Services
From the first pipeline to production AI. Engage us for one piece or the whole chain — data, analytics, models and the architecture that ties them together.
Data engineering & platforms
Pipelines, warehouses and lakehouses that your team can trust. We design ingestion, modelling and orchestration so data arrives clean, on time and documented.
- ETL/ELT pipelines (Airflow, dbt, Dagster)
- Snowflake, BigQuery, Databricks, Postgres
- Data quality checks, lineage & governance
- Cloud cost and performance optimisation
BI & analytics
Dashboards people actually open. We define metrics once, build a semantic layer and ship reporting that answers real business questions.
- KPI frameworks & metric definitions
- Power BI, Tableau, Looker, Metabase
- Self-service analytics & semantic layers
- Product, marketing and financial analytics
Machine learning
Predictive models that move a number: demand forecasting, churn, pricing, scoring, recommendations, anomaly detection.
- Forecasting, classification, ranking
- Feature engineering & experiment design
- MLOps: training pipelines, registry, monitoring
- Model evaluation tied to business metrics
AI architecture & LLMs
Production-grade generative AI: retrieval-augmented assistants, agents and document automation built on your own data, safely.
- RAG, agents & LLM application design
- Vector search and knowledge pipelines
- Evaluation, guardrails & observability
- Build-vs-buy and vendor/model selection
Data science & research
Rigorous answers to hard questions: causal analysis, A/B testing, segmentation and statistical modelling.
- A/B testing & causal inference
- Customer segmentation & LTV
- Optimisation & simulation
- Exploratory research and data audits
Data & AI strategy
A roadmap from where your data is today to where it needs to be — prioritised by value, with realistic effort and cost.
- Data maturity assessment
- Use-case discovery & prioritisation
- Architecture reviews
- Team setup and hiring support
Ways to work together
Choose the format that fits your stage and budget.
Discovery sprint
Data audit, use-case prioritisation and a costed roadmap.
Proof of concept
A working solution on your data, measured against a baseline.
Dedicated team
Engineers and scientists embedded with your team to build and run.
Frequently asked questions
How does a typical engagement start?
With a free 45-minute call, then a short paid discovery (usually 1–2 weeks) where we look at your data, systems and goals and propose a scoped first project with a fixed price.
Do you work with our existing stack?
Yes. We are tool-agnostic and prefer to build on what you already have — cloud, warehouse, BI tool — unless there is a clear reason to change it.
Who owns the code and models?
You do. Everything we build lives in your repositories and cloud accounts, with documentation and a hand-over so your team can run it.
How do you handle sensitive data?
We work inside your environment under NDA, follow least-privilege access, and can design for GDPR and industry-specific requirements from day one.
Can you support us after launch?
Yes — through a monthly retainer for monitoring, improvements and new use cases, or by helping you hire and onboard your own data team.