Swibit Solutions
AI Infrastructure
Kubernetes, vector DBs, and CI/CD tuned for AI.
Overview
The platform layer underneath your AI products, vector databases, feature stores, model registries, observability, and CI/CD designed for ML workloads, not retrofitted from web infrastructure.
Capabilities
- Model serving and routing
- Vector search and semantic indexes
- Feature stores and offline/online parity
- MLOps pipelines and model registries
- LLM tracing and observability
- Cost telemetry across training and inference
How we deploy
From discovery to production
- STEP 1
Reference architecture
Tailored to your hardware, network, and identity stack.
- STEP 2
Foundation rollout
Lakehouse, registry and access controls live in weeks.
- STEP 3
Onboard workloads
Migrate one workload at a time with measured outcomes.
- STEP 4
Operate
SLOs, drift monitoring, and quarterly reviews.
Under the hood
Tech stack
Apache Iceberg / DeltavLLM / TritonLangfuse tracingFeast feature store
FAQs
Common questions
Are we locked in?
Open formats end-to-end. You can leave whenever you want.
Talk to Swibit
Ready to build sovereign AI?
Tell us what you're working on. We respond within one business day with a clear next step.