AI and software product 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 through engineering, workflow design, evaluation and governance.
- Agentic AI vs automationAgentic AI suits work that means choosing and sequencing actions across changing state. Deterministic automation suits workflows you can specify explicitly.
- What is the Model Context Protocol (MCP)?The Model Context Protocol (MCP) is an open standard that connects AI applications to tools, data and workflows. What it is, and when a product should use it.
- When should you use AI?Use AI where language, interpretation or uncertain reasoning creates the value. Use deterministic software where the correct behaviour can be written as a rule.
- 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 about the riskiest assumption.
- How to scope a digital productScope a digital product by defining the problem, workflow, product boundary, riskiest assumptions, architecture and the evidence the first release must produce.
- Should you build or buy software?Buy software when the need is common and the process standard. Build when the workflow is distinctive, integration is the product, or workarounds cost more.
- How to choose a software development partnerChoose a software development partner by how they reduce risk: how they scope, what they would remove, 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 starts with the problem.