Insights
What we think about building AI systems that run.
Positions first, reference underneath. The pieces at the top argue for something — including things that cost us work to say — and the explanations below them are the shared vocabulary those arguments assume. No client examples and no performance figures, the same standard as the rest of this site.
We would rather publish the reasoning behind a decision than a case study we cannot let you verify. Where an argument here costs us work — the piece on why most agent projects should not be agents is the clearest example — that is the point of it, not an oversight.
Drafted with AI assistance and edited by the Bralak engineering team. No client examples, performance figures or benchmark comparisons appear in these pieces — every technical claim is one a reader can check independently.
All articles
Eight pieces, five subjects.
Each is written to a question we are actually asked — what a thing is, when it is the wrong choice, or what breaks when it meets production.
Agents
When an AI Agent Is the Wrong Answer
Most of the systems we are asked to build as agents should not be agents. The autonomy that makes an agent impressive is a cost, it is paid on every run, and there is a specific test for whether you are getting anything back for it.
Production
What Breaks Between a Prototype and Production
The prototype worked. The production system is late, and nobody can say why the estimate was wrong. It was not wrong about the model — it was wrong about which parts of the problem the prototype was allowed to skip.
Agents
What Is an AI Agent?
An agent is a system that decides what to do next. That single property is what separates it from the automation you already run and from the chatbot you already have — and it is also what makes it harder to build.
RAG
What Is RAG?
Retrieval-augmented generation closes the gap between what a model was trained on and what your organisation actually knows. The interesting part is not the generation. It is that a well-built RAG system changes what happens when it does not know.
Agents
AI Agents vs Chatbots
The comparison is usually framed as a contest of conversational quality. It is not. The line between the two is consequence — whether the system can change anything — and everything that matters about building either one follows from which side of it you are on.
RAG
RAG vs Fine-Tuning
These are not two ways of doing the same thing. One changes what a model can look up; the other changes how it behaves. Choosing between them is easy once you know which of those you actually need — and most business cases need the first.
Voice
How AI Voice Agents Work
A voice agent is a text system with two conversions bolted to its ends and a stopwatch running. Understanding why latency, rather than accuracy, is the binding constraint explains almost every design decision in one.
Strategy
How to Identify AI Opportunities in Your Business
Most AI programmes do not fail on technology. They fail because the first project was chosen for how interesting it sounded rather than for whether it could be finished. Here is an audit you can run yourself, before anybody is asked for a budget.
Your Next Intelligent System Starts Here.
Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path.