Prediction and forecasting
Estimate demand, predict an outcome or identify unusual activity in structured records. We discuss which errors matter most and compare the model with an appropriate baseline.
Service 01 / Custom AI modelsA model built for
A model built for
your actual task.
Build a machine learning system around the decision, prediction or workflow your company needs. We start with your data and a measurable definition of success.
Discuss a model projectThe work we can scope
From business question
to a testable model.
Text and document workflows
Classify text, extract fields, search approved documents or adapt a language model to a bounded task. Evaluation includes relevant examples, failure cases and the limits of automated outputs.
Computer vision
Classify images, detect objects or inspect visual patterns. The scope accounts for annotation quality, operating conditions and the difference between training images and real use.
Recommendations and ranking
Rank relevant items or prioritise cases using the signals your product collects. We define an evaluation strategy that reflects the intended user experience and available feedback.
Evaluation matters
Know where the
model works.
A single score rarely explains enough. The evaluation plan should match the real decision, the available data and the cost of mistakes.
Separate training from evaluation
Define a holdout strategy that accounts for time, repeated entities and related records. Check for data leakage before drawing conclusions from performance.
Inspect errors, not just averages
Look at representative failures and relevant data slices. Record weak spots, confidence limits and cases that should be escalated to a person.
Agree on operational constraints
Latency, cost, data availability and the deployment environment influence the design. An accurate model can still be a poor fit for a particular workflow.
The handover
A usable result.
With its limitations.
- ✓Versioned model artifacts
The agreed model and the configuration needed for inference.
- ✓Evaluation and error report
Test methodology, metrics, examples and documented caveats.
- ✓A way to run it
Batch scripts, an API or integration instructions as specified in the scope.
- ✓Development documentation
Data requirements, dependencies and guidance for future revisions.
What helps us scope the work
You don’t need a technical specification.
Describe the current workflow and the task you want to improve. A short summary of your available data is enough for the first conversation.
- What should the system predict, find or produce?
- Who will use the output, and what will they do with it?
- What historical data, labels or examples are available?
- What mistakes, delays or costs are unacceptable?
- Does it need to fit into an existing product or process?
We may recommend a simpler rule, a data improvement or a feasibility study first. Model development starts when the use case and data support it.
Have a task in mind?
Describe the problem and the data. We’ll discuss a sensible starting point.