies. The combination produces answers that are both contextually aware and structurally verified.
Spatial Citation
Every answer includes a full citation chain: source document, page number, paragraph, and visual bounding box in the document. You do not receive an answer you have to trust. You receive an answer with the evidence attached.
Your client files don't leave your vault.
Every project you create in TitleTrace exists in an isolated, encrypted, firm-scoped environment. Your documents are not stored alongside another firm's documents. Your graph is not accessible to other tenants. Your AI sessions are ephemeral — they are not logged, retained, or used to improve any model.
Read our full security architectureSee it on a real project.
The most effective way to understand TitleTrace is to watch it process a project from your own workflow — not a scripted demo with sanitized data.