The hardware you already own, running the AI you need.
DANI orchestrates AI inference across the workstations, servers, and laptops already inside your perimeter, GPUs, NPUs, CPUs, whatever you have. Minutes to deploy. Zero data egress. Fully auditable.
Built for defense, healthcare, finance, and government.
- 100% on-premise
- Zero data egress
- Existing hardware
- No GPU procurement
- Minutes to deploy
- Not months
- Fully auditable
- Compliance-ready
The problem
Regulated enterprises are locked out of modern AI.
Banks, hospitals, defense contractors, and government agencies face a widening gap between what AI can do and what their compliance posture allows. DANI was built for that gap.
- 01
Cloud AI is off the table
Data residency, compliance, and sovereignty rules make it impossible to send customer records, patient data, classified material, or transaction data to a hosted LLM.
- 02
Dedicated GPU clusters are too slow and too expensive
Procurement takes quarters, not weeks. Capex is enormous. And most of the time most of the capacity sits idle.
- 03
The available open models are a trust problem
Many of the highest-performing open models come with data-sovereignty concerns that make them non-starters in defense, critical infrastructure, and regulated sectors.
- 04
Your fleet is already capable
The refresh cycle just put NPUs and capable GPUs into every corporate workstation. That compute is sitting idle while teams wait on AI.
How DANI works
From ideation to a production-ready secure AI environment in minutes.
Map
DANI discovers the inference-capable hardware already inside your network: workstation GPUs, server-side accelerators, laptop NPUs, CPU-only nodes. Nothing is installed outside your perimeter.
Orchestrate
Inference jobs are scheduled across available capacity. Latency-sensitive work lands on nearby accelerators. Batch work fills gaps. The control plane enforces tenancy, quotas, and audit policy.
Serve
Teams consume inference through a familiar OpenAI-compatible interface. Data stays local. Every request is auditable. No token-based billing, you pay for DANI, not for throughput.
Architecture
One perimeter. No exceptions.
DANI’s control plane, inference fabric, and model registry all sit inside your network. Outbound connectivity to the internet is configurable and off by default.
Control plane
Scheduling · tenancy · audit
Inference fabric
GPUs · NPUs · CPUs across your fleet
Model registry
Signed · version-pinned · offline
Internal apps
Chat · RAG · copilots
Automation
Agents · workflows
Analysts & devs
OpenAI-compatible API
How it compares
Built for the constraint, not around it.
Where data is processed
Time to production
Hardware footprint
Governance & audit
Pricing model
Supply-chain trust
| Dimension | DANI | Hosted cloud AI | DIY GPU cluster |
|---|---|---|---|
| Where data is processed | Inside your perimeter, always | Vendor-operated infrastructure | Your data center (if you built one) |
| Time to production | Minutes to days | Fast, but compliance-blocked | Quarters of procurement |
| Hardware footprint | Uses the fleet you already own | Pay per token, forever | Capex-heavy top-tier GPUs |
| Governance & audit | Full visibility, every request logged | Vendor-controlled telemetry | You build it |
| Pricing model | Fixed — not per-token | Per-token, unpredictable | Amortized capex |
| Supply-chain trust | Transparent, Western-aligned | Depends on provider | Depends on model choice |
What you get
Four things regulated AI has never had at once.
Data sovereignty
Every byte of input, context, and model weight stays behind your firewall. No third-party ever sees a request.
Time to market
Minutes to days from ideation to a production-ready secure AI environment. No procurement cycle. No integration lift.
Governance by default
Per-tenant isolation, policy-driven routing, immutable audit trails. Designed to satisfy the review, not to be retrofitted for it.
Cost collapse
No more per-token billing. Process as much as you can on hardware you already paid for. One price, unlimited throughput.
For the architects in the room
DANI is adjacent to, but distinct from what you already know.
- Distributed compute
Ray
General-purpose. DANI is purpose-built for inference inside a regulated perimeter.
- Orchestration
Kubernetes
Powerful and heavy. DANI ships an inference-first control plane with governance built in.
- Decentralized compute
DePIN networks
Open, token-incentivized, external. DANI is closed, enterprise-controlled, and auditable.
DANI design-partner program
Build with us.
We work closely with a small number of design partners in defense, healthcare, finance, and government. If your organization is shaping its AI strategy right now, we’d like to meet.