This is how we work.
We are an AI consultancy. We do data science, machine learning, LLM and agent systems, recommendation systems, computer vision, voice, and the data engineering that holds all of it up. Our clients come to us because they have a hard problem and a deadline, not because they want a research paper.
The thing that makes us useful is not that we know more models. It is that we run research the way good engineering teams run software. Version control, tests, continuous integration, code review, small visible increments, honest measurement. Plenty of well funded research teams do none of this, which is why so much of their work never leaves a notebook.
This playbook is our own operating manual. We wrote it for ourselves: to onboard new people, to settle arguments, and to hold ourselves to a standard when a deadline makes it tempting to skip a step. We publish it because a client deserves to know how their money will be spent before they spend it, and because most of what goes wrong in AI projects is process, not mathematics.
It describes the standard we hold ourselves to and are still building out in places. Where a practice depends on the size or shape of an engagement, we say so.
The five parts
What We Believe
Ten principles that decide the arguments. Baselines before models, evaluation before implementation, negative results as real results, and the reasons we sometimes tell a client not to build the thing.
How We Work
How an engagement starts. The first call, what happens in each week of a two week discovery, and what you receive at the end of it. The one sentence we ask every client to write, why measurement comes before building, what a stopping rule is actually for, and who does the work.
Working Together
The longer part. The four ways we engage, from full delivery to building your own team and leaving. What a week looks like, what we need from you, what you own, and what happens after handover.
Research and Evaluation
How we run experiments so the results can be trusted and repeated. Framing a question, building a baseline, tracking runs, analysing errors, calibrating model judges, and building the evaluation before the model exists.
Engineering
How research becomes a system somebody else can operate. Testing, continuous integration, deployment, monitoring, cost control, data governance, the specific production checks we run, our own use of AI coding tools, and handover.
Who this is for
Read it if you are considering hiring us and want to know what you would actually be buying. The services pages describe what we sell; this describes how it is done.
Read it if you work with us, on either side, and want to know what to expect week to week.
Read it if you run an AI or data team anywhere and want to steal something. Most of this is not proprietary. It is standard engineering discipline applied to work that usually escapes it, and the industry would be in better shape if more teams did it.
A living document
Every practice here earned its place by surviving a real project. Some of them replaced practices that did not. We revise this document when we learn something, not on a schedule, and we would rather change our minds in public than defend a position we have stopped believing.
If something here is wrong, or if you have a better way, tell us. We are at hello@boringai.tech.
Start at the beginning: What We Believe
Want this applied to your problem?
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