πŸš€ Our Development Process

From Idea to Production in 8-12 Weeks

Our proven AI development methodology combines agile practices with enterprise-grade rigor to deliver custom AI solutions that transform your business operations.

OpenAI v5.2 (GPT-4) Claude Opus & Sonnet AWS Bedrock LlamaIndex RAG MCP Servers
1

Discovery & Assessment

Week 1-2

We begin with a comprehensive analysis of your business workflows, identifying automation opportunities and defining clear success metrics. Our team evaluates which AI models and frameworks best fit your specific use case.

πŸ“‹ What We Do

  • βœ“ Workflow analysis and process mapping
  • βœ“ Automation opportunity identification
  • βœ“ ROI projection and success metrics
  • βœ“ Technical requirements gathering

πŸ“¦ Deliverables

  • β†’ Discovery Report with recommendations
  • β†’ Process flow diagrams
  • β†’ Technical feasibility assessment
  • β†’ Project timeline and milestones
2

Architecture & Design

Week 2-4

We design the AI agent architecture, selecting the optimal combination of models and frameworks. This phase establishes comprehensive test cases and validation criteria to ensure reliability.

πŸ”§ Technical Decisions

  • βœ“ Agent architecture design
  • βœ“ Model selection (GPT-4, Claude, Bedrock)
  • βœ“ Integration point mapping
  • βœ“ Security and compliance planning

πŸ“¦ Deliverables

  • β†’ System architecture document
  • β†’ API specifications
  • β†’ Test case documentation
  • β†’ Data flow diagrams
3

Development & Training

Week 4-8

Our engineers build the AI solution, configure models, implement RAG pipelines with LlamaIndex, and train the system with your data. Rigorous testing ensures reliability and accuracy.

⚑ Development Activities

  • βœ“ Agent development and configuration
  • βœ“ LlamaIndex RAG implementation
  • βœ“ MCP server connections
  • βœ“ Prompt engineering and fine-tuning

πŸ“¦ Deliverables

  • β†’ Functional AI agent in staging
  • β†’ Test results and validation reports
  • β†’ Performance benchmarks
  • β†’ Demo environment access
4

Integration & Deployment

Week 8-10

We deploy your AI solution to production, integrate with existing systems via MCP servers and APIs. Full documentation and team training ensure smooth handoff and adoption.

πŸš€ Deployment Steps

  • βœ“ Production deployment on AWS
  • βœ“ System integration testing
  • βœ“ User acceptance testing
  • βœ“ Go-live support

πŸ“¦ Deliverables

  • β†’ Production-ready AI solution
  • β†’ Team training sessions
  • β†’ Complete documentation
  • β†’ Admin dashboard access
5

Monitoring & Optimization

Week 10+

Post-deployment, we monitor performance, analyze usage patterns, and continuously improve the AI models. Regular updates keep your solution cutting-edge and aligned with your evolving needs.

πŸ“Š Ongoing Activities

  • βœ“ Performance monitoring & analytics
  • βœ“ Continuous model improvement
  • βœ“ Regular updates and new features
  • βœ“ Quarterly business reviews

πŸ“¦ What You Get

  • β†’ Monthly performance reports
  • β†’ Priority support access
  • β†’ Feature enhancement roadmap
  • β†’ ROI tracking dashboard

Technologies We Use

We leverage the most advanced AI models and frameworks to build solutions that deliver real business value.

πŸ€–

OpenAI v5.2

GPT-4 Turbo for natural language

🧠

Claude

Opus & Sonnet for reasoning

☁️

AWS Bedrock

Enterprise-grade infrastructure

πŸ“š

LlamaIndex

RAG for document intelligence

πŸ”Œ

MCP Servers

Custom API integrations

Ready to Start Your AI Journey?

Book a free consultation to discuss your project. Our team will analyze your needs and provide a custom roadmap for your AI transformation.

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