Enterprise AI infrastructure · Developed by CSP Solutions
One Gateway. Every AI Model. Total Enterprise Control.
Secure, govern, optimize, and scale enterprise AI through one intelligent gateway. Connect enterprise applications, AI agents, and employees to approved models in the cloud, private infrastructure, or on premises.
Control requests through enterprise policies, intelligent routing, load balancing, data protection, cost controls, and auditability—with CSP Solutions supporting architecture, integration, and deployment.
Security
Guardrails, identity & redaction
Intelligence
Routing & workload balancing
Efficiency
Budgets, quotas & cost visibility
Control
Monitoring, audit & governance
The enterprise control plane
AI adoption creates a growing network of applications, providers, employee assistants, and autonomous workflows. BrainShift AI Gateway sits between that demand and the models that fulfill it, giving CIOs, CTOs, CISOs, architects, and AI teams a common point of control.
For government, financial services, and regulated enterprises, the objective is not simply access to more models. It is consistent policy enforcement, transparent operating costs, resilient routing, and clear accountability across the AI estate.
Explore the BrainShift productCore capabilities
01
One standardized API for enterprise applications and agents. Add or replace approved providers without rebuilding every integration.
Connect supported OpenAI, Azure AI, Anthropic Claude, Google Gemini, DeepSeek, Meta Llama, AWS Bedrock, and private endpoints. Integration availability is confirmed for your deployment.
02
Choose models by task, sensitivity, availability, latency, and cost—not just a fixed endpoint.
Configure routing rules and approved model pools around request type, task complexity, model capabilities, and enterprise policies.
03
Distribute traffic across available providers and endpoints to manage capacity and demand.
Use provider health, capacity-aware routing, traffic allocations, and workload priorities to reduce reliance on one deployment.
04
Enforce input validation, content policies, and approved model access at the gateway.
Configure prompt-injection controls, unsafe-content filtering, output policies, and application-specific rules. Guardrails reduce risk; they do not guarantee detection of every threat.
05
Identify and mask protected information before requests reach external providers.
Configure email, phone, identity, financial, document, and enterprise-defined patterns. Detection coverage and redaction behavior must be validated for the organization's data.
06
Control access for people, applications, and services through a common policy layer.
Scope identity-provider integration, SSO, roles, API keys, group permissions, and department-specific model access to the agreed architecture.
07
Manage requests, tokens, and concurrent workloads across teams and applications.
Apply per-user, department, application, provider, and model policies. Limits help manage runaway consumption, protect capacity, and prioritize critical workloads.
08
Understand token consumption, allocate costs, and make cost-aware routing decisions.
Compare model costs, define budgets and spending limits, review usage trends, and balance efficiency against the quality your workload requires.
09
Switch to approved alternatives when endpoints are unavailable or overloaded.
Configure health checks, retries, fallback models, and traffic redistribution. Fallback rules must preserve data-handling and approved-destination restrictions.
10
See request volumes, tokens, response times, errors, and provider performance together.
Review latency percentiles, availability, estimated costs, consumption attribution, and routing or failover events through centralized operational reporting.
11
Trace model selection, access, policy events, and administrative activity.
Configure audit retention and privacy policies. Operational metadata can be recorded without universally retaining sensitive prompt or response content.
12
Manage approved models, provider endpoints, model pools, and allowed uses centrally.
Maintain a catalog of available models, capabilities, access rules, fallback configurations, and usage reports for public and privately deployed environments.
Enterprise AI architecture
Inspect sensitive data and enforce policies before routing. Approved destinations and fallback paths are constrained by your data-handling requirements.
Request lifecycle
STEP 01
Validate the application, API key, identity, and model-access permissions.
Data security & governance
A central enforcement layer makes it possible to apply consistent identity, content, and data policies before an application reaches an external provider. Redaction rules and model permissions can be adapted to organizational requirements.
Restrict sensitive workloads to approved private endpoints, and ensure fallback routes honor the same restrictions. Audit retention should reflect privacy requirements rather than automatically storing every sensitive prompt.
Illustrative redaction flow
Incoming request · fictional sample
Review the account for alex@example.com
Approved outbound request
Review the account for [EMAIL_REDACTED]
Security controls, detection coverage, retention, infrastructure isolation, and data-location requirements are validated for the agreed deployment. No universal certification, residency guarantee, or perfect threat detection is implied.
Deployment options
Choose an architecture aligned with your governance and operational requirements. Kubernetes and OpenShift deployment options are scoped with your infrastructure teams.
Run the gateway in your own data center, with private model endpoints and enterprise operations scoped around your requirements.
Operate in dedicated enterprise environments with agreed networking, access, and operational responsibilities.
Connect approved cloud LLMs and privately hosted models through common identity, routing, and data policies.
Define approved regional and processing boundaries. Sensitive traffic can be restricted to appropriately hosted models.
Restricted or disconnected deployments depend on configured model endpoints and supporting infrastructure. Provider and environment compatibility are confirmed before implementation.
Enterprise benefits
Manage model access and policies through a common control plane.
Apply consistent masking, access, and data-handling policies.
Improve visibility, select suitable models, and manage consumption.
Reduce single-provider dependency with approved fallbacks.
Keep applications connected while changing approved providers.
Attribute usage across departments, users, applications, and agents.
Operational visibility
Gateway Operations
Illustrative dashboard · not customer results
Total requests
128,400
Active providers
4
Token consumption
18.6M
Estimated monthly spend
$2,840
Enterprise use cases
Control approved public and private model access, enforce data-handling boundaries, and maintain oversight of government AI services.
Unify authentication, consumption limits, policy enforcement, and auditable access for regulated workloads.
Provide governed LLM access to employee companions, knowledge assistants, and business applications.
Manage the routing, security, consumption, and operational costs of agents executing multi-step workflows.
BrainShift ecosystem
Apply common model permissions and consumption policies to employee assistants and enterprise knowledge requests.
Explore WorkspaceGive agents, AI applications, and intelligent workflows access to models approved by the gateway.
Explore StudioBring autonomous and assisted model requests under shared routing, cost, monitoring, and security controls.
Explore AI AgentsGateway also serves third-party enterprise applications independently of other BrainShift products.
Enterprise engagement
Centralized model access, policy management, and governance for multiple applications and departments.
Enterprise-controlled deployment with architecture planning, integration, and agreed support options.
Operations, provider configuration, traffic monitoring, and technical support under an agreed service contract.
Capabilities, provider integrations, deployment options, service levels, and commercial terms are confirmed against your requirements. Pricing is provided by quotation.
Frequently asked questions
A centralized infrastructure and governance layer between enterprise applications and AI models. It brings access policies, routing, usage controls, and operational visibility into one platform.
Yes. Supported internal and external endpoints can sit behind one standardized API. CSP Solutions confirms the required providers, credentials, licensing, and integrations for your deployment.
Yes. Compatible private inference services can join approved model pools alongside cloud models, with rules governing which workloads may use each destination.
On-premises and private-cloud architectures are available for scoping. Restricted or disconnected operation depends on local model endpoints and all supporting infrastructure.
Configurable redaction, PII detection, guardrails, access policies, and destination restrictions help control data handling. Controls must be tested for your data and are not a blanket compliance guarantee.
Policies select from approved models using factors such as task type, sensitivity, cost, latency, capacity, and availability. Your teams define the permitted choices.
Configured health checks, retries, and fallback policies can redirect requests to approved alternatives. A fallback must not bypass your security or data-location restrictions.
Department and application attribution, quotas, budgets, and spending controls support cost oversight. Metering and budget behavior are agreed for the deployment.
Enterprise identity and SSO integration can align gateway access with your directory. Identity-provider compatibility and permission mapping are confirmed during implementation.
Applications can generally connect to the gateway instead of directly to models. Compatibility, authentication, request formats, and any application changes are assessed during integration.
Container orchestration options can be scoped for Kubernetes and OpenShift. Cluster versions, networking, storage, observability, and support responsibilities are confirmed in the architecture review.
Workspace employee requests and Studio agent workflows use the gateway's approved models and policies. Third-party enterprise applications can also use Gateway independently.
Talk to CSP Solutions
Unify AI model access, protect enterprise information, optimize performance, and govern consumption. Discuss cloud, private, and on-premises deployment with CSP Solutions.
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