Work & evidence
Livnium does not publish invented client outcomes. This page shows how we structure delivery and the public technical evidence available today.
Client names, screenshots, and performance results appear here only with permission and enough context to make the claim useful. Confidential work remains confidential.
Delivery patterns
Every engagement is shaped around the specific system, but these patterns show how Livnium connects business understanding to technical execution.
Map the current process, decisions, tools, repeated data entry, and handoffs. Remove unnecessary steps, connect systems through APIs or shared data, automate stable work, and make the result observable.
Reviewable evidence: before-and-after workflow map, scope decisions, working integration, exception handling, operational metrics, and support ownership.
Turn the uncertain idea into explicit users, constraints, acceptance criteria, architecture, and delivery stages. Build in working increments, test early, prepare releases, and retain the context needed after launch.
Reviewable evidence: agreed scope, prototypes, working builds, QA record, release checklist, documentation, and an improvement backlog.
Start with chance and ordinary baselines. Control leakage, isolate the proposed mechanism, compare like with like, document limitations, and keep negative results when the idea does not survive testing.
Public example: Livnium Labs reports both a 68.92% SNLI result and the stronger 70.13% MLP probe, so the unique benefit of collapse remains unproven pending matched multi-seed ablations.
What counts as proof
A product release is not proven by a research benchmark, and a research claim is not proven by a polished interface. We match evidence to the decision being made.
Acceptance criteria, usability, edge cases, performance, security, release readiness, and observable use.
Less repeated work, fewer handoffs, faster completion, better visibility, and clearly owned exceptions.
Baselines, ablations, reproducible scripts, multi-run context, limitations, and negative results.
Your context first
Tell us what is slow, fragile, disconnected, or hard to scale.