Reimagining QA with AI and Digital Twin Labs

Welcome to the second blog in the series “AI-led delivery,” where we discuss the Rakuten Symphony Vision for the Future of Telecom Validation (QA). Our delivery services start with the customer experience labs, and here, we explore a transformational approach to QA in the future.

Introduction

At Rakuten Symphony, we are at the forefront of redefining how software for telecom networks are tested, validated, and deployed. In an industry where innovation is constant and the tolerance for failure is zero, traditional QA processes no longer meet the speed or scale required by next-generation networks.

The emergence of AI-powered Digital Twin Labs represents a fundamental shift in how we assure quality—replacing manual pipelines with intelligent, predictive systems that mirror the complexity of live environments and learn continuously from real-world data.

From manual QA to autonomous, AI-led validation

Legacy QA methods in telecom are reactive, slow, and often disconnected from the real-time state of the production network. The feedback loop is fundamentally broken, limiting it to work in real time. As networks evolve into disaggregated, software-defined systems spanning cloud, edge, and Open RAN architectures, and largely AI led, testing must evolve too.

At Rakuten Symphony, we believe QA must shift left and become AI-first—integrated across the lifecycle, constantly learning, and always improving. Our QA model is not just automated but adaptive in real time, using AI to generate, monitor, and evolve test environments at machine speed.

What is a Digital Twin Lab?

A Digital Twin Lab is a software-based, real-time emulation of the live telecom network. It reflects production data flows, configuration changes, fault behaviors, and usage patterns through AI-instrumented pipelines and CI/CD integration.

Unlike simulations that approximate scenarios, or traditional emulators that reproduce behavior partially, a digital twin must:

• Fully replicate production network states (not just simulate, but emulate)

• Integrate synthetic and real-time data feeds,

• Must largely use software models that mimic the actual hardware in production

• Leverage AI to inject intelligence across every interface
  

This transformation turns the QA lab into a self-learning validation environment that dynamically mirrors network intent and performance. It is entirely visualized, emulated, and tested using AI models purpose-built for the network.  

AI: The core engine of the Digital Twin Lab

We embed AI across every layer of the QA lab—not just to automate but to optimize. Here’s how the Digital Twin Lab architecture works:

The data sources are at the core of the architecture. The data is both agent-simulated and real data fed through the data pipelines. It is modelled to test for corner case scenarios, scale testing, and what-if scenarios.

At the next layer is the LLM, which performs the software emulation using the data feed for the corresponding component that is being emulated.  

Machine learning Models (DNNs) are trained using these vast datasets. The models capture complex behaviors and predict how a real device (UE or DU or Network) would behave under various conditions.

Generative AI enables scenario creation for these models, letting the digital twin predict failures missed in routine QA, edge cases. The next layer is the LLM, which performs the software emulation using the data feed for the corresponding component and rare failures. Outside of this, AI can also simulate the wireless environment – a digital twin might move through a 3D virtual city, interacting with virtual base stations and real-time network models.

In addition, the digital twin constantly applies reinforcement learning by working with actual physical hardware elements, collecting real-time data streams, and updating itself to match real-world conditions.

A model such as this, an AI-powered simulation and continuous learning with real-time feedback, helps to test thousands of scenarios and test cases in real-time.

Let us explore how this model functions to emulate a few devices in a live network:

1. AI-Driven UE Intelligence

• AI agents simulate real-world user behaviors—AI agents’ mobility, app usage, fault injection—using Reinforcement Learning and user behavior cloning.  

• Traffic is generated using RL models trained on historical network logs, which mimic billions of user interactions with extreme realism.

2. AI-Enhanced RF Channel Twin

• Uses Generative Adversarial Networks (GANs) to create terrain-aware RF propagation environments.
  

• Predicts multipath effects, interference, and fading conditions based on real deployment logs (urban, rural, indoor, etc.).

3. AI-Tuned gNodeB / eNodeB Control

• Closed-loop feedback auto-adjusts scheduling, QoS, and HARQ parameters based on twin insights.
  

• AI-powered xApp simulators, anomaly injectors, and RIC behavior models recreate edge case failures

4. AI-Powered Core & IMS Emulation

• AI models identify signaling anomalies in N1/N2/N3 and S1 interfaces, even under DDoS or congestion.
  

• AI-generated workloads simulate complex IMS/MEC scenarios like edge gaming or autonomous vehicles.
  

• LLMs generate test scenarios on demand via natural language prompts ("Validate 10ms latency across a slice").

5. End-to-End Orchestration by AI Controller

• Uses LangChain + Vector DB + Multi-agent frameworks to orchestrate full-stack tests.
  

• Auto-generates test scripts, executes scenarios, and delivers rich post-test analytics with remediation plans.

The AI-powered digital twin can enable strategic use cases for telecom, such as:

1. AI-Powered Configuration Drift Detection

ML models detect deviations from golden config baselines across RAN, Core, and Edge—before they cause disruptions.

2. Regression Testing at Hyperscale

Replay historical + synthetic traffic across hundreds of environments simultaneously, validating new releases 10x faster than legacy methods.

3. AI-Led Root Cause Analysis (RCA)

Using graph-based AI and probabilistic modelling, the twin highlights the most probable fault causes—compressing RCA time from days to minutes.

4. Intent Validation for Private 5G & Network Slicing

Verify whether business intent—like SLA thresholds—is truly met under realistic, AI-generated load and slice conditions before rollout.

5. Self-Healing QA Environments

Digital agents automatically spin up environments, monitor telemetry, and repair broken tests or pipelines autonomously.

Business impact for CSPs and Vendors

The benefits of an AI-led QA framework extend far beyond technical gains. For Rakuten Symphony customers, the value is measurable and immediate:

• Faster Time-to-Market: Releases validated in days, not weeks.
  

• Increased Software Reliability: AI catches issues before users do.
  

• Lower QA Costs: Fewer manual hours, more parallel test coverage.
  

• Differentiated Customer Experience: Networks behave predictably, even under pressure.  

The future is self-intelligent QA

As telecom moves toward disaggregated networks and on-demand services, QA must become a strategic enabler. Rakuten Symphony’s AI-led QA labs not only assure quality—they accelerate innovation. With our digital twin approach, networks become self-aware, test environments self-heal, and quality becomes a constant, not a checkpoint.