60,000+
Clients Served
50,000+
Caregivers Nationwide
85M+
Care Hours Delivered Annually
Industry
Sub-Industry
A decade of acquisitions left the organization managing 40+ disconnected source systems with no shared data model and no reliable enterprise view of patients, caregivers, or financial performance. Inferenz built a unified data warehouse, resolved patient and caregiver identity conflicts across all EMRs through an AI-powered de-duplication layer, and delivered a scalable M&A onboarding framework alongside three AI applications in production. Every acquired entity now goes from close to analytics-ready in under 90 days.
The client had access to massive volumes of trial, lab, and EHR data, but disconnected sources prevented a unified view for predictive risk analytics. As a result, early warning signals were often missed and AI initiatives slowed.
Every report required manual consolidation across disconnected systems. Numbers were stale, inconsistent, and untrustworthy for clinical or financial decisions.
The same patient or caregiver appeared as separate records across multiple systems, making care continuity unreliable and M&A ROI impossible to measure.
No onboarding framework existed. Acquired entities could spend months before contributing to enterprise reporting, delaying visibility into every deal.
Clinical documentation, caregiver scheduling, and workforce analytics were on the roadmap but structurally blocked without clean, governed, unified data.
A foundation built once, reused across every acquisition — and powering AI applications that move clinical, financial, and operational outcomes.
EMRs, payroll, workforce, finance, and recruitment, into a single governed repository, serving as the data source for enterprise Workday and Salesforce rollouts and the keeper of historical records when legacy EMRs were decommissioned.
An AI-powered de-duplication layer created a single golden record per patient and caregiver across every EMR, the prerequisite for care continuity tracking, M&A ROI visibility, and all downstream clinical analytics.
Brings each acquisition to analytics-ready within 90 days, without custom builds or disruption to existing data flows.
An NLA agent for finance stakeholders to query revenue and compliance metrics in plain English; an AI documentation agent that transcribes caregiver visits and auto-populates clinical forms; and a caregiver recommendation engine that triggers on cancellations, matches on availability and patient preference, and confirms via automated IVR.
Centralized warehouse
Single authoritative source for clinical, workforce, financial, and operational data.
time taken to be reporting ready post M&A
Every acquired entity integrated and reporting-ready without manual consolidation.
Data accuracy
Clean, deduplicated patient and caregiver records from day one of every integration.
AI use cases live
NLA chatbot, clinical documentation agent, and caregiver recommendation engine in live.
Whether you’re starting with data modernization or exploring AI copilots, we’re here to help.
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