InfraMate
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Get Started with InfraMate

Transform your code into production-ready infrastructure in minutes with our agentic AI platform that autonomously reasons about your needs and makes intelligent decisions.

Step 1

Agentic Repository Analysis

InfraMate's agentic AI autonomously analyzes your repository to understand your application's structure, requirements, and makes intelligent decisions about your infrastructure needs.

  • Programming languages used
  • Frameworks and libraries
  • Database requirements
  • Directory structure and dependencies

$ inframate analyze --repo github.com/user/project

Analyzing repository structure...

Detected languages: JavaScript (85%), HTML (10%), CSS (5%)

Detected frameworks: React, Express

Detected database: MongoDB references found

Analyzing package.json dependencies...

Reasoning about application architecture...

✓ Analysis complete

Repository analysis summary:

- MERN stack application (reasoning: MongoDB, Express, React, Node.js detected)

- RESTful API structure (reasoning: Express routes follow REST patterns)

- Authentication requirements detected (reasoning: JWT libraries found)

- File upload functionality detected (reasoning: multer dependency identified)

# Inframate Configuration

## Description

Your application description

## Language

Detected automatically

## Framework

Detected automatically

## Database

MySQL

## Requirements

- High availability

- Auto-scaling

- Cost-effective deployment

Step 2

InfraMate Configuration

You can provide an optional inframate.md file in your repository to customize the analysis.

This file allows you to specify requirements that might not be automatically detected, such as:

  • Application description
  • Specific database requirements
  • Infrastructure requirements (high availability, auto-scaling, etc.)
Step 3

Agentic AI Recommendations

Using the Google Gemini API, InfraMate's agentic AI autonomously generates intelligent infrastructure recommendations tailored to your application, with reasoning for each decision.

  • Recommended AWS services for your application
  • Infrastructure recommendations based on application requirements
  • Complete Terraform templates for deployment
  • Estimated monthly cost breakdown for all resources
  • Explainable reasoning for each infrastructure decision

Recommended AWS Services

  • Amazon ECS

    For containerized application deployment

    Reasoning: Optimal for your microservices architecture with auto-scaling needs

  • Amazon RDS (MySQL)

    For database requirements

    Reasoning: Matches your MySQL requirement with high availability configuration

  • Amazon ElastiCache

    For caching and performance

    Reasoning: Detected session management patterns that benefit from caching

Estimated Monthly Cost

Amazon ECS$45.60
Amazon RDS$29.20
Amazon ElastiCache$12.40
Other services$8.80
Total Estimated Cost$96.00/month

Generated files:

├── main.tf

├── variables.tf

├── outputs.tf

├── terraform.tfvars

└── README.md

$ cat main.tf

provider "aws" {

region = var.aws_region

}

module "vpc" {

source = "./modules/vpc"

# VPC configuration

}

module "ecs" {

source = "./modules/ecs"

# ECS configuration

}

Step 4

Terraform Generation

InfraMate uses a template-based approach to generate Terraform files that are ready to deploy.

Templates are stored in the templates/aws/terraform/ directory and combined based on the recommended services.

The following files are generated:

  • main.tf: Primary infrastructure definition
  • variables.tf: Variable definitions
  • outputs.tf: Output declarations
  • terraform.tfvars: Default variable values
  • README.md: Documentation including cost estimates and deployment instructions
Step 5

CI/CD Automation

InfraMate includes a full Terraform CI/CD pipeline that automates your infrastructure deployment workflow.

  • Automated Terraform plan execution
  • Security scanning with tfsec
  • Optional deployment after approval
  • PR comments with results of each step
  • Cost estimation included in the PR
Pull Request #42: Add RDS Database

Terraform Plan: Success

Plan: 3 to add, 0 to change, 0 to destroy

Security Scan: Passed

No security issues found with tfsec

Cost Estimation

Monthly cost: +$29.20

RDS db.t3.small instance with 20GB storage

Deployment: Awaiting Approval

Approve to deploy these changes

Proactive Optimization Alert

Detected potential cost savings

Your RDS instance has been running at low utilization (15%) for the past 30 days.

Recommendation: Downsize from db.t3.small to db.t3.micro to save $14.60/month

Performance improvement opportunity

API response times have increased by 25% during peak hours.

Recommendation: Enable auto-scaling for your ECS service with a target CPU utilization of 70%

Learning from Your Environment

InfraMate has analyzed 30 days of metrics and identified these patterns:

  • Traffic spikes every Monday at 9am EST
  • Database connections peak on weekends
  • Cache hit ratio decreases during promotional events
Step 6

Continuous Learning & Adaptation

InfraMate's agentic AI continuously monitors your infrastructure, learns from patterns, and proactively suggests optimizations.

  • Autonomous monitoring of infrastructure metrics
  • Proactive cost optimization recommendations
  • Performance improvement suggestions based on usage patterns
  • Adaptive infrastructure that evolves with your application needs

Ready for Intelligent, Autonomous Infrastructure?

Connect your repository and let our agentic AI transform your infrastructure with intelligent, autonomous decisions.