Thinking out loud
Practical thinking on AI systems architecture, business automation, platform engineering, and technical leadership — written for founders, CTOs, and technical teams building with AI.
Featured / AI Systems / 11 min read
Most companies that invest in AI see disappointing results. The failure is rarely the AI. It is almost always the integration.
Read the article ↗Architecture / 10 min read
The distance between an impressive AI demo and a production AI system is measured in infrastructure layers, not intelligence.
Read the article ↗Business Automation / 9 min read
AI tools give individuals leverage. AI systems give organisations leverage. Knowing when to make the shift is one of the highest-value decisions a technical leader can make.
Read the article ↗Architecture / 10 min read
The build-vs-buy decision for AI is not one decision. It is a stack of decisions, one per layer, and the wrong default at any layer will cost you months.
Read the article ↗AI Systems / 9 min read
Retrieval-Augmented Generation is the most widely deployed AI pattern in enterprise software. Here is what it actually is, how it works, and when it is the right choice.
Read the article ↗AI Systems / 8 min read
Model selection is a product decision, not a benchmarks exercise. The right model is the one that fits your use case, budget, and latency requirements — not the one that scored highest on an academic leaderboard.
Read the article ↗AI Systems / 8 min read
AI agents are one of the most overhyped and most misapplied patterns in modern AI engineering. Here is what they actually are, where they work, and where they do not.
Read the article ↗AI Systems / 8 min read
Fine-tuning promises better performance. Prompt engineering promises faster iteration. Choosing correctly between them — and at the right moment — makes or breaks AI project economics.
Read the article ↗AI Systems / 8 min read
Hallucination is not a model bug to be fixed in the next release. It is a structural property of LLMs that has to be managed through system design.
Read the article ↗AI Systems / 7 min read
LLM API costs are deceptively cheap at prototype scale and surprisingly expensive at production scale. The teams that control costs do three specific things.
Read the article ↗Business Automation / 8 min read
Overselling AI projects creates expectation debt. Building a credible, conservative business case is the foundation for AI investments that deliver sustainable returns.
Read the article ↗Business Automation / 7 min read
Most AI automation projects cannot demonstrate their ROI 12 months after launch. The problem is not the AI — it is the absence of measurement infrastructure.
Read the article ↗Business Automation / 9 min read
Document processing is the highest-value first target for AI automation in most businesses. Getting the architecture right from the start determines whether you end up with a system that scales or one that requires constant firefighting.
Read the article ↗Business Automation / 7 min read
RPA and AI automation are complementary, not competing. Understanding what each does well — and where each breaks — prevents expensive mis-deployments.
Read the article ↗Business Automation / 8 min read
A well-designed pilot answers the question the organisation actually needs answered: will this automation work reliably enough, at sufficient quality, to justify full deployment?
Read the article ↗Business Automation / 8 min read
Professional services firms run on document-intensive, knowledge-intensive workflows that AI is particularly well-suited to accelerate. These five processes deliver the clearest ROI.
Read the article ↗Technical Leadership / 8 min read
The AI engineering market is full of people who are excellent at building prototypes and weak at building systems. The interview process that reveals which is which is not the standard software engineering interview.
Read the article ↗Technical Leadership / 9 min read
A CEO does not need to understand how transformers work to make good AI strategy decisions. But they do need a framework for evaluating AI opportunities without being captured by technical enthusiasm or vendor marketing.
Read the article ↗Technical Leadership / 8 min read
The hire-vs-build decision for AI expertise is more nuanced than for most capabilities. The right answer depends on your timeline, the specificity of your use case, and what you want to own long-term.
Read the article ↗Technical Leadership / 8 min read
AI projects fail for reasons that traditional project management does not anticipate. The differences are not cosmetic — they require genuinely different approaches to planning, estimation, and risk management.
Read the article ↗Architecture / 9 min read
The data pipeline is the most underestimated component of an AI system. A well-designed pipeline is the foundation that makes everything else reliable.
Read the article ↗Architecture / 7 min read
The vector database market has matured rapidly. The decision is less about which database is "best" and more about which trade-offs fit your specific access patterns, scale, and infrastructure.
Read the article ↗Architecture / 8 min read
AI APIs have different characteristics from traditional APIs — higher latency, probabilistic outputs, streaming requirements, and variable cost per call. Designing for these characteristics from the start prevents painful refactors.
Read the article ↗Technical Leadership / 9 min read
AI governance is not a compliance exercise. It is the operational structure that allows organisations to deploy AI at scale without losing control of quality, risk, and accountability.
Read the article ↗Business Automation / 8 min read
Process mapping for AI is different from standard process documentation. You are not just capturing what happens — you are identifying the inputs, outputs, decision points, and exception patterns that determine whether AI can automate reliably.
Read the article ↗Business Automation / 7 min read
Client onboarding is one of the highest-leverage automation targets: it is high-volume, document-intensive, and rule-based, but the client experience depends on feeling personally attended to.
Read the article ↗Architecture / 8 min read
Event-driven architecture pairs naturally with AI workloads that process streams of inputs asynchronously. But most AI workloads do not need it — knowing the difference prevents unnecessary complexity.
Read the article ↗Architecture / 7 min read
Semantic caching returns cached LLM responses for semantically similar queries — without requiring identical inputs. At scale, it is one of the most effective cost reduction techniques available.
Read the article ↗Technical Leadership / 8 min read
Fractional CTO is one of the most misunderstood roles in the startup and scale-up ecosystem. This is what it actually involves week to week — and what it is not.
Read the article ↗Technical Leadership / 7 min read
The skill gap in AI engineering is real but specific. Most engineering teams do not need to become ML researchers — they need to develop a targeted set of production AI engineering skills.
Read the article ↗Architecture / 8 min read
Multi-tenancy in AI systems is harder than in traditional software because LLM context windows, shared caches, and fine-tuned models create subtle isolation challenges that do not exist in standard web applications.
Read the article ↗AI Systems / 9 min read
AI customer support is one of the most deployed and most often poorly deployed AI use cases. The gap between a system that annoys customers and one that delights them is in the design, not the model.
Read the article ↗Business Automation / 7 min read
AI sales qualification automates the research and scoring work that currently takes sales reps 30-45 minutes per lead. Done well, it frees reps for the relationship work that actually closes deals.
Read the article ↗Technical Leadership / 8 min read
Every business has more AI opportunities than capacity to pursue. The prioritisation framework determines whether you spend your AI investment where it creates the most value or where it creates the most interest.
Read the article ↗AI Systems / 8 min read
You cannot improve what you cannot measure. Building programmatic LLM evaluation is the infrastructure that separates AI systems that get better over time from ones that degrade silently.
Read the article ↗AI Systems / 7 min read
Few-shot examples are not always better than zero-shot instructions. Knowing when each technique is appropriate — and why — is one of the most practical skills in LLM application engineering.
Read the article ↗Business Automation / 8 min read
AI legal document review is one of the most rapidly maturing AI applications in professional services. The capability is real — but the risk profile requires careful system design.
Read the article ↗Technical Leadership / 7 min read
AI literacy is not a one-day training event. It is a cultural capability built through sustained exposure, real use cases, and leadership that models AI-informed thinking.
Read the article ↗Business Automation / 7 min read
Business reporting consumes significant analyst time on work that is largely mechanical: data gathering, formatting, and first-draft narrative. AI automates the mechanical layer so analysts focus on insight.
Read the article ↗Architecture / 8 min read
Testing AI systems requires fundamentally different approaches from testing traditional software. The output space is infinite, correctness is probabilistic, and the failure modes are subtle.
Read the article ↗Business Automation / 7 min read
Procurement processes are document-heavy, rule-intensive, and slow — exactly the profile that AI automation addresses most effectively.
Read the article ↗Architecture / 7 min read
Context window limits constrain what information you can give an LLM in a single call. Managing this constraint is an engineering discipline with significant quality and cost implications.
Read the article ↗Architecture / 9 min read
An AI platform is the shared infrastructure that enables the organisation to build AI systems faster and cheaper. Building it early pays compound returns; building it late means rebuilding every project from scratch.
Read the article ↗Technical Leadership / 7 min read
Board-level AI communication fails in two modes: overselling capability that does not yet exist, or burying progress in technical detail that boards cannot evaluate. There is a precise middle path.
Read the article ↗Business Automation / 8 min read
The quality challenges that are manageable at 100 transactions per day become serious at 10,000. Scaling AI automation requires architectural decisions that are hard to retrofit once you have committed.
Read the article ↗Business Automation / 8 min read
Financial document processing is one of the highest-volume, most rule-intensive automation targets in any organisation. AI brings accuracy and speed — with a risk profile that requires careful design.
Read the article ↗Architecture / 8 min read
Reliable AI systems are not systems that never fail. They are systems that fail gracefully, recover quickly, and always give users a usable experience even when the AI component is unavailable.
Read the article ↗AI Systems / 8 min read
A knowledge base is only as good as the content it contains and the structure it imposes on that content. Most knowledge bases that fail AI applications fail because of content quality, not AI quality.
Read the article ↗Architecture / 7 min read
AI systems degrade in ways that traditional error monitoring does not catch. The alerts that matter for AI systems are different from the alerts that matter for conventional software.
Read the article ↗Technical Leadership / 9 min read
AI regulation is evolving faster than most organisations can track. Technical leaders who understand the regulatory landscape now will make architectural decisions that are defensible rather than expensive to retrofit.
Read the article ↗A good place to start
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