Technology
An AI system is only as dependable as the engineering around it. We design the full stack — data, models, services, interfaces and operations — as one architecture.
How a system fits together
The layers we design and build for a typical AI product, from raw data to the person using it.
- 01
Data layer
Ingestion from source systems, validation, transformation and documented datasets with lineage.
- 02
Model layer
Experiment tracking, reproducible training, evaluation against baselines and explainability reports.
- 03
Service layer
Versioned model APIs, integration with existing software, authentication and audit logging.
- 04
Experience layer
Web and mobile interfaces that present outputs with context, confidence and reasons.
- 05
Operations
Automated deployment, performance and drift monitoring, alerting and scheduled re-validation.
Technical depth
The engineering disciplines behind every system we deliver. Tools are chosen per project; these are ones our team works with.
AI and machine learning
Classical ML, deep learning, computer vision, NLP and large language models.
- Python
- PyTorch
- TensorFlow
Explainable AI
Attribution, feature importance and stability analysis so outputs can be inspected.
Data engineering
Ingestion, transformation, quality checks and analytical storage.
- Python
- PostgreSQL
- Snowflake
- Power BI
Cloud architecture
Scalable, observable infrastructure on major cloud platforms.
- AWS
- Microsoft Azure
- Google Cloud
MLOps and DevOps
Reproducible training, versioning, deployment pipelines and monitoring.
- Docker
- Kubernetes
- GitHub Actions
Software engineering
Web, mobile and backend services that carry AI to users.
- React
- Next.js
- Node.js
- .NET
- Flutter
APIs and integration
Connecting AI services to existing records, tools and workflows.
Privacy-aware security
Access control, encryption and data minimisation designed in.
Need a technical view on your idea?
We can review your architecture, data readiness or model approach before you commit to a build.