Reinvent Acceptance Testing
Device Testing
Characterize new device performance before lab entry and benchmark against competitors with simultaneous multi-device testing — powered by cloud analytics and AI/ML-accelerated insights.
Evolving Challenges
The device testing landscape is growing more complex — networks, applications, and device form factors are all changing simultaneously.
Legacy Processes
- Waterfall testing doesn’t support continuous development
- Dependence on costly labor
- Data spread across companies
Time-to-Market
- Need early cycle problem detection and remediation
- Need better data sharing between stakeholders
Budget Pressure
- Smaller teams focused on device readiness
- Driving cost out of process is critical
Increasing Complexity
- Band proliferation
- Carrier aggregation
- Uneven resource assignment
- Frequent AI-driven changes
Changing Requirements
- More latency-sensitive use cases
- Growth in uplink traffic
- Edge Inferencing
- NTN and D2D
Multiplying Form Factors
- Smartphones a plurality, no longer the majority
- FWA/HINT routers
- More Things
Solution Highlights
Adopt a shared acceptance system that enables a CI/CT model
Reduce dependency on scarce engineering with automation
Speed up triage and identify root causes with AI/ML-accelerated analytics
Overcome closed data silos with secure cloud-based information sharing
Value Delivered
Distinct, measurable value for both mobile network operators and device OEMs — across the full device testing lifecycle.
Protect the brand — ensure devices deliver the advertised performance and QoE subscribers expect
Facilitate fast triage between device and network issues — reduce MTTR via access-controlled portal
Consolidated data across the whole portfolio for trending, issue identification, and percentile ranking
Solutions that support AI-RAN initiatives and next-generation network strategies
Near-real-time reporting of test progress and results in a shared stakeholder portal
Improve productivity of increasingly stretched engineering staffing via AI/ML automation
Actively measure and improve efficient use of spectrum by new devices
Enables CI/CT of devices and full life cycle device QoE & performance management
Facilitate fast triage between device and network issues — reduce MTTR via access-controlled portal
See performance relative to competitive devices on same network via percentile rankings
Enables self-testing with operator confidence — use own logging tools alongside FITView analytics
Near-real-time reporting of progress and results in shared portal
Consolidated data across the whole portfolio for trending, issue identification, and ranking
Improve productivity of increasingly stretched engineering staffing via AI/ML automation
FITView™ Platform
A cloud-hosted analytics and reporting platform that connects MNO device engineering teams, OEM certification teams, and engineering services — with centralized control, real-time visibility, and AI-accelerated insights. Characterize performance of new devices prior to lab entry. Compare results of your previous models in head-to-head benchmark tests for both voice quality and data performance. Test a wide array of devices using multiple chipset interfaces.
- Cloud-based reporting accessible by all stakeholders via a secure and confidential access-controlled portal
- AI-accelerated analysis for faster root cause determination
- Performance and failure trending across device portfolio
- Centralized test cases for remote automation and control that simulate real customer usage
- Real-time monitoring of distributed test sessions
- Multi-chipset support: Qualcomm, Samsung, Mediatek
- Automated FIT reports for compliance and documentation
Ready to reinvent your device testing?
Talk to a Wireless Metrix expert about the FITView solution for your device program