Developer Integration Path

Developers and integration engineers

Everything you need to integrate Enkrypt AI into your applications. Covers API setup, SDK usage, deployment configurations, and troubleshooting common integration challenges.

For

Developers and integration engineers

Duration

1 hr 51 min

Lessons

11 lessons

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Lessons in This Path

Getting Started(2 lessons)

Platform Overview & Navigation

Learn how to navigate the Enkrypt AI dashboard and understand each section

8 min

Quick Integration Guide

Connect Enkrypt AI to your applications using Python SDK or AI Proxy

10 min

Guardrails(5 lessons)

Creating Guardrail Configurations

Step-by-step guide to creating and configuring guardrail setups

10 min

Configuring Deployments

Connect your guardrail configurations to AI models and create protected endpoints

10 min

AI Proxy Integration

Integrate guardrails with minimal code changes using the AI proxy endpoint

8 min

Python SDK Integration

Use the Python SDK for custom workflows and direct guardrail calls

10 min

Batch Processing Logs

Process multiple texts efficiently using the batch detection API with rate limit handling

12 min

Endpoints(2 lessons)

Adding Multimodal Model Endpoints

Configure endpoints for models that handle images, audio, and text

8 min

Adding Agent Endpoints

Configure endpoints for AI agents with tool use and RAG systems

10 min

MCP Security(2 lessons)

Installing the Secure MCP Gateway

Install and set up the Secure MCP Gateway to protect your MCP infrastructure

10 min

Configuring the Gateway

Configure MCP servers, guardrails, and security policies in the gateway

15 min

What You'll Learn

Integrate Enkrypt AI using Python SDK
Configure AI proxy endpoints
Implement guardrails in production workflows
Set up and manage model endpoints
Troubleshoot common integration issues

Complete to earn

Integration Pro

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