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Meet the Zinkworks AI powered rApp Studio

Stop buying rApps.Start building rApps

Your engineers already know what should be automated. rApp Studio lets them build, train and deploy without waiting for a vendor.

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rApp AI Design Studio
KPI Input A
Input

Traffic load and congestion telemetry.

KPI Input B
Input

Cell quality and performance metrics.

AI Algorithm ML Model
Model

Predicts optimization actions from KPI signals.

KPI Output 1
Output

Energy-saving policy recommendation.

KPI Output 2
Output

Coverage tuning and capacity adjustment.

Capacity Policy
Policy

Combines KPI outputs into a coordinated optimization policy.

Wake / Sleep Action
Action

Executes low-load sleep mode automation across target cells.

Building rApp

Packaging workflow, validating nodes, and generating deployment-ready artefacts.

Validated with Vodafone
O-RAN aligned
TM Forum AN L4 aligned
Days, not months
Concept to deployable rApp
No vendor dependency
You control rApp delivery
Zero-touch
AI-assisted lifecycle management
Multi-vendor
SMO-portable rApp packaging
10× faster time-to-value per rApp
Validated in Vodafone's RAN innovation programme
Why rApp Studio

Build the rApps you already know you need.

01 Hypothesis to trained rApp in days, not months.
02 Your domain experts build directly - no vendor, no SI, no procurement.
03 Test, iterate, discard, and improve at the speed of your ideas.

Before

Multiple specialists and
repeated handoffs
3-6 months

With rApp Studio

One platform, one engineer,
one continuous flow
3-4 days
Platform Workflow

Operate the full rApp lifecycle from one governed studio.

Move from visual design to code generation, model selection, packaging, simulation, and live assurance without leaving the platform.

Canvas KPI + Intent Blocks Domain-first logic design
Studio Core rApp AI Orchestrator Turns design intent into governed workflows
Bundle SMO Packaging Vendor-compliant deployment artefacts
Pre-Prod SMO Simulators Replay counters, CM data, and APIs
Live Loop Closed-Loop Assurance Validate policy-safe writes on live networks
Model Factory Regression to Deep Learning
GenAI Build Code + APIs + Orchestration
Proven with Vodafone

What used to take months now takes less than a week

Vodafone's Network Innovation team used rApp Studio to define workflows, train models, and package deployable rApps, compressing what normally required multiple specialists and handoffs into a repeatable, efficient process.

10× Faster time-to-value
< 1 week Idea to deployed rApp
In-house rApp development capability
We are working to simplify and accelerate the deployment of AI-powered applications that directly improve the customer experience. This platform allows us to focus on delivering a stronger, more reliable signal and greater network capacity while meeting our sustainability goals.
AR
Alberto Ripepi Chief Network Officer, Vodafone
Vodafone
JM
From the team who built it

We built rApp Studio to solve a real delivery problem. Teams were spending months coordinating data, models and deployment just to get a first rApp into a sandbox. This gives them a repeatable path, clear governance, and evidence they can trust. It shortens time-to-value by at least an order of magnitude. And if model accuracy drifts or the network changes, rebuilding and retraining takes minutes.

James McNamara Vice President of Product, Zinkworks
Industry Alignment

Built on standards you've already committed to

TM Forum, O-RAN, and Zero X are already reflected in how rApp Studio is designed, packaged, validated, and governed.

Use Cases

What operators are building

01

Traffic Prediction

Forecast congestion 15+ min ahead. Proactive, not reactive.

02

QoS Automation

Detect degradation, trigger validated corrective actions autonomously.

03

Energy Optimisation

Predictive cell sleep/wake driven by demand forecasting models.

04

SON Migration

Legacy SON features → governed, portable rApp architecture.

Common questions

01What happens in the demo?

We walk through how rApp Studio handles visual design, model selection, code generation, packaging, simulation, and governed live assurance using realistic operator workflows.

02Can you tailor it to our vendor environment?

Yes. We can focus the walkthrough around your SMO targets, vendor mix, lifecycle process, and the use cases your engineers want to automate first.

03Do we need data or models prepared in advance?

No. We can show the platform using built-in simulators and pre-validated model templates, then map what real data and training paths would look like for your team.

04 Will the session cover deployment and operations, not just design?

Absolutely. The demo covers the full path from design to packaged rApp, along with drift detection, retraining, governed writes, and operational lifecycle management.

05 How quickly can we move from demo to pilot?

We can use the session to scope a concrete pilot path around one or two priority use cases, so your team can see what implementation and validation would look like in practice.

Book a Demo

See how rApp Studio fits your network, workflows, and vendor environment.

Request a tailored walkthrough of the visual designer, model factory, packaging pipeline, and live assurance lifecycle with your team’s real use cases in mind.

Walk through the exact rApp workflow your engineers want to automate.
Explore how packaging, simulation, and deployment fit your SMO environment.
See how lifecycle management handles retraining, drift, and governed live actions.
Tell us what you want to see

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