What is APES?
APES (AI-first Product Engineering, with Specs) is a product development methodology and architectural framework for rapidly shipping well-architected products securely and at minimal cost using AI coding agents.
🤖 Accelerated Product Engineering
Speeds every step of the process from idea -> product definition -> implementation -> deployment
🏗️ Production Ready
Validated microservice architecture with established patterns for security, scalability, and expandability
🧠 Context-Optimized for AI
Preventing context bloat is one of the biggest challenges when working with coding agents. Our role-based approach ensures that just the right information is available for the task at hand.
Why APES?
🚀 Ship Faster
Rapid MVP delivery with production-ready patterns and service templates
- Modern, production-grade web stacks
- Reusable Terraform modules
- Effective architecture decisions are already made for you with the flexibility to easily adjust based on your needs.
💰 Costs Less
Cost-optimized cloud infrastructure from day one
- Pay only for compute usage with serverless containers
- Open source ingress networking saves drastically over AWS managed NAT services
- Automated cost monitoring and budget alerts
📈 Scale Better
Production-grade architecture and Spec-Driven Development (SDD) process
- Built to scale as your business grows
- SDD enables confident feature additions
- Clear documentation simplifies onboarding
How It Works
APES structures your product engineering process into clear phases that AI agents can execute efficiently.
Define Your Product
Start with epics that define major features and user journeys. Each epic breaks down into detailed specs.
Role-Based Development
Load only the context you need. Full-Stack, DevOps, Architect, or Product Owner—each role gets optimized context.
Implement with AI
Coding agents implement specs across your components with full awareness of your architecture and standards.
Deploy Securely
Production-ready security, monitoring, and scalability from day one. Cost-optimized infrastructure managed by Terraform
GridPulse: built by APES
APES began as a hypothesis: Could AI coding agents enable a fundamentally different approach to product engineering?
The Challenge:
Build a methodology that leverages AI not just for faster coding, but for accelerated product discovery, architecture, and implementation—all while maintaining quality and reducing costs.
The Validation:
Rather than theorize, we built GridPulse as a real-world solution. If the methodology worked, we'd deliver production-grade infrastructure in a complex domain (energy analytics) in record time.
The Result:
GridPulse is proving the hypothesis. So far, we have validated:
- Production microservice infrastructure on AWS ECS Fargate
- Zero-trust security with CloudFlare Tunnel
- Automated monitoring and deployment pipeline
The Extraction:
Once validated, we formalized the patterns that worked into the APES framework:
- Role-based AI context system
- Epic-to-implementation methodology
- Reusable architectural templates
- Production-ready infrastructure patterns
Tech Stack Proven:
- React Router 7 SSR webapp
- Python FastAPI microservices
- Terraform-managed infrastructure
- PostgreSQL with Supabase
The APES Advantage: A framework born from intentional methodology design and validated through real production requirements.
Ready to build your v1?
Let's discuss how APES can help you ship a scalable, secure product.
Schedule a demo