Automated
discrepancy
detection
at Rx asset level
Root cause
analysis
across 5+ discrepancy
categories
Migration
readiness
with benchmark validation
Client snapshot
ConceptVines deployed DataIQ agents with SpeedX orchestration to reimagine reconciliation. AI-native workflows automatically detected discrepancies at the Rx asset level, categorized differences across taxonomy, timing, and configuration, and embedded a root cause analysis engine to distinguish methodological improvements from true errors. Executives gained real-time visibility through knowledge graph–powered dashboards, turning reconciliation from a reactive burden into a proactive intelligence process.
The result was a step-change in confidence: manual reconciliation cycles were reduced, data quality strengthened, and migration readiness assured. By proving the reliability of its next-generation platform, the organization not only de-risked its transition but also secured market trust, laying the foundation for scalable, AI-driven data integrity across its global operations.
The challenge
The organization was running two platforms in parallel: a long-established legacy system and a modern next-generation platform. Despite ingesting the same raw data, the systems produced inconsistent outputs at the transformation stages, leading to:
- Data Quality Risks: Reduced clarity in market intelligence reporting.
- Decision-Making Gaps: Lower confidence in insights delivered to pharmaceutical clients.
- Operational Inefficiency: Heavy manual reconciliation of mismatches.
- Migration Delays: Without validation, the transition risked setbacks and credibility loss.
Unaddressed, these issues could erode trust with clients and slow the company’s modernization journey.
ConceptVines’ approach
ConceptVines introduced DataIQ powered by SpeedX orchestration, embedding AI-native discrepancy detection, classification, and resolution workflows:
Automated identification of mismatches across Rx assets.
Classified outputs as valid (methodological improvements) or invalid (errors requiring remediation).
Diagnosed differences stemming from data gaps, taxonomy misalignments, calculation variations, timing issues, and configuration errors.
Balanced AI automation with expert validation evolving toward autonomous remediation.
Integrated tracker data into a dynamic knowledge base with trend visualization for executives.
This AI-native orchestration layer transformed reconciliation from a manual burden into a structured, scalable intelligence process.
The transformation delivered measurable business outcomes:
Why ConceptVines
