White Papers & Technical Resources
Deep dives into how we engineer software - methodologies, toolchains, and lessons learned.
Our AI-Augmented Development Methodology
Spec-Driven Design, Test-Driven Design, Human-in-the-Loop
Most teams prompt AI and hope for the best. We use a structured methodology built on three pillars: Spec-Driven Design encodes requirements before code. Test-Driven Design validates them with concrete examples. Human-in-the-Loop ensures AI augments judgment without replacing it. Covers the OpenSpec workflow, practical toolstack, and the patterns that close the gap between intent and shipped software.
Migrating Large-Scale Systems to the Cloud
A Risk Framework and 63-Point Operational Checklist
Cloud migration is four interconnected risks that compound under pressure. This white paper distills those risks, compares two approaches to managing them, and provides a prioritized 63-point checklist any team can adopt immediately - organized by impact, tagged by domain, and validated through failure injection.
How We Modernized a Critical Infrastructure Platform With Zero Downtime
From Lift-and-Shift to Cloud-Native Performance
Most cloud migrations start with lift-and-shift. The workload runs in the cloud, but the architecture and bottlenecks came along for the ride. This paper covers the four constraints that survive migration, the modernization spectrum, and the cloud-native patterns that deliver measurable results.
From Proof of Concept to Production
Why Most PoCs Fail to Ship - and How to Fix That
Most PoCs prove the idea works - then die in the gap between demo and deployment. This paper covers the five failure modes that kill PoCs, a phased framework for production readiness, and the engineering practices that bridge the gap between 'it works on my machine' and 'it runs in production.'
Scaling AI in Industrial Systems Without Starting Over
A Hypothesis-Driven Framework for Moving AI from Pilot to Plant Floor
87% of AI pilots in industrial settings never reach production. The gap is not the model - it is the missing engineering discipline between 'it works in the lab' and 'it runs on the plant floor at 2 AM.' This paper covers the four risks that kill industrial AI projects, a phased scaling framework grounded in hypothesis-driven experimentation, and the practices that bridge lab accuracy to operational reliability.
The Agentic GTM Framework
How Autonomous AI Agents Replace Your $1,400/mo Tool Stack
Most B2B SaaS companies pay for 6-10 separate GTM tools and use 20% of each. This paper covers the top-20% principle, the six-layer agentic framework, the agent stack, cost comparison, and deployment. Built by Eastgate for Eastgate - then offered to clients.
Want to See These Ideas in Action?
Schedule a 30-minute call. We'll walk you through how our methodologies apply to your project.
Faster Delivery
With AI-augmented workflows
Engineers
Using AI-augmented development daily
Client Retention
Partners, not vendors