RAN Testing

60% Faster Test Cycles with AI-Driven Capabilities

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AI-Powered, Real-Device RAN Testing - From Lab to Field

Amantya’s AI-powered RAN testing suite combines machine learning, real-device workflows, and portable infrastructure to deliver smarter, faster validation across 4G/5G RAN, vRAN, ORAN, and NTN.  Built for intelligent networks, our tools don’t just test - they think. With AI-led log parsing, intent-based test creation, QoE/QoS scoring, and smart slice validation, every step reduces manual effort and accelerates outcomes. From lab, edge to field, we use real devices and real conditions to automate, scale, and streamline RAN testing - giving you continuous intelligence, not just periodic results. 

Explore RAN Test Automation 

Real World Outcomes

50% Lower Cost, 100% Smarter Testing

Test Cycle Acceleration  

Up to 50% faster validation with AI-based test case generation and pre-built test suites  

Deployment Speed   

Portable test environments ready within 24 hours 

Issue Resolution  

60% fewer unresolved bugs with AI-led log analysis  

Use Case Coverage 

4x more scenarios validated through parallel test execution 

Cost Efficiency 

Validates RAN at nearly 50% lower cost than other market tools  

Cost Efficiency  

40% lower OPEX vs traditional test and validation setups 

Why We’re Not Just Another RAN Testing Tool.

AI-First Automation

AI-First Automation

Smart log parsing, anomaly detection, QoE scoring, and root cause analysis powered by AI models.

Real-Device Accuracy

Real-Device Accuracy

Built-in support for iOS/Android COTS devices, modems, NTN devices - ensuring test realism beyond simulation.

Anywhere Testing

Anywhere Testing

AutoRAN for lab, CI/CD, and integration workflows. Delphi for field and edge. Together covering full lifecycle testing.

Intent-Based Test Case Generation

Intent-Based Test Case Generation

Automatically creates intelligent test scenarios based on network intents and device behaviors for faster, broader, and smarter validation.

Advanced RAN Feature Validation

Advanced RAN Feature Validation

Supports MIMO, Carrier Aggregation (CA), handovers, slicing, and other key RAN features for comprehensive 4G/5G RAN/ORAN testing.

NTN Validation

NTN Validation

Validates NB-IoT over NTN, RedCap, satellite delay, Doppler, and high-latency environments. 

Smarter RAN Testing Starts Here. Tuned for speed.

AutoRAN brings AI-driven automation and real-device testing into one powerful platform - built to decode, validate, and accelerate 4G/5G RAN, vRAN, ORAN, and NTN. From log intelligence to slicing validation, it transforms complex RAN testing into repeatable, scalable workflows that move as fast as your network evolves.

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Portable, AI-Driven RAN Testing for Field and Edge

Why limit RAN testing to the lab? Delphi brings it to the field with a compact, AI-enabled form factor built for real-world, on-the-go validation. With real-device support and smart automation, it’s the go-to tool for field teams, edge labs, and pilots demanding fast, accurate, and scalable testing - anytime, anywhere.

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Smarter RAN Testing Starts Here. Tuned for speed

Key Market Segments 

Mobile Operators

Validate coverage, QoS, mobility, slicing, and CBRS performance before network rollouts or upgrades.

OEMs & Chipset Vendors

Test across multi-RAT setups with real UEs, modems, and pre/post integration workflows.

Developers & DevOps Teams

Validate coverage, QoS, mobility, slicing, and CBRS performance before network rollouts or upgrades.

Private Network Deployers

Test factory floors, smart campuses, ports, and mission-critical deployments for SLA-ready 5G. 

Test Labs & OTIC Integrators

Support conformance, coexistence, and automation testing across 4G/5G RAN, ORAN, and NTN

Academic & Research Institutions

Experiment with AI-led test automation and emerging multi-RAT scenarios.

System Integrators & Field Engineers

Use portable tools like Delphi for on-site live testing, acceptance, and troubleshooting. 

NTN & Satellite Operators

Validate NB-IoT, RedCap, and high-latency satellite scenarios with Doppler and edge-aware emulation.

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