01 · AI Solutions
AI Model Engineering
Deciding what the model must do, what it learns from, and how it will be judged - before any training begins.
AI Solutions
AI Model Engineering
Deciding what the model must do, what it learns from, and how it will be judged - before any training begins.
Showing 1 of 5
- 1Frame the problem
- 2Assess feasibility
- 3Specify the data
- 4Choose the approach
- 5Agree success measures
What We Do for You
- Turn your business need into a task a model can perform.
- Establish the baseline the model must beat.
- Specify the data required, including the rare cases.
- Choose the approach and record why.
- Agree the measures that decide pass or fail before work starts.
How this is bought: Bought as an assessment first, then a project priced from what the assessment finds. Build an estimate for your case.
Our Approaches Explained
Problem framing
Turning a business need into a task a model can perform: classification, extraction, ranking, generation or forecasting.
Feasibility and baseline
Establishing what a simple rule or existing tool already achieves, so the model must beat something real.
Data requirement design
How many examples, of what kind, covering which cases - including the rare ones that matter.
Feature engineering
Preparing the inputs the model learns from, and recording why each was chosen.
Architecture selection
Choosing between a classical model, a fine-tuned model, or a retrieval approach on a general model.
Success criteria
The measures that decide pass or fail, agreed with the business before work starts.
Risk and bias assessment
Identifying who could be affected by a wrong answer and how that is detected.
Data licensing and provenance
Confirming ARRIX and the client are permitted to use every source in training.
The Standards We Work To
CRISP-DM and MLOps maturity modelsNIST AI RMF map functionModel cards and datasheets for datasetsFeature store patterns
We follow the structure and controls these standards describe. We do not claim to be certified against them - where you need a formal certificate, we prepare the evidence and an accredited body performs the audit.
What You Get
- Problem and success definition
- Data requirement specification
- Architecture decision record
- Risk and bias assessment
- Baseline comparison
Projects with a written baseline100%
Data provenance recorded95%
Risks assessed before build92%
These are the areas clients most often ask us to improve. Your project sets its own targets, measured and agreed with you.