PIPELINES · WAREHOUSING · INTELLIGENCE
From raw data todecisions you can defend.
Analytics work fails at the pipeline far more often than at the dashboard. We build the ingestion, modelling and warehousing underneath — then the reporting and intelligence layers that sit on top of it.
CAPABILITIES
What we actually build.
Pipelines, warehousing and business intelligence — from raw sources to decisions.
01
Data engineering
Ingestion and transformation pipelines that run on a schedule, fail loudly, and can be re-run without corrupting downstream state.
02
ETL / ELT
Movement and reshaping of data between operational systems and the analytical model, with lineage you can trace.
03
Data warehousing
A modelled warehouse where each metric has one definition, rather than several that nearly agree.
04
Big data
Distributed processing for volumes where a single database has stopped being the right tool.
05
Business intelligence
Reporting built for the decision being made, not for the number of charts that fit on a screen.
06
Data visualisation
Interfaces that make the shape of the data legible — and make anomalies obvious rather than averaged away.
07
Data integration
Reconciling entities across systems that were never designed to agree on what a customer is.
08
AI-ready data
The structure, quality and governance that production AI depends on, built before the model work starts.
09
Data science
Analysis, segmentation and modelling applied to a specific question, with the assumptions stated.
OUTCOMES
What changes when this is done well.
One definition per metric
Departments stop arriving at meetings with different numbers for the same thing.
Pipelines you can trust
Monitoring, alerting and idempotent re-runs, so a failed job is an incident rather than a silent gap.
Analysis instead of assembly
Analysts spending their time on questions rather than on collecting and cleaning exports.
A foundation for AI
Well-governed data is the prerequisite for anything intelligent built on top of it.
TECHNOLOGY
The stack we work in.
Data
Pipelines, storage and reporting infrastructure underneath analytics and AI.
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Firebase
- Power BI
- Plotly
- Seaborn
- Dash
- Scala
Intelligence
Model, agent and vision tooling used to build the intelligence layer.
- OpenAI
- LangChain
- Python
- PyTorch
- TensorFlow
- TensorRT
- OpenCV
- Deepgram
- ElevenLabs
- Perplexity
Cloud & DevOps
Infrastructure, automation and the path from commit to production.
- AWS
- Azure
- Google Cloud
- Docker
- Kubernetes
- Terraform
- Jenkins
- Git
- Bitbucket
RELEVANT INDUSTRIES
Where domain knowledge changes the answer.
Insurance
Policy, claims and risk systems where regulation and accuracy are the product.
FinTech
Payments, lending and financial platforms where correctness is not negotiable.
Healthcare
Patient systems, clinical workflow and secure data infrastructure.
eCommerce
Custom commerce platforms, order management and marketplace systems.
FAQ
Common questions.
With a decision someone is currently making badly or slowly, traced back to the data it needs. Starting from a platform choice rather than a decision is the most common way these programmes stall.
It depends on how structured your sources are and what you intend to run on them. A modelled warehouse serves reporting well; a lake suits high-volume semi-structured data and ML workloads. Many organisations end up with both, and the important part is that the boundary between them is deliberate.
Yes. The modelling and pipeline layer matters more than the visualisation tool sitting on top, and we build so that the tool can be changed without rebuilding what feeds it.
NEXT STEP
Tell us what you're building.
Bring us the problem with its real constraints attached. We will tell you what we would build, and what we would not.