Content that helps you decide.
Practical thinking on AI adoption, governance, coding agents, and building engineering teams that work effectively with AI.
AI Governance
Practical frameworks for AI compliance, SOC 2 readiness, data handling policies, and audit-ready AI operations. Built for engineering leaders who need governance that works without slowing teams down.
ExploreAgentic Systems
Implementation patterns, deployment guides, and governance strategies for AI coding agents, autonomous workflows, and AI employees in engineering organisations.
ExplorePrivate LLMs & RAG
Architecture patterns, deployment guides, and cost-compliance trade-offs for private LLM hosting, retrieval-augmented generation, and enterprise knowledge systems.
ExploreCompliance and standards: a map for AI engineering teams
Customers and regulators expect organisations (and the engineering teams behind them) to explain how they handle data, secure systems, govern models, and prove software integrity. This guide maps the main laws and standards you are likely to meet.
VS Code and its forks: landscape in 2026
In 2026, Cursor, Windsurf, Kiro, Antigravity, Trae, and Qoder all compete on top of the same VS Code foundation. Teams choosing between them need to look past the editor UI and check agent behaviour, code/data flow, extension support, telemetry, and vendor risk.
From manual coding to an agentic SDLC: a team playbook
Moving from manual coding to an agentic software development lifecycle is not a tooling decision. It changes how tickets are written, PRs are reviewed, builds are trusted, and incidents are handled. Here is the staged sequence we use without overwhelming the team.
Golden datasets for testing AI: ten practical rules
Every AI feature in production needs a golden dataset, the small, curated set of examples that tells you whether the next model, prompt, or pipeline change made things better or worse. Here are the ten rules we use when we build one.
Atlas: how we measure AI adoption inside engineering teams
Most CTOs do not actually know who on their team is using AI, what they are using it for, which models are in play, or how much it is costing. Atlas plugs into the tools they already use, shows what is really happening, and turns it into a focused 90-day plan.
Code migration services in hyperscalers: what are the options
Let's take a look at what services AWS, Azure, and Google Cloud provide to customers to upgrade legacy enterprise systems.
Seven AI use cases that actually matter in production engineering
The most valuable AI applications are not the flashy demos. They are the high-friction, repetitive, context-heavy tasks that slow engineering teams down every day. A detailed breakdown of seven real-world scenarios.
Private LLM hosting patterns: Azure, Anthropic, and self-hosted compared
Architecture patterns and trade-offs for deploying LLMs without exposing sensitive data to public endpoints. Covers cost modelling, compliance implications, and operational complexity for each approach.
Coding agent evaluation matrix: Copilot, Cursor, Claude Code, and Codex
A practical comparison of AI coding agents across compliance, capability, and enterprise readiness dimensions. Built for engineering leads evaluating which tools to roll out to their teams.
Agent workflow patterns for enterprise engineering teams
Architecture patterns for deploying AI agents in production: tool use, human-in-the-loop supervision, multi-agent orchestration, and observability. Practical guidance for engineering leads.
How we structure RAG pipelines for enterprise knowledge retrieval
A technical walkthrough of retrieval-augmented generation architecture: chunking strategies, embedding selection, retrieval scoring, and the trade-offs we make in production deployments.
Setting up Claude Code for automated test generation
A practical walkthrough: configuring Claude Code skills for test generation, writing effective prompts, and measuring the impact on coverage and review cycles.
AI adoption checklist before rollout
Twelve questions every CTO should answer before deploying AI tools to engineering teams. Covers tooling, risk, and evidence.
AI coding agents and compliance: how to choose
A practical framework for evaluating AI coding assistants against your data handling, audit, and enterprise requirements.
Individual vs corporate AI plans: what changes
The shift from personal ChatGPT to a company-wide AI subscription is not just pricing. It's governance, IP, and team trust.