Resources / Data Nexus
DeveloperPlatform overview
A private control layer between your apps and your AI models — running on your own servers, in a private cloud, or in a hybrid setup.
SYVA Data Nexus is a secure platform you install yourself. It helps organizations run, control, and scale AI model serving on their own infrastructure. It sits between your applications and your AI capabilities — whether those are custom models on private GPUs or approved external AI services.
In one line: the private control layer for enterprise AI serving and agent workloads. It does not train models or replace your model runtime — it helps you register, secure, route, and operate the AI services you already run or connect to.
The business problem
When companies run AI on their own hardware (or mix private models with cloud APIs), the same problems keep showing up: services scattered across machines, different ways to access each model, weak records of who called what, and no single safe entry point for apps that need AI.
| Challenge | Impact |
|---|---|
| AI services live on many machines and teams | Hard to find, route traffic to, and operate at scale |
| Model traffic shares the same path as admin tools | Slower performance and a larger security risk |
| Every model is exposed in a different way | App teams struggle to integrate consistently |
| Cloud AI APIs sit beside private models | Two separate security and integration approaches |
| AI data must stay inside your network (no outside AI clouds) | You need software that runs on your servers, not a vendor cloud dashboard |
What you bring — and what you get
Inputs
- AI model services on private servers or GPU machines
- Approved external AI providers you choose to connect
- Operators, access rules, and policies
- Apps and automation that need controlled access to AI
- Infrastructure you own or control
Outputs
- One secure HTTPS API for all approved AI services
- A central admin console for the cluster and access management
- Visibility into usage, health, diagnostics, and audit history
- Controlled access to on-prem models and cloud APIs
- Session and conversation building blocks for agent-style apps
Core capabilities
AI service operations
- Register and onboard model services across one or many machines
- Approve services and manage their lifecycle before production traffic
- Emergency lockdown to block new service registrations during incidents
- Monitor health and scale related services as a group
Secure access & governance
- One front door for apps that call AI APIs
- API keys limited by service, rate limits, roles, and audit logs
- Network controls that fit enterprise security policies
Hybrid AI integration
- Reach private model services without exposing every port to the wider network
- Bring approved cloud APIs under the same access rules as on-prem services
- Keep provider API keys on the server — not inside every consumer app
Developer enablement
- Syva Service SDK — register, send heartbeats, and shut down cleanly so your service leaves the mesh
- One proxy-style URL pattern and API keys for apps that consume AI
Who it is for
| Stakeholder | Primary need |
|---|---|
| CIO / CTO | Keep AI under company control with clear governance |
| AI / ML platform | Run many model services across GPU machines |
| IT / Security | Network controls, access policy, and audit trails |
| Application teams | A stable, documented API to approved AI capabilities |
| Compliance & risk | Proof of who accessed which AI capability and when |
Deployment models
| Model | Description |
|---|---|
| Single-site | Control platform and model serving in one environment — good for pilots and a first department rollout |
| Distributed cluster | Central platform with model serving on multiple GPU worker machines |
| Hybrid AI | Private on-prem models plus approved cloud providers, under the same access rules |
All models are hosted by you. SYVA runs on infrastructure you control.
Typical use cases
- Serving private LLMs and specialized models on GPUs you own
- One internal AI API hub for teams and approved partners
- Mixing on-prem and cloud AI under one set of policies
- Sites that must keep AI inside their network (e.g. banks, hospitals, government)
- Chat and agent applications that need controlled access to models
Product story and visuals: SYVA Data Nexus product page. Architecture narrative: How it works.