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Local and private AI
Local, private, and hybrid AI without guesswork.
Not every AI workflow belongs in the same place. Folium Systems helps businesses choose the right balance of local models, cloud APIs, private endpoints, containers, VMs, GPUs, and edge systems.
Runtime placement
Private AI is an operating placement decision, not a model-name debate.
Folium separates the workflows that can use cloud APIs from the work that deserves private endpoints, local models, hybrid routing, or future appliance-style deployment.
Sensitive work gets data-boundary review before runtime choice.
Local, cloud, and hybrid lanes are selected by privacy, latency, cost, fallback, and support.
The buyer can see why a workflow runs where it runs before AI becomes operational.
What Folium Builds
Clear systems, reviewable proof, and a path your team can operate.
Run AI where it makes sense
We help decide which workflows need cloud scale, which need local control, and which need a hybrid path with clear fallbacks.
- Ollama, llama.cpp, SGLang, and vLLM planning
- Provider-compatible local gateways
- Model compatibility and serving matrix
- Token budgets and usage controls
- Vendor exit and fallback planning
- Declarative public/private runtime map
Design the runtime, not just the model
Useful private AI depends on placement, data flow, retrieval, memory, observability, and supportability.
- RAG, memory, and vector store deployment
- Container, VM, CT, and GPU placement
- Private endpoint design
- Privacy, cost, and fallback controls
- Storage and model road readiness
Runtime placement map
Workflows route to the runtime that matches the business risk.
Folium chooses placement by privacy, latency, cost, scale, fallback, integration, and operational control rather than forcing every task into one provider.
- 01 Workflow class Separate support, document, commerce, finance, internal, and sensitive workflows by risk.
- 02 Data need Decide what context is required, what can be redacted, and what must never leave custody.
- 03 Runtime route Choose cloud API, private endpoint, local model, appliance, browser proof, or hybrid path.
- 04 Fallback Define degraded mode, provider exit, offline behavior, and safe handoff when a route fails.
- 05 Operate Track cost, latency, model version, retrieval quality, logs, incidents, and upgrade decisions.
Proof Point
Sensitive workflows get placement options.
Folium packages this as visible evidence so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Proof Point
Cost and privacy are designed before launch.
Folium packages this as visible evidence so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Proof Point
Cloud and local AI can cooperate instead of competing.
Folium packages this as visible evidence so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Start here
Bring the next AI step under control.
You do not need to know every model name, runtime option, or integration path. Tell us what is slow, risky, expensive, confusing, or disconnected. We will help translate it into a practical AI systems plan.