From the Frontier
Insights on AI-native engineering, frontier AI workflows, and building production systems at speed.
consulting · enterprise
How to Choose an AI Engineering Partner (Without Getting Burned)
Most AI consultancies sell demos. Here's a practical framework for evaluating partners based on what actually predicts project success — and the red flags that predict failure.
enterprise · methodology
Measuring ROI on AI: The Metrics That Actually Matter
Most AI investments can't prove their value because nobody defined success upfront. Here's how to build a business case, pick the right metrics, and know whether your AI project is actually working.
methodology · consulting
When Not to Use AI: A Checklist from People Who Ship It
We build AI systems for a living, and we regularly talk clients out of using AI. Here are the situations where traditional software, human processes, or doing nothing will outperform any model.
production-systems · security
Vibe Coding Security: What AI Coding Tools Don't Check
AI coding tools like Cursor, Copilot, v0, and Bolt let you ship fast. But the code they generate has predictable security gaps. Here's what to look for — and how to fix it before someone else finds it.
ai-engineering · methodology
Forget Prompt Engineering — Context Engineering Is What Matters Now
Prompt engineering got all the attention, but the teams shipping real AI systems have moved on. Context engineering — controlling what the model sees, not just what you ask — is the discipline that separates demos from production.
enterprise · case-study · ai-engineering
Your FileMaker System Isn't the Problem — Your Migration Plan Is
FileMaker served your business for years. Now it's holding you back. Most migration plans fail because they treat it as a rewrite. Here's why extraction-first wins.
ai-engineering · claude-code · methodology
What AI-Native Engineering Actually Means (And Why It Matters)
AI-native isn't about bolting ChatGPT onto your existing workflow. It's a fundamental rethink of how software gets built. Here's what we've learned shipping production systems with Claude Code.
domain-intelligence · enterprise
The No-Code AI Trap: Why Low-Code Platforms Create High-Cost Problems
No-code AI platforms promise democratisation. They deliver vendor lock-in, limited customisation, and costs that scale faster than your business.
ai-engineering · methodology
The Software Engineer of 2027 Doesn't Look Like You Think
Software engineering is changing faster than any time since the internet. Here's what the role looks like in two years — and how to prepare.
production-systems · consulting
Startup Speed, Enterprise Quality: You Can Have Both
The idea that you have to choose between moving fast and building properly is outdated. AI-native engineering delivers both — and here's how.
ai-engineering · methodology
The Developer Experience Gap: Why Your Engineers Hate Your AI Tools
Most enterprise AI tooling has terrible developer experience. If your engineers aren't using the AI tools you bought them, the tools are the problem — not your engineers.
testing · production-systems
Shipping Fast Without Breaking Things: Our Testing Philosophy
Speed without quality is just recklessness. Here's how we ship production systems in weeks while maintaining the kind of test coverage that lets us sleep at night.
ai-engineering · enterprise
AI Agents in Business: Hype, Reality, and What Actually Works
AI agents are the hottest topic in tech. Most of what you've heard is hype. Here's what actually works in production today — and what's still science fiction.
enterprise · case-study
Legacy System Modernisation: The AI Shortcut Nobody Talks About
AI can read, understand, and translate legacy code faster than any human team. Here's how we use it to modernise systems in weeks instead of years.
ai-engineering · methodology
Your Team Doesn't Need AI Training — They Need AI Tooling
Stop sending your team to AI workshops. Instead, embed AI directly into their existing workflows. The best AI adoption happens when people don't even notice it.
production-systems · enterprise
Security for AI Systems: Beyond the OWASP Top 10
Traditional web security doesn't cover the attack surface of AI systems. Prompt injection, data poisoning, and model extraction need new security patterns.
production-systems · ai-engineering
Data Pipelines Are the Unsexy Foundation of Every AI System
Everyone wants to talk about models and prompts. Nobody wants to talk about data pipelines. But pipelines determine whether your AI system works in production or just in demos.
enterprise · consulting
Five Enterprise AI Mistakes We See Every Month
After working with dozens of enterprises, the same mistakes keep showing up. Here are the five most common — and how to avoid them.
consulting · methodology
Why We Don't Bill by the Hour
Hourly billing incentivises slowness. We charge for outcomes because AI makes the 'hours spent' metric meaningless — and our clients prefer it that way.
domain-intelligence · testing
You Can't Improve What You Can't Measure: AI Evaluation Done Right
Most AI systems ship without meaningful evaluation. Here's how to build evaluation frameworks that actually tell you whether your AI is working — before your users do.
production-systems · methodology
AI Doesn't Create Technical Debt — Bad Engineering Does
The fear that AI-generated code creates technical debt is backwards. AI-assisted code with proper engineering practices produces less debt than traditional development.
ai-engineering · case-study
The Internal Tool Renaissance: Why AI Makes Build-Your-Own Viable Again
For a decade, buying SaaS was cheaper than building. AI has flipped that equation. Internal tools are back — and they're better than anything you can buy.
enterprise · methodology
How Long Does an AI Project Actually Take?
Everyone asks how long an AI project takes. The honest answer is 'it depends' — but here's a practical framework for estimating timelines that actually holds up.
domain-intelligence · methodology
Why Domain Experts Should Control AI Systems (Not Engineers)
The biggest mistake in enterprise AI is treating it as a pure technology problem. The best AI systems are built by domain experts with engineering support — not the other way around.
claude-code · case-study
Claude Code in Production: What We've Learned After 50 Deployments
After shipping 50+ production systems with Claude Code, here are the patterns that work, the pitfalls that don't, and why the tool matters less than the methodology.
ai-engineering · methodology
Three Prototypes in a Day: How AI Changes Architecture Decisions
When prototyping costs hours instead of weeks, you can explore multiple architectures before committing. This changes how teams should make technical decisions.
enterprise · methodology
Build vs. Buy: When Custom AI Beats Off-the-Shelf
The build-vs-buy decision changes fundamentally when AI collapses development timelines. Here's our framework for when custom beats vendor — and when it doesn't.
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