DigiEdze AI · Private AI
Secure Enterprise Intelligence for Futuristic Innovation
Private AI is the practical middle path: models and retrieval inside your boundary for sensitive work, external capacity for the rest, and a policy layer that decides which is which on every request.
- private model hosting
- secure inference
- enterprise data boundaries
- hybrid deployment
Reference architecture
- 06Request with data classification
- 05Policy gateway
- 04Private inference (sensitive) · External inference (permitted)
- 03In-perimeter retrieval
- 02Key management & isolation
- 01Boundary evidence telemetry
The problem
What Changes When This Goes Wrong?
A blanket ban on external AI blocks the whole programme; a blanket allowance breaks the data policy.
Nobody can prove which requests carried sensitive content to which endpoint.
Hybrid deployments drift until the boundary exists only in documentation.
What we build
Private AI Capability Grid
Private model hosting
In-perimeter serving with autoscaling, monitoring and lifecycle management, operated like any other production platform.
Data boundary enforcement
Classification-driven routing enforced at the gateway, so sensitivity decides the destination on every call.
Secure inference
Encryption in transit and at rest, key control, tenant isolation and no retention at the inference layer.
Hybrid architecture
Private and external capacity behind one interface, with failover that respects the policy rather than bypassing it.
Private retrieval
Vector index and knowledge stores held inside the boundary alongside the models.
Proof of boundary
Telemetry that evidences where each class of data was processed.
How it works
Transform your enterprise by leveraging AI without compromising data security or compliance. We protect your data by implementing best-fit practices.
Technology
- Private inference runtimes
- Model gateways
- Data classification
- KMS and HSM
- In-perimeter vector stores
Use Cases
Practical Implementation
Insurance
Claim documents processed privately while general drafting uses external capacity.
BFSI
Customer data never leaves the boundary; public reference tasks route externally.
Manufacturing
Process and design IP held inside the perimeter under enforced routing.
Outcomes
What We Measure
Operational efficiency, data security and compliance are promising advantages that remain within your organization’s environment while offering flexibility and customization.
- Sensitive workloads served privately
- Boundary violations detected
- Latency parity with external endpoints
- Cost per private inference
Questions
Private AI, Answered Directly
Is private AI more expensive?
At low volume, usually yes. At sustained volume it frequently costs less, because you are paying for utilised capacity rather than per token. The crossover depends on your utilisation curve, and it is worth modelling before assuming either direction.
Related services
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Our Private AI Strategy
Get ready to secure your enterprise future by privatizing AI through expert-led implementations at scale.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT