15-Day Process

From Legacy APIs to AI-Native in 4 Steps

No 18-month roadmaps. No million-dollar consulting engagements. Just a clear path from assessment to deployed MCP servers.

5 Minutes

Step 1: ROI Assessment

Start with a free assessment that quantifies your automation potential. Select your industry, enter key metrics, and get instant ROI projections—no sales call required.

What Happens

  • Select industry (Healthcare, Automotive, Financial Services, etc.)
  • Enter operational metrics (revenue, headcount, key workflows)
  • AI analyzes 200+ data points against industry benchmarks
  • Get instant EBITDA impact and payback projections
  • Identify highest-impact automation opportunities

Know exactly what AI transformation can deliver before you invest a dollar

ROI Assessment Dashboard
Day 1-7

Step 2: API Discovery

Upload your OpenAPI specifications. MigrateForce parses every endpoint, automatically suggests MCP tool mappings, and lets you customize the configuration before code generation.

What Happens

  • Upload OpenAPI 2.0 or 3.x specs (JSON or YAML)
  • Automatic endpoint parsing and validation
  • AI-suggested MCP tool names and descriptions
  • Custom pre/post request hooks for each endpoint
  • Include/exclude specific endpoints from generation

What typically takes weeks of manual mapping happens in minutes

API Discovery and Mapping Interface
One Click

Step 3: MCP Server Generation

One click generates a complete, production-ready MCP server package. Python-based FastAPI server with Docker packaging, documentation, and deployment instructions included.

What Happens

  • main.py - FastAPI-based MCP server
  • Dockerfile - Production-ready container
  • requirements.txt - All Python dependencies
  • README.md - Deployment instructions
  • .env.example - Environment configuration template
  • mcp-manifest.json - MCP discovery manifest

Download a complete, deployable package—no manual coding required

Generated MCP Server Package
Day 8-15

Step 4: Deploy & Connect

Deploy your MCP server to any infrastructure—cloud, on-premise, or containerized. Connect to any LLM platform and start enabling AI agents to interact with your systems.

What Happens

  • Deploy to AWS, GCP, Azure, or on-premise
  • Docker support for containerized deployments
  • Connect to Claude, GPT, or any MCP-compatible platform
  • Enable AI agents to execute your API operations
  • Role-based access control for generated tools

Your APIs are now AI-native—agents can discover and use them automatically

MCP Server Deployment

Platform Performance

15 days

Average deployment time

92%

ROI projection accuracy

73%

Show 40%+ automation potential

Start Your Free Assessment

Takes 5 minutes. No sales call required.