CIO / CEO
Lower cost and scale infrastructure
Build once and use across many internal apps. Turn AI spend into predictable governed capacity on hardware you already own.
Use cases
Where private AI infrastructure earns its place.
Gridlight is for organizations that want cloud-like AI functionality while keeping sensitive work, model execution, and capacity governance inside environments they control.
Stakeholders
Different buyers. Same infrastructure foundation.
CISO / CSO
Improve security posture
Keep prompts and sensitive context local while centralizing guardrails, reporting, and audit across formal and informal AI apps.
CDO / AI leader
Improve inference quality
Support multiple model types and future routing/collaboration without forcing every app team to hand-pick models.
AI infrastructure
Operate a shared foundation
Expose one predictable API, observe capacity by app/system/model, and add compute capacity without rewiring apps.
Capacity and ownership
Private AI patterns map to owned hardware and governed capacity.
Hardware optimization
New apps join shared capacity instead of creating isolated server islands.
Traditional
1 server / Claims
1 server / Search
1 server / Support
1 server / Ops
Gridlight
Virtualized physical resources
Claims AISearch AISupport AIRAGAgents
Capacity Governor
Allocate TFLOPs by app, team, and priority lane.
186TFLOPs allocated
Claims AI26%
Search AI18%
Support AI22%
RAG19%
Agents15%
14msqueue
highcompliance priority
fixedcapacity economics
Primary deployments
The same control plane supports multiple enterprise use cases.
Regulated AI environments
Healthcare, financial services, government, and compliance-sensitive teams that cannot casually transmit data to shared cloud AI endpoints.
- Data locality by default
- Audit lineage per inference event
- Policy enforcement at the control plane
Internal AI application builders
Teams replacing over-built SaaS workflows or vibe-coding internal tools need durable inference, memory, governance, and cost controls after the prototype works.
- Single private API
- Shared memory and context patterns
- Capacity allocation by app/team
Manufacturing, robotics, and edge operations
Local inference supports low-latency analysis, anomaly detection, predictive maintenance, and robotic/agent workflows near equipment and data.
- Sub-second LAN paths
- Edge worker nodes
- Pattern learning over time
Solution providers and MSPs
Partners can deliver private AI capability into client environments without creating a new cloud data processor relationship for every workload.
- Repeatable deployment model
- One governance surface
- Private infrastructure story for clients
Resilience
A shared foundation is easier to govern and recover.
Data gravity
Inference follows the data instead of moving data to a shared endpoint.
Cloud AI patternexternal transmission events
Your dataCloud endpointApp
- ✕ Data leaves the customer environment per request.
- ✕ External vector DB or vendor logs may expand review scope.
- ✕ Data movement creates latency and vendor risk review work.
Gridlight-controlled patterninside configured boundary
Local dataLocal RAGLocal modelLineage
- ✓ Local retrieval and context assembly.
- ✓ No external vector database required.
- ✓ Full lineage recorded in the customer audit path.
One architecture, policy-selected routes.
Gridlight’s virtualized API interface can load balance across orchestrators, while model redundancy across worker nodes supports fault tolerance. If cloud AI is configured as a route, applications keep one architecture while policy decides where work runs.
Use case review