Machine Learning & Computer Vision
Build models around the task your business needs to solve.
Develop predictive models and image-analysis systems, with representative evaluation and a practical route into your software or workflow.
Who it is for: Businesses with a repeatable classification, prediction or visual inspection task and access to relevant examples.
Illustrative workflow
- Define the task
- Check representative data
- Compare and evaluate
- Integrate with review
An example of a possible implementation, not a customer result.
What the project covers
What we can scope together
For example, assess a bounded image-classification task using an authorised sample, compare a baseline and candidate model on held-out examples, then demonstrate a reviewable output. Production integration is quoted against the agreed operating requirements.
- Feasibility review of the task, data and permissions
- A baseline and representative evaluation plan
- Model selection, adaptation or training where justified
- An interface or integration for agreed inputs and outputs
- Documented limitations, human review and handover
Boundaries and human review
Agree representative examples, acceptance criteria and exception handling. Review failed or uncertain actions before extending the scope, with clear ownership and operating guidance.
Extensions and project dependencies
Data volume, access requirements, integrations, security needs and the deployment environment determine the final scope and price.
Data preparation and labelling, model or API licences, training compute, hosting and ongoing inference are scoped explicitly. Dataset size, quality and deployment requirements determine the price.
From conversation to handover.
01
Define the task
Agree the decisions supported, the cost of mistakes and where a person remains responsible.
02
Assess the examples
Check permissions, labels, coverage and whether suitable evaluation data can be held back.
03
Compare approaches
Compare a simple baseline with suitable models, reporting errors and limitations as well as successes.
04
Integrate carefully
Scope deployment, review thresholds, monitoring and what happens when inputs differ from the examples.
Your implementation runs on accounts you control. Handover includes documentation and agreed acceptance checks; purchasing ongoing care is optional.
Your choice after handover
Optional care · From £100/month
One monthly health check and up to 30 minutes of minor fixes or changes. Unused time does not roll over. Response within two business days; this is not a resolution guarantee or continuous monitoring. Larger work, software and usage charges are additional.
You can operate the system yourself or agree a different support scope. Provider accounts and running costs remain separate.
Your questions
- Will we need a large dataset?
- It depends on the task. An existing model or simpler rule may be sufficient. We review representative examples before recommending collection, labelling or training work.
- Can you guarantee accuracy?
- No. We agree task-specific evaluation, report performance on held-out examples and make uncertain or consequential cases reviewable. A prototype result does not guarantee production performance.
- Can a model run in our existing application?
- We assess the application, latency, access and deployment environment before quoting integration. Cloud services and deployment within your environment can both be considered.
Talk through your process.
Tell us what happens today, which tools you use and what you want to improve. Art will review your enquiry and suggest a useful next step.