Cloud for AI & data
Turn demanding data workloads into business capability.
Data products need reliable ingestion before they can deliver useful analysis or AI experiences. We connect cloud infrastructure, processing pipelines and managed inference to the workloads your business depends on.
Talk to an expertWhat we deliver
Useful data. Dependable delivery.
Make the path from source data to product decisions easier to operate. Separate workloads, manage asynchronous processing and keep visibility across ingestion, transformation, retrieval and inference.
- Ingestion and transformation
- Design scheduled and asynchronous pipelines with validation, retries and observable processing. Normalize source data into models the product and analytics teams can use.
- Scalable processing
- Match compute, queues, databases and storage to workload patterns. Separate ingestion and background processing from the application paths users depend on.
- AI application infrastructure
- Connect managed inference, semantic retrieval and document workflows. Assess latency, throughput and GPU requirements when selecting a hosting model; dedicated GPU capacity depends on the agreed workload.
- Analytics and operating cost
- Build reporting-ready data foundations and monitor pipeline health. Make processing cost and freshness visible so infrastructure choices support business priorities.
Our approach
A clear path from the first step.
Map the data journey
Identify sources, freshness requirements, product consumers and failure modes. Define what a useful, reliable output looks like.
Deliver the pipeline
Build ingestion, transformation and application integrations in stages. Validate representative workloads and operational recovery.
Scale with evidence
Use throughput, latency, freshness and cost signals to guide capacity decisions as the product grows.
Our work
The experience behind the service.
Explore the engineering work and business context in our case studies.
Cloud infrastructure for data-intensive AI.
How Wagner connected high-volume data ingestion, cloud infrastructure, semantic search, and AI applicant workflows for Sidehustles.
Read case study BarbraMarketing intelligence for smarter investment.
How Wagner helped Barbra connect marketing spend to campaign performance, compare return across three advertising channels, and build the data foundation for forecasting and budget planning.
Read case studyDo you build AI models or the infrastructure around them?
This service focuses on cloud infrastructure, data pipelines and application integrations for AI. Custom model training is a separate scope, and we do not assume it is required for every AI product.
Does every AI workload need GPUs?
No. Managed inference can meet many application needs. We assess workload requirements and cost before recommending dedicated GPU hosting or a managed service.
Can you support both product data and business analytics?
Yes. Sidehustles illustrates ingestion and AI application workflows; Barbra illustrates a reporting-ready foundation for marketing investment analysis. Each engagement starts with its own consumers and business priorities.
Ready to stabilize and scale your cloud?
Start with a technical conversation about the infrastructure issues creating risk, instability, or friction for your team.
Talk to an expert