White Papers & Technical Resources
Deep dives into how we engineer software - methodologies, toolchains, and lessons learned.
Agent-Centric Development Cycle (ACDC): An AI-Native SDLC
Eastgate's own flavor of harness engineering
AI can shorten the time it takes to write code, but faster implementation only helps when the rest of delivery can keep up. ACDC gives AI-assisted work a shared structure: agents build from agreed specifications, tests check the expected behavior, and people remain responsible for decisions and release approval across all six lifecycle stages.
Modernizing Legacy Systems with Structured AI
Why modernization stalls, and how Eastgate's ACDC harness turns structure into savings
Legacy code is code without tests, and the safety net that fixes it was always too expensive to build. That is the cost AI agents actually attack. But pointed at unprotected code without a harness, AI makes experienced engineers 19% slower, not faster. This paper covers the Brownfield Paradox, the four-phase sequence of survey, protect, change, and compound, and exactly where humans keep the final say.
The Complete Checklists Behind Every Large-Scale Migration
A Risk Framework for Moving Mission-Critical Systems
Cloud migration failures rarely come from anything breaking outright. They come from five risk areas that compound under pressure. This white paper distills those risks, compares two approaches to managing them, and provides a self-scoring 52-checkpoint operational readiness checklist - weighted by risk tier, organized by category, ready to run with your team today.
How We Modernized a Critical Infrastructure Platform With No Downtime At All
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.
Beyond the Demo: Getting Your PoC Into Production
5 Reasons Promising Prototypes Stall 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.'
How to Scale Industrial AI Without Starting Over
A practical guide to moving from a successful pilot to reliable plant-floor operations
Most industrial AI pilots 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 stop industrial AI from scaling, a phased scaling framework grounded in hypothesis-driven experimentation, and the practices that bridge lab accuracy to operational reliability.
Choosing a Secure Offshore Engineering Partner in the Nordics
A practical guide to GDPR data transfers, NIS2 supply-chain security, and IEC 62443 secure development
Offshore engineering adds another layer to security and compliance reviews. Nordic enterprises need to know how a partner will protect personal data, manage cyber risk across the delivery chain, and develop secure software for industrial environments. This guide explains what to assess under GDPR, how NIS2 shapes supplier due diligence, when IEC 62443-4-1 is relevant, and which contracts, controls, and evidence to request before choosing a partner.
Choosing an Engineering Partner for APRA-Regulated Delivery
How to assess third-party risk, review CPS 230 and CPS 234 evidence, and select the right engagement model
APRA requires regulated organizations to manage risks from their service providers, and the organization itself remains responsible even when a partner delivers part of a key service. This guide explains how CPS 230 and CPS 234 affect the review of technology partners, which records to request, what questions to ask, and how to choose a delivery model that fits the risk.
The Agentic GTM Framework
How AI Agents Can Simplify Your GTM Tool Stack
B2B SaaS teams often manage their go-to-market work across several tools but only use a narrow set of features from each one. We built the Agentic GTM Framework around the workflows that had the clearest effect on our own pipeline. This paper explains how the six-layer engine works, which agents power it, what it costs compared to a traditional stack, and how we deploy it for clients.
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