Google Cloud Media and Entertainment Sweden

Nextory

Application Refactoring

Nextory is one of Europe’s largest sources for audiobooks, ebooks, and digital magazines. Working with hundreds of publishers across the world, the streaming platform aims to provide readers with everything from the latest releases to the classics.

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Cuts large-book processing from 80 minutes to two

Migrated 100,000 lines of Ruby code in eight weeks

Reduces publisher onboarding from months to a week

Reported impact

Business outcome

Migrated 100,000 lines of Ruby code in eight weeks; Reduces publisher onboarding from months to a week; Cuts large-book processing from 80 minutes to two; Builds repeatable agentic AI migration practices.

Solution

Nextory worked with Google Cloud to map, analyze, and modernize its media processing system in just eight weeks using agentic AI. Nextory is one of Europe’s largest sources for audiobooks, ebooks, and digital magazines. Working with hundreds of publishers across the world, the streaming platform aims to provide readers with everything from the latest releases to the classics. Each new publishing agreement means back catalogs of hundreds of thousands of titles to process at once—something Nextory’s legacy Ruby on Rails architecture was increasingly struggling to keep up with. Nextory and saw a chance not only to rebuild our ingestion system, but also learn a repeatable way to use agentic AI on legacy code,” explains Mattias Engblom, CTO at Nextory.

Solution architecture

5 components · 4 layers

Ingest
- Pub/Sub Moves high-volume events and API traffic between services so the workflow stays decoupled and resilient.
Compute
- Cloud Run Runs serverless application services and APIs with automatic scaling for bursty workloads.
Serving
- Gemini CLI Extends Google Cloud-assisted development into command-line engineering workflows.
Orchestration
- Antigravity Analyzes legacy code and helps engineers plan large refactors of core application workflows.
- Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.

Architecture clues

Gemini CLI
Google Antigravity
The Google Cloud team showed us the importance of using agentic skills to reverse-engineer the process. Using Antigravity and Gemini CLI, we were able to do in two to four weeks what would have taken six or seven months manually.
The migration took place in two phases. First, Nextory and Google Cloud teams worked together to reverse-engineer the existing codebase using Antigravity and Gemini CLI. The teams began by creating specialized agent skills to audit the Ruby
We had started looking at Google tools like Antigravity and Gemini CLI, and saw a chance not only to rebuild our media processing system, but also learn a repeatable way to use agentic AI on legacy code.
“The Google Cloud team showed us the importance of using agentic skills to reverse-engineer the process,” says Sathish Yellanty, sr. staff engineer at Nextory, “Using Antigravity and Gemini CLI, we were able to do in two to four weeks what

Evidence from the source

Builds repeatable agentic AI migration practices

Cuts large-book processing from 80 minutes to two

Google Antigravity

Migrated 100,000 lines of Ruby code in eight weeks

Nextory worked with Google Cloud to map, analyze, and modernize its media processing system in just eight weeks using agentic AI.

Reduces publisher onboarding from months to a week

Transparency Evidence & methodology 85/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
2 metrics describe measurable impact.
Deployment scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence77%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity8/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
Nextory customer story

First-party
https://cloud.google.com/customers/nextory

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

Sitemap last modified Jul 2, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source
Unverified extracted date Jul 2, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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