AI engineering service
Custom AI systems for problems off-the-shelf tools do not solve.
When your workflow, data, or product needs something specific, we design and engineer the AI system end to end — models, data pipelines, application layer, and the operations to keep it healthy.
The problem
Some problems need more than a subscription.
Packaged AI products cover common cases. When your data is unusual, your workflow is a differentiator, or you need control over cost and behaviour, a custom system is often the better investment.
What we typically hear
- Packaged tools do not fit the shape of your data or process.
- Per-seat pricing becomes expensive at scale.
- You need ownership of the system and its improvements.
- Specialised tasks — vision, forecasting, classification — need trained models.
- Integrating several vendor tools has become its own problem.
What we build
What we build
LLM applications
Systems built around large language models with retrieval, tools, and structured outputs.
Machine learning models
Classification, prediction, and ranking models trained on your data where that outperforms general models.
Computer vision
Image and document understanding for inspection, extraction, and categorisation tasks.
Data pipelines
Collection, cleaning, labelling, and feature pipelines that feed models reliably.
Application layer
The interfaces, APIs, and back end that make the AI usable by people and systems.
MLOps
Versioning, deployment, monitoring, and retraining processes.
Typical use cases
Where this work pays off
Domain-specific assistant
01An AI system trained on and grounded in a specialised domain's terminology and rules.
Recommendation engine
02Personalised product or content recommendations based on behaviour and attributes.
Visual inspection
03Models that classify or flag images for review.
Demand or risk scoring
04Predictive models that prioritise work for human teams.
Architecture
Custom AI lifecycle
- 1
Data
Collect, clean, and label the right data.
- 2
Model
Select, prompt, train, or fine-tune.
- 3
Evaluate
Measure against agreed metrics.
- 4
Serve
Deploy behind an API with monitoring.
- 5Human
Review
Experts review outputs and edge cases.
Technology considerations
Decisions we make with you
Technology choices follow from the workflow, data, and constraints — not the other way around.
Model strategy
Hosted models, open-weight models, or custom-trained models — chosen per task.
Hosting
Managed APIs, your cloud, or dedicated infrastructure depending on data requirements.
Evaluation
Task-specific metrics agreed before training starts.
Ownership
You own the code, configuration, and any models trained on your data, as agreed in contract.
Delivery
How the engagement runs
01
Feasibility
Validate that the data and approach can meet the goal.
02
Prototype
Build a minimal end-to-end version to test assumptions.
03
Engineer
Production build of models, pipelines, and application.
04
Validate
Evaluate with real data and domain experts.
05
Operate
Monitor, retrain, and improve.
Security & governance
Control is designed in, not added later.
Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.
- Documented training data sources and consent
- Bias and failure-mode review for decision-support models
- Model versioning and rollback
- Access controls on training data and model endpoints
- Expert review for high-impact outputs
FAQ
Frequently asked questions
01When is custom AI better than an existing product?
When your data or process is specific, when you need control over cost and behaviour, or when the AI system is part of your competitive advantage. We will tell you if an existing product is the better choice.
02Do we own what you build?
Ownership terms are agreed in contract. Our standard approach is that clients own the code and configuration built for them.
03How much data do we need?
It depends on the approach. Many LLM-based systems need little training data, while custom ML models need representative labelled examples. We assess this during feasibility.
Related reading
Go deeper on this topic
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Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal