01
Model gateway
Use one stable API across providers, private models, routing rules, and automatic fallbacks.
New
NeuralVault v4.0 is now live
The Infrastructure for
Intelligent Applications.
Deploy, scale, and secure Large Language Models directly within your enterprise environment. Zero latency. Zero trust. Infinite scalability.
project/NeuralVault-dashboard
Production
Designed to fit the infrastructure your team already operates
AWS
Azure
Google Cloud
Docker
GitHub
The platform
Everything between your model and your users.
One operating layer for routing, private context, policy, quality, cost, and deployment—so product teams can ship while platform and security teams stay in control.
03
Policy engine
Apply model permissions, validation, and request controls before output reaches an application.
05
Data protection
Inspect, redact, and route sensitive information according to workload and destination.
06
Request tracing
Follow latency, route decisions, retrieved context, policy outcomes, and errors in one trace.
08
Usage controls
See spend by team and workload, then enforce limits before usage becomes a surprise.
09
Flexible deployment
Keep one API and governance model across managed cloud, private VPC, or dedicated environments.
Architecture
A clean boundary between your applications and the AI ecosystem.
Your teams keep the tools they already use. NeuralVault becomes the controlled layer that connects them.
Private context
Databases
Documents
Cloud storage
NeuralVault control plane
Govern every request
IdentityPolicyRoutingTracing
Model layer
Hosted models
Open models
Private models
Business applications
Copilots
Enterprise search
Workflow automation
One APIAcross model providers
Flexible deploymentCloud, VPC, or dedicated
Policy by defaultBefore every response
Full traceabilityAcross every request
01 import { NeuralVault } from '@neuralvault/sdk';
02
03 const client = new NeuralVault({
04 apiKey: process.env.NEURALVAULT_API_KEY
05 });
06
07 const response = await client.responses.create({
08 model: 'auto',
09 policy: 'enterprise-default',
10 context: { collection: 'company-knowledge' },
11 input: 'Summarize our renewal policy.'
12 });
Policy passed
Trace nv_tr_84Q2
200 OK
Developer experience
One API, without locking your architecture.
Integrate once, then change models, policies, and deployment targets without rewriting application logic.
- Familiar interfaceSimple REST endpoints and typed SDKs.
- Streaming by defaultResponsive experiences for user-facing products.
- Trace IDs everywhereMove from an application error to the full request path.
Security and governance
Controls your security team can inspect—not just promises they have to trust.
Bring identity, data boundaries, model permissions, and audit history into the same place your AI platform team operates.
01
Identity-aware accessCarry user and service identity through retrieval, model access, and logging.
02
Controlled data movementChoose where data is processed and which services are allowed to receive it.
03
Reviewable operationsKeep policy changes, requests, and model decisions in a consistent audit trail.
Governance workspaceProduction policy
Request controlsApplied before model execution
4 activeVerify application identityRequire an approved workload and environment.
Redact sensitive fieldsRemove configured identifiers before routing.
Restrict model destinationsUse private models for classified requests.
Write immutable audit eventRecord request, policy outcome, and model route.
Built for accountable teams
One platform. Three teams that need the full picture.
NeuralVault turns AI infrastructure into a shared operating model instead of a collection of isolated tools and approvals.
Platform engineering
Standardize the foundation.
Give every team approved access to models, data, policies, and shared operational tooling.
Security and risk
Make controls visible.
Review where data moves, who can call each model, and what happened on every request.
Product engineering
Ship without waiting on plumbing.
Build user-facing workflows on stable APIs while the platform layer handles policy and change.
Pricing
Start small. Keep the same architecture as you scale.
Choose the level of isolation, governance, and support that fits your current stage.
Builder
For prototypes and internal evaluations.
$0/ month
- Shared model gateway
- One workspace
- Basic request logs
- Community support
Best for production
Scale
For teams operating customer-facing AI.
Usage based
- Advanced routing and fallback
- Private knowledge collections
- Policy and evaluation controls
- Extended traces and analytics
- Priority support
Enterprise
For private, regulated, or high-scale deployments.
Custom
- VPC or dedicated deployment
- Enterprise identity and roles
- Custom data and retention controls
- Architecture and migration support
- Custom support agreement
Pricing and included limits are template content and should be adjusted to match the final product offer.
Common questions
What teams usually ask before a technical review.
Need to discuss a specific architecture, data boundary, or migration path? Book a session with the platform team.
Book a technical reviewEnterprise deployments can be structured around private networking or dedicated environments. The final topology depends on your cloud, data boundary, and operational requirements.
The platform is positioned around isolated customer data and controlled model routing. Confirm the exact retention and training policy in your final legal and product documentation.
Yes. The gateway is designed to keep application integration stable while routing rules, model providers, and fallback behavior change behind the endpoint.
Scale fits standard production workloads. Enterprise is intended for teams that need private networking, dedicated environments, custom controls, or a tailored support agreement.
Architecture review
Bring the diagram you have today. Leave with a clearer production path.
Use the session to review model access, private data, deployment boundaries, migration, and the controls your organization needs before launch.
30-minute working sessionFocused on your current architecture and priorities.
Technical, not genericSpeak with a platform specialist about implementation.
Clear next stepsLeave with the deployment questions that need resolution.