how i work
Law practices run on data that has to be right. These are the rules every pipeline I build follows, the posture on client data, and who you would actually be working with.
Engineering principles
Deterministic over generative
The same input produces the same output, every time. If a value lands on a legal document or a government form, it got there through explicit code that can be read, tested, and audited. Not through a model's best guess.
Schema-first
The client record is defined once, precisely: names, roles, relationships, assets, history. Every system in your stack maps to that schema instead of holding its own slightly different copy of the truth.
Every field traceable
Any value in any document traces back to who entered it, where, and when. "Where did this number come from" is a lookup in the audit log, not an investigation across inboxes.
Human review gates
The pipeline prepares. Your attorneys and paralegals approve. Nothing is drafted, filed, or sent to a client without a person signing off. Judgment stays with the people licensed to exercise it.
Fail loudly
A value that fails validation stops and goes to a person. No silent defaults, no best guesses, no "it probably meant this." An error you can see costs minutes. An error that flows through quietly costs a malpractice deductible.
Built on your stack
No rip-and-replace. The pipeline connects WealthCounsel, Clio, Docketwise, and the rest of the software your firm already chose. Your team keeps working in the tools they know.
Where LLMs fit
I have built and shipped LLM integrations, and I use models where they earn their place. In a legal data pipeline, that place is never the pipeline of record. Nothing generated by a model lands on a client document or a government form at runtime. Field mappings are code. Where accuracy matters, the answer is engineering, not a prompt.
Data handling
// the exact storage architecture is specified in writing as part of every proposal
Who you're working with
LJR Dev is Liam Roumila, full-stack engineer and solutions architect, and the founder and principal of the practice. 8+ years building systems integration and data pipelines, 6+ of them working directly inside client businesses: learning the operation from the people doing the work, then shipping the system that fixes it.
Before focusing on law practices, I designed, built, and operated production SaaS end to end: subscription billing, scheduling systems, and data enrichment pipelines, run in production with real customers. That work is why the principles above exist. They are what running systems that handle other people's money and other people's data teaches you.
no account managers · no handoffs · no outsourced team · you work with me from the first call to the last commit
What an engagement looks like
01
Workflow mapping
I follow a real matter through your office and map every point where data gets re-keyed. Days, not months.
02
Fixed-scope proposal
What gets built, the full field mapping, the data handling architecture, and what "done" means. Scoped and priced per firm.
03
Build + parallel run
The pipeline runs alongside your current process on real work until the outputs match. Then, and only then, cutover.
04
Support
Monitoring, reconciliation reports, and keeping the mappings current as your vendors and USCIS change things underneath you.
30 minutes, free. If a pipeline isn't worth building for your practice, I'll tell you that on the call.