Every deployment below happened in production, at enterprise scale, on Microsoft Azure. The numbers speak for themselves.
Processing 2,000+ calls daily across 75+ languages. Phone interpreters costing $750–$2,370 per agent per month. Long hold times causing caller abandonment. Compliance recording gaps leaving the agency exposed during audits.
VoiceCoreIQ deployed in the customer's own Azure tenant. Real-time AI translation handling 100+ languages, live sentiment analysis for supervisor escalation, and automated compliance recording from day one — no additional infrastructure required.
Interpreter costs cut by more than half. Callers connected to help in seconds instead of waiting for a human translator. Full compliance recording active from go-live, eliminating audit risk entirely.
15,000+ Teams Phone users across 200+ sites with a mean time to resolution of 24 hours. CQD data was overwhelming and unactionable. Teams Phone issues were blamed on the network; the network team blamed UC. Nobody owned the problem.
TeamsCoreIQ deployed AI-driven root cause analysis that automatically correlates CQD data with network telemetry. Proactive alerting surfaces issues before users notice. A single dashboard ended the blame game between UC and network teams.
Resolution time dropped from a full day to half an hour. Escalations to Microsoft dropped by 60%. Proactive issue detection now catches problems before they impact users, and cross-team accountability is built into every incident.
3,000+ network devices across 50 sites monitored by 4 separate tools with zero correlation between them. The UC team and network team worked in complete silos. Average fault isolation time: 4+ hours. Every outage was a war room.
NetmonIQ delivered unified voice-aware network monitoring — a single pane correlating SBC health, Teams call quality, and network performance metrics. AI-driven anomaly detection surfaces degradation before it becomes an outage.
Four monitoring tools consolidated into one. Fault isolation dropped from 4 hours to 15 minutes. Proactive anomaly detection eliminated most war rooms entirely. The NOC team was able to reduce headcount by 2 FTEs through automation.
5,000+ Copilot licenses deployed with less than 40% adoption. No visibility into who was using what. License costs growing quarter over quarter but ROI was unclear. No department-level accountability — the CFO was asking hard questions with no answers.
Copilot License Accelerator with Business Line Intelligence (BLI) dashboards giving each department head visibility into their usage. License Saver automation identified and reclaimed unused seats. Chargeback reporting gave the CFO real numbers.
35% of licenses recovered in the first quarter alone. Every department now has a clear adoption score and accountability. Automated reclamation runs continuously, preventing future waste. The CFO has a live ROI dashboard that answers every board question.
Every deployment runs in your Azure tenant, with your data, under your control. Let's talk about what's possible.