Analytics engineering
Data analytics &business intelligence
Reliable data pipelines, a cloud data platform and clear reporting — from revenue and operations to cloud cost and engineering metrics.
Built with
Swipe to explore
Platforms & tools
Python
pandas
Apache Airflow
Apache Spark
Databricks
Snowflake
Amazon Redshift
Azure Synapse Analytics
AWS Glue
Azure Data Factory
Amazon Athena
Azure Machine Learning
Amazon S3
Azure Event Hubs
MLflow
Kubeflow
BigQuery
PostgreSQL
Overview
Data Analytics & Business Intelligence
We convert business and operational data into actionable insight using modern analytics pipelines, cloud data platforms and business intelligence solutions.
The problem we solve
Data sits across databases, SaaS tools, spreadsheets and cloud billing exports. Reports are assembled by hand, arrive late and often disagree, so decisions rely on instinct rather than evidence.
How we work
We agree the questions and KPIs that matter, build pipelines that bring data into a cloud data platform, model it into trusted datasets, then deliver reports and interactive visualisations people can explore.
What you get
A single, reliable source for key metrics, less manual reporting, and visibility into business performance, cloud spend and engineering delivery in one place.
Your business at a glance.
Revenue, growth and channel performance unified into one interactive view, built on modelled data. This is an interactive sample with demo data.
Insight Hub
dashboards / executive-overview
Revenue
▲ 12.4%$312,400
Orders
▲ 8.1%8,942
Conversion
▲ 0.6pt3.8%
Churn
▼ 0.4pt1.9%
Revenue vs target ($k)
Actual TargetChannel mix
100%
traffic
- Organic38%
- Paid24%
- Referral18%
- Email20%
Growth by region
Top products
- Pro subscription84%
- Analytics add-on66%
- Mobile app52%
- Integrations37%
Models that keep themselves accurate
When production data drifts, Python pipelines retrain, evaluate and redeploy the model automatically — tracked, versioned and rolled out safely.
Swipe to explore
- Detect
- Monitor model accuracy and data drift in production.
- Retrain
- Python pipelines pull fresh data, train and evaluate with MLflow tracking.
- Redeploy
- Register the new version and roll it out gradually on Kubernetes.
What's included
From raw data to trusted metrics and clear reporting.
Data integration & transformation
ETL/ELT pipelines that pull from databases, APIs and files, then clean and model the data.
Cloud data platform implementation
Warehouse or lakehouse setup on AWS, Azure or Google Cloud, sized for your data and team.
MLOps
Model training pipelines, experiment tracking with MLflow, model registries and automated retraining and deployment with Kubeflow or cloud ML services.
Python data automation
Python jobs (pandas, Airflow) that clean data, reconcile reports and automate repetitive analysis your team does by hand.
Business intelligence & operational reporting
Scheduled and self-service reports for leadership, operations and finance.
Interactive data visualisation
Interactive dashboards and charts with filters and drill-downs, built on modelled data.
KPI & performance analytics
Clear KPI definitions, targets and trend analysis that everyone reads the same way.
Cloud infrastructure cost analysis
Billing data broken down by team, service and environment to show where spend goes.
Engineering & deployment metrics
Delivery metrics such as deployment frequency and lead time drawn from your tooling.
Integration with existing systems
Connecting to your current databases, CRMs, ERPs and business applications.
Where teams usually start
Common ways teams work with us on data and analytics.
Executive KPI reporting
A shared view of the company's key metrics, refreshed automatically from source systems.
Data platform setup
A cloud data warehouse with ingestion pipelines and modelled, documented datasets.
Cloud cost visibility
Reporting on cloud spend by team, product and environment to support cost decisions.
Engineering delivery metrics
Deployment and pipeline data turned into delivery and reliability reporting.
Operational reporting
Inventory, orders or service operations tracked from the systems that already run them.
Spreadsheet to self-service BI
Replacing manual spreadsheet reporting with governed, self-service analytics.
FAQ
Questions about Data Analytics & BI
- Do you only build dashboards?
- No. Dashboards are one output. Most of the work is in reliable data pipelines, a well-modelled data platform and agreed KPI definitions, which make reporting trustworthy.
- Which BI tools do you use?
- We work with common BI and visualisation tools such as Power BI, Looker Studio and Grafana, and can fit in with what your team already uses.
- Do you help with MLOps?
- Yes, on the engineering side: training pipelines, experiment tracking, model registries, automated retraining when data drifts, and deploying models to production. Your data scientists own the models.
- Can you analyse our cloud and engineering data too?
- Yes. Cloud billing exports and CI/CD or deployment data can be brought into the same platform for cost and delivery reporting.
Ready to trust your numbers?
Tell us where your data lives and what you need to know. We'll outline a practical analytics plan.
Discuss your data