Insights
Clear answers to product questions.
Short, practical explanations for teams deciding what to build, how much AI to use, and where complexity actually belongs. Each one starts with the answer.
- What is AI product development?AI product development turns model capability into a usable, evaluated and dependable product by combining software engineering, workflow design, evaluation and governance.
- Agentic AI vs automationAgentic AI is useful when a system must choose and sequence actions across changing state. Deterministic automation is better when the workflow can be specified explicitly.
- What is the Model Context Protocol (MCP)?The Model Context Protocol (MCP) is an open standard that lets AI applications connect to tools, data and workflows through one consistent interface. Here is what it is and when a product should use it.
- When should you not use AI?Do not use AI when correct behaviour can be specified deterministically, the error cost is disproportionate, the data is inadequate or the model adds complexity without changing the outcome.
- How to evaluate an AI productEvaluate an AI product at the task level: define success, representative cases, failure modes, thresholds, regressions, operational behaviour and human review.
- What makes a good software MVP?A good MVP tests the product thesis with the smallest coherent product that can create evidence, rather than building a lower-fidelity version of the final roadmap.
- How to scope a digital productScope a digital product by defining the problem, target workflow, product boundary, riskiest assumptions, architecture and evidence required from the first release.
- Should you build or buy software?Buy software when your need is common and the process is not a source of advantage. Build when the workflow is distinctive, integration is the product, or off-the-shelf tools force costly workarounds.
- How to choose a software development partnerChoose a software development partner by how they reduce risk: how they scope, what they would remove, how they test assumptions, who owns the code, and what happens after launch.
Build what matters.
Tell us what you are trying to build, change or understand. A first conversation is about the problem, not the feature list.