INFOSEC • TrueFoundry

TrueFoundry

Enterprise AI Gateway Platform

TrueFoundry is a cloud-agnostic Enterprise AI platform for building, managing, and monitoring AI applications, Large Language Models (LLMs), machine learning models, MCP servers, and AI agents within an organization through a single centralized management system.

The platform acts as a control layer between applications, users, AI models, and internal tools, allowing development teams to connect to models from multiple providers, including open-source and self-hosted models, through a unified API and consistent governance policy, without managing API keys, routing, access control, and monitoring separately for each project.

TrueFoundry also enables organizations to adopt AI agents and MCP servers safely, with access control, authentication, auditing of every command and tool call, and configurable guardrails to detect and mask personal data, detect prompt injection, filter content, and enforce organizational safety policies.

Key Features:

Unified AI Gateway: Connect to AI models and LLMs from multiple providers, including open-source and self-hosted models, through a single unified API, with centralized API key and access management.
Intelligent Routing, Load Balancing & Failover: Route model traffic based on speed, cost, region, or organizational policy, with automatic load balancing and failover to backup models when errors occur or a provider becomes unavailable.
AI Governance & Access Control: Control access to model endpoints, MCP servers, and AI agents by user, team, application, or environment using Role-Based Access Control (RBAC), Single Sign-On (SSO), OAuth 2.0, JWT, and API keys.
MCP Gateway & Registry: Aggregate, register, and manage both internally developed and third-party MCP servers in a single centralized registry, enabling AI agents to securely discover and invoke authorized tools.
AI Guardrails & Content Safety: Enforce security and safety checks on both inbound and outbound model data, including personal data detection and masking, content filtering, prompt injection detection, and enforcement of organizational safety policies.
End-to-End Observability: Track requests, prompts, responses, and tool calls while monitoring latency, error rate, token usage, cost, and infrastructure performance from a centralized dashboard.

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BENEFIT

Key Benefits

Centralized AI Management: Manage AI applications, AI models, AI agents, MCP servers, access controls, and monitoring through a centralized platform, reducing the complexity of managing these capabilities separately across individual projects.
Flexible Multi-Model Connectivity: Connect to models from multiple AI providers, including open-source and self-hosted models, through a unified API, giving organizations greater flexibility in selecting appropriate models for different use cases.
Stronger AI Governance & Access Control: Apply consistent access controls and governance policies across AI models, MCP servers, and AI agents based on users, teams, applications, or environments with enterprise authentication and authorization mechanisms.
Safer AI Agent & MCP Adoption: Enable organizations to adopt AI agents and MCP servers with controlled access to authorized tools, authentication, and comprehensive auditing of commands and tool calls.
Improved AI Safety & Data Protection: Use configurable AI guardrails to detect and mask personal data, filter content, detect prompt injection, and enforce organizational AI safety policies.
Higher AI Service Reliability: Intelligent routing, load balancing, and automatic failover help maintain AI service continuity when model errors occur or an AI provider becomes unavailable.
End-to-End Visibility & Cost Control: Gain centralized visibility into requests, prompts, responses, tool calls, latency, error rates, token usage, costs, and infrastructure performance to support operational monitoring and more effective AI usage management.

Unified AI Gateway: Connect to AI models and LLMs from multiple providers, including open-source and self-hosted models, through a single unified API, with centralized API key and access management.

Intelligent Routing, Load Balancing & Failover: Route model traffic based on speed, cost, region, or organizational policy, with automatic load balancing and failover to backup models when errors occur or a provider becomes unavailable.

AI Governance & Access Control: Control access to model endpoints, MCP servers, and AI agents by user, team, application, or environment using Role-Based Access Control (RBAC), Single Sign-On (SSO), OAuth 2.0, JWT, and API keys.

MCP Gateway & Registry: Aggregate, register, and manage both internally developed and third-party MCP servers in a single centralized registry, enabling AI agents to securely discover and invoke authorized tools.

AI Guardrails & Content Safety: Enforce security and safety checks on both inbound and outbound model data, including personal data detection and masking, content filtering, prompt injection detection, and enforcement of organizational safety policies.

End-to-End Observability: Track requests, prompts, responses, and tool calls while monitoring latency, error rate, token usage, cost, and infrastructure performance from a centralized dashboard.

Centralized AI Management: Manage AI applications, AI models, AI agents, MCP servers, access controls, and monitoring through a centralized platform, reducing the complexity of managing these capabilities separately across individual projects.

Flexible Multi-Model Connectivity: Connect to models from multiple AI providers, including open-source and self-hosted models, through a unified API, giving organizations greater flexibility in selecting appropriate models for different use cases.

Stronger AI Governance & Access Control: Apply consistent access controls and governance policies across AI models, MCP servers, and AI agents based on users, teams, applications, or environments with enterprise authentication and authorization mechanisms.

Safer AI Agent & MCP Adoption: Enable organizations to adopt AI agents and MCP servers with controlled access to authorized tools, authentication, and comprehensive auditing of commands and tool calls.

Improved AI Safety & Data Protection: Use configurable AI guardrails to detect and mask personal data, filter content, detect prompt injection, and enforce organizational AI safety policies.

Higher AI Service Reliability: Intelligent routing, load balancing, and automatic failover help maintain AI service continuity when model errors occur or an AI provider becomes unavailable.

End-to-End Visibility & Cost Control: Gain centralized visibility into requests, prompts, responses, tool calls, latency, error rates, token usage, costs, and infrastructure performance to support operational monitoring and more effective AI usage management.