systems integration for law practices · new york
Every re-key burns paralegal hours and is a transcription error waiting to be filed. I build deterministic pipelines for estate planning and immigration practices: client data entered once at intake, delivered correctly to your CRM, case management, and drafting software, never re-typed.
Your stack
No rip-and-replace. The pipeline connects the software your firm has already chosen.
What deterministic means here
The same input produces the same output, every time, and every output traces back to its source field. Nothing that lands on a client document or a government form is generated by an LLM at runtime. Where a model is useful, it stays outside the pipeline of record. No client data is ever used to train anything.
The problem
A 40-document-per-month practice typically re-keys thousands of fields a year. That is paralegal time spent on data entry, and every entry is a chance for the error that surfaces later: in a pour-over will, in Surrogate's Court, or in an RFE. This is not a staffing problem. It is a data pipeline problem, and it is fixable.
Reference architectures
These are engineering documents, not case studies. Each one lays out the full design: data flow, error handling, audit trail, and what stays human-reviewed. Skim it, or hand it to your office manager to go deep.
Estate planning
Intake-to-drafting pipeline
DecisionVault or Lawmatics intake, normalized into one client and asset schema, synced to Clio matters, assembled into documents through WealthDocx via the WealthCounsel API. Kills the re-keying between intake, CRM, and drafting.
read the architecture →
Immigration
High-volume form assembly pipeline
One structured client data store, field-level validation pinned to current USCIS form editions, population into Docketwise or INSZoom, and change detection for form-version churn. Every field logged, every change auditable.
read the architecture →
How I work
Deterministic over generative
If a value lands on a legal document, it got there through code that can be read and tested. Not through a model's best guess.
Schema-first
The client record is defined once, precisely. Every system in the stack maps to that schema instead of holding its own copy of the truth.
Every field traceable
Any value in any document traces back to who entered it, where, and when. If a number is wrong, the audit trail says why.
Human review gates
The pipeline prepares. Your attorneys and paralegals approve. Nothing is drafted, filed, or sent without a person signing off.
Who you're working with
LJR Dev is Liam Roumila, full-stack engineer and solutions architect. 8+ years building systems integration and data pipelines, 6+ of them working directly inside client businesses. Before focusing on law practices, I designed, built, and operated production SaaS: subscription billing, scheduling systems, and data enrichment pipelines. You work with me directly, from the first call to the last commit.
no account managers · no handoffs · no outsourced team
A 30-minute call is enough to map where your client data gets re-keyed and tell you honestly whether a pipeline is worth building. If it isn't, I'll say so.