An immigration-tech company drowning in manual visa-memorandum drafting needed to scale without scaling headcount. We shipped an ecosystem of four LLM products grounded in their own document sets — and automated 80% of routine drafting.
Visa memoranda were drafted by hand from large, inconsistent document sets — slow, repetitive and hard to scale. Case managers were the bottleneck, support tickets piled up, and quality varied with workload.
A retrieval-augmented (RAG) pipeline that reads raw document sets and drafts structured visa memoranda with citations back to source — handling the routine 80% so experts focus on the hard 20%.
LLM-assisted triage, summarization and next-step suggestions wired into the existing case-management workflow.
Real-time monitoring of client conversations against SLA targets, flagging at-risk cases before they breach.
An assistant grounded in the platform's knowledge base that answers common client and applicant questions instantly.
Figures reflect outcomes measured on this engagement. Client withheld under NDA.
We selected models per task, grounded generation in the client's documents to keep outputs accurate and citable, and added evaluation and guardrails so quality stayed measurable. The stack can run on private infrastructure where applicant data must stay contained.
We scope a clear plan with milestones and architecture options — and right-sized GPU hardware if AI workloads are involved.