Engagements
Three engagement types we are most often called in for, described as they run. These are patterns rather than client stories — where we name a company, it is named because our engineers worked there.
A configuration half-finished by a departed consultant. Intake works, document generation does not, nobody trusts the deadline logic, and the firm has gone back to the spreadsheet it was supposed to replace.
A fixed-fee assessment first: what exists, what is salvageable, what must be rebuilt, and what it will cost to finish. Then the fix — usually repairing the data model underneath before touching anything users can see.
The firm stops paying for a system it is not using, and gets a defined end date instead of an open-ended recovery.
Twelve years of matters in a system the vendor has stopped developing, a billing process held together by one person’s memory, and three spreadsheets nobody will admit are load-bearing.
Discovery produces the blueprint. The data model is built around how this firm runs matters, not a template. Migration is planned, run and validated against the source before go-live, and the staff who have to use it are trained by the people who built it.
One system of record, and a migration nobody has to take on faith.
Client funds tracked beside the case management system rather than inside it, reconciliation performed monthly by hand, and a quiet anxiety about what an examination would turn up.
IOLTA handling built into the matter itself — ledgers, three-way reconciliation, disbursement controls and an audit trail — either natively with Accounting Seed or integrated with the firm’s existing accounting platform.
Trust compliance stops being a monthly act of faith and becomes a property of the system.
Prior production work
Delivered by our engineers inside the companies named, before this firm existed. Not Edge Solutions client engagements — stated plainly because the distinction matters.
A visa application system that read and validated submitted documents using computer vision and OCR, checking them against the application record and removing ninety-five percent of the manual processing.
Why it is relevant here: immigration and high-volume practices run on exactly this problem — documents arriving as scans and free-form uploads that someone has to read, check and re-key.
Production LLM applications serving more than ten thousand requests a day under three hundred milliseconds, automating seventy percent of tier-one support, with the latency budgets and fallback paths that separate a working system from a demo.
Why it is relevant here: the difference between a pilot and something a firm uses on a Monday is almost entirely this kind of work.
Retrieval-augmented pipelines across an e-learning platform’s support queue and content operation, cutting resolution time forty-two percent and tripling content output, on a Django and PostgreSQL backend the same team built.
Why it is relevant here: retrieval over a corpus a business already owns is the same shape as retrieval over a firm’s own matter history.
Describe the system you are trying to build, or the implementation that stalled. We will follow up with a call and, if it is a fit, a scope and a timeline.