Lexcopia
Rotman School of Management · University of Toronto

Lexcopia · Access-to-justice technology

Our first product · Family Docket AI

The hard part of family law isn't reading the documents. It's knowing which law applies.

A contested family matter buries people in paper: receipts, statements, correspondence, court filings. Family Docket AI reads that pile, organizes it, and flags the legal issues, sections of law, and forms that may apply.

Ontario family law Built for self-represented litigants Synthetic data only · not legal advice

Watch the overview.

Hundreds of documents. One matter. No time.

A single contested family matter in Ontario can generate hundreds of documents that all have to be gathered, sorted, disclosed, and tied to the specific issues in dispute: support, parenting time, the matrimonial home, the division of property.

People with lawyers pay by the hour for this work. People without lawyers, a growing share of family court, do it by hand, late at night, or not at all. The organizing is where matters stall before the law is even reached.

A majority of family litigants, priced out of the help they need.

~65%
of family law cases have a respondent with no lawyer (applicants: about a third)
$25k–$100k+
typical legal cost to each side of a family case that reaches trial
~$7.7B
spent out of pocket every year by Canadians facing legal problems
~1 in 2
adults face a serious legal problem in any three-year period

Family matters run on documents: hundreds of receipts, statements, and filings that must be gathered, organized, and tied to the issues in dispute. A litigant can have the stronger case on the merits and still lose it because their disclosure is incomplete, late, or disorganized. That is the gap Family Docket AI is built to close.

Sources: Statistics Canada, Family law cases in civil courts, 2024/2025 · Department of Justice Canada · Canadian Forum on Civil Justice.

From a folder of documents to an organized, law-linked record.

Every document moves through the same five steps, automatically.

01

Ingest

Read the text out of each uploaded document, whatever its format.

02

Classify

Identify what kind of document it is and the family-law issue it concerns.

03

Extract

Pull out the details that matter: parties, dates, amounts, a plain summary.

04

Surface the law

Surface the sections of law and forms that may apply to each document.

The hard part
05

Organize

Produce a structured record a person can actually review and file with.

Reading a document is easy for AI. Knowing the law is not.

Modern language models read and summarize documents reliably. But asking a search engine "which statute governs this?" is a different problem, and getting it confidently wrong is worse than staying silent.

Asked for the law governing a couple's matrimonial home, naive legal search returned the Income Tax Act, correctly cited, and completely wrong.

A real, properly-cited Canadian statute. Also not the law that divides a family home. For someone without a lawyer, a plausible wrong citation is a trap.

Our approach: anchor the AI to the actual legislation, and defer to a lawyer.

Family Docket AI keeps the AI anchored to the actual legislation. It surfaces the legal issues, sections of law, and forms that may apply, using a lawyer-reviewed reference. Where the law is settled it does this with confidence; where it is unsettled or fact-specific, it flags the matter for a lawyer. It is not legal advice, and it always recommends confirming with one. That grounded reference is the defensible core of the project.

An honest research prototype, and a clear next mile.

Working today

Read, classify, extract

An end-to-end pipeline that classifies synthetic test documents and pulls out their structured details, measured against a hand-built answer key.

The research frontier

Surfacing the relevant law

The step that makes the tool valuable, and hard. We keep the AI anchored to the actual legislation, using a lawyer-reviewed reference, and flag anything unsettled for a lawyer.

The discipline

Synthetic data only

No real family-law documents, from any matter, ever. Every test document was authored for this project, keeping the work clear of privacy and ethics obligations.

Bridging engineering, law, and AI.

SO

Saba Owji

Founder & CEO · P.Eng., M.Eng., PMP · MBA Candidate, Rotman School of Management, University of Toronto

Lexcopia is the venture founded by Saba Owji, a Professional Engineer (P.Eng.) with a Master of Engineering from the University of Toronto, specializing in ELITE, the university's Entrepreneurship, Leadership, Innovation and Technology in Engineering stream, a certified project manager (PMP), and an MBA candidate at the Rotman School of Management. Family Docket AI is its first product.

Her career has been in the management of large, complex, document-heavy projects: project and program management, management consulting, and high-stakes advisory work for executives on major capital projects and infrastructure. That work is fundamentally about turning overwhelming volumes of documentation into something organized, accountable, and defensible, the exact discipline this venture needs. She is also a prior founder, having built and run Equity Stone Realty and Consulting Group since 2019.

Family Docket AI took shape during her MBA Global Practicum in London, in human-centered business design. That lens set its direction: a venture with the person, and family law, at its center, built to save people the money, the headaches, and the hassle that today's system takes for granted.

Saba is passionate about law and policy, and about how a working tool could ease the burden on an overwhelmed court system and, in doing so, contribute to Canada's productivity and economic growth. This summer, she is immersing herself in the law itself, taking two senior-level law courses through the University of Calgary at Imperial College London.

Her achievements span research and business: she is a two-time recipient of the competitive NSERC (Natural Sciences and Engineering Research Council of Canada) Undergraduate Student Research Award, and was granted the Women in Business Excellence Entrance Award upon her acceptance into the MBA program at the Rotman School of Management.

This is bigger than software. Every day, people face the hardest moments of their lives, separation, custody, the loss of a home, on their own, overwhelmed, and priced out of the help they need. Even those who can afford a lawyer often burn through their budget on this mundane organizing instead of on the strategy and decisions that matter, and then run out and end up representing themselves anyway. Nothing today is built to carry that burden for them. Family Docket AI exists to change that: to give people without a lawyer a real chance at justice, to ease the strain on overwhelmed courts, and to prove that access to the law should not be a privilege reserved for those who can afford it. That is the company Saba is here to build.