The Decentralized AI Network Infrastructure that turns the fleet you already own — GPUs, NPUs, CPUs — into a governed inference fabric. Inside your perimeter. Out of the cloud. On your schedule.
Why DANI exists
Cloud AI trades governance for convenience. That trade is unavailable to banks, hospitals, defense, and government — exactly the organizations where AI would move the needle the most.
DANI was built under a different assumption: that the compute you already own, behind the firewall you already trust, is the right place to run modern AI. The platform just has to meet the standard.
That’s what we ship.
Architecture
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 DANI works
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.
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.
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.
What you get
Every byte of input, context, and model weight stays behind your firewall. No third-party ever sees a request.
Minutes to days from ideation to a production-ready secure AI environment. No procurement cycle. No integration lift.
Per-tenant isolation, policy-driven routing, immutable audit trails. Designed to satisfy the review, not to be retrofitted for it.
No more per-token billing. Process as much as you can on hardware you already paid for. One price, unlimited throughput.
How it compares
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 |
For the architects in the room
General-purpose. DANI is purpose-built for inference inside a regulated perimeter.
Powerful and heavy. DANI ships an inference-first control plane with governance built in.
Open, token-incentivized, external. DANI is closed, enterprise-controlled, and auditable.
Design-partner program
We’re onboarding a small, deliberate group of design partners across defense, healthcare, finance, and government. Scoped pilots. Direct founder contact. Shape the roadmap.
daniel.feldman@loxia.ai · daniel.suissa@loxia.ai