Case Studies
Production systems with before-and-after ranges. Real numbers, not marketing ROI. Open any case for the full story. Client names withheld under NDA unless publicly approved.
Quick Summary
Xenqube Enterprise AI Deployments: Xenqube deploys production AI for enterprises: exchange ops agents, claims automation, clinical ops assist, legal research RAG, risk frameworks, manufacturing vision, and BD intelligence. Each engagement ships with measured outcomes and human approval gates.
Yes. They represent production work. Some company names and team sizes are anonymized at client request. Metrics are shown as realistic ranges where book mix, data quality, or site maturity change outcomes.
Because pretending every insurer processes 2,000 untouched claims/day - or every hospital cuts admin 85% - sets buyers up to fail audits and steers. We publish ranges we can defend in discovery.
Yes - agents, RAG, vision screening, private LLM, and revenue intelligence patterns. Start with an Architecture Brief or readiness assessment so we size a realistic outcome for your workflow.
Often 3-12 months when the workflow is high-volume and information-heavy (intake, research, extraction). Exact ROI depends on volume, wage mix, and how much exception handling remains human.
Request an Architecture Brief. We map your workflow to a realistic outcome range for your context, without inventing ROI theater.