Reported impact
Business outcome
Founded in the heart of Stockholm in 2016, is now at the forefront of leveraging AI and data analytics to revolutionize content discovery and media optimization worldwide.
- Customer Experience
- Scalability
Google CloudMedia and EntertainmentMarketing PersonalizationAI and Machine LearningGenerative AI / LLMMedia and ContentCustomer ExperienceScalabilityUndated Google CloudGC#4285f4Media and EntertainmentSwedenVionlabs , a content intelligence company founded in Stockholm Sweden, wants to transform how audiences engage with content. They offer solutions to the global media and entertainment industry, assisting streaming…Optimizes costs with hosted APIs on Gemini platformArchived · Jan 19, 20264Marketing PersonalizationBigQuery · Cloud Run · Dataflow0 2026-01-19 4
Vionlabs
Marketing Personalization
Vionlabs , a content intelligence company founded in Stockholm Sweden, wants to transform how audiences engage with content. They offer solutions to the global media and entertainment industry, assisting streaming services, studios, and broadcasters in understanding and enriching their video libraries.
Optimizes costs with hosted APIs on Gemini platform
This approach has enabled Vionlabs to scale their revenue without significantly impacting costs.
Solution
With Llama 3.1, available on Gemini platform, Vionlabs uses multimodal AI to transform content discovery on streaming platforms. However, rather than building their own text-based large language model, Vionlabs opted to use Llama 3.1 405B and Llama 3.1 70B models, available on Gemini platform. This decision was based on several factors, including maintaining high quality over a long context window; consistency in output that the model and its temperature parameter could achieve; and the ease of integration and cost-effectiveness of Gemini platform's hosted API. Further, Vionlabs used Gemini platform to simplify launching and tracking new training jobs while also integrating seamlessly with its database of choice, BigQuery.
Solution architecture
- Compute
- Cloud Run Runs serverless application services and APIs with automatic scaling for bursty workloads.
- Google Kubernetes Engine (GKE) Runs containerized or virtualized application workloads with the elasticity needed for production scale.
- Dataflow Processes and transforms batch or streaming data for downstream analytics and AI.
- Llama Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
- Storage
- BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
- Orchestration
- Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
Evidence from the source
Accelerates deployments with Gemini platform
Maintains high feature velocity using Llama on Gemini platform
Optimizes costs with hosted APIs on Gemini platform
Vionlabs strengthens its multimodal AI with Llama text models on Gemini platform
With Llama 3.1, available on Gemini platform, Vionlabs uses multimodal AI to transform content discovery on streaming platforms.
Transparency Evidence & methodology
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 value51/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth10/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence75%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity6/10Rewards 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.
- https://cloud.google.com/customers/vionlabsGoogle CloudFirst-party
Vionlabs 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
- First archived Jan 19, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.Open date source
- Sitemap last modified Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
Related
Similar use cases
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