AI and data
Most AI projects fail on data quality and integration rather than modelling. We start from the decision you are trying to improve, establish whether your data can support it, and build only what earns its place in production.
Discuss this workWhat this covers
- Applied AI: assistants, document processing, and classification
- Retrieval-augmented systems over your own knowledge base
- Forecasting and predictive models for planning and risk
- Data pipelines, warehousing, and reporting
- Process automation across existing systems
- Evaluation harnesses so model quality is measured, not assumed
What you receive
- Deployed models or services with monitoring in place
- Documented data pipeline and its assumptions
- Evaluation results, including where the system performs poorly
- Retraining and operating guidance
Typical engagement
Discovery engagement first, then a fixed-scope build once feasibility is established.
Tools we commonly use
Chosen per project against your constraints and what your team can maintain, not by habit.
- Python
- PyTorch
- scikit-learn
- LangChain
- dbt
- Airflow
- PostgreSQL
- Vector databases
How the engagement runs
Phase 01
Discover
We understand the challenge, the context around it, and the outcome your organisation needs.
Phase 02
Build
We turn the opportunity into a clear solution designed around your organisation and its users.
Phase 03
Deliver
We bring the solution to life and prepare it for use across the organisation.
Phase 04
Support
We help the system remain useful as your organisation grows and its needs change.
Need help with AI and data?
Describe the problem in a few sentences. We will come back with how we would approach it, what it would take, and what it would cost.