Make your data easier to trust and use.
Connected data pipelines, reporting and dashboards built around the decisions your business needs to make.
A new idea, an existing product or a platform ready for its next chapter.
Start with what your business needs.
Useful analytics starts with clear questions and shared definitions. A dashboard is only as dependable as the sources and logic behind it.
More about our approach
We connect operational data, model the measures that matter and build reporting for the people who use it. Source quality, ownership and review processes are part of the conversation, not problems left for a dashboard to conceal.
Explore what’s possible.
Select a capability to see how it can take shape. Your project brings the pieces together.
Data pipelines
Collection and transformation flows, tested, monitored and recoverable, from sources to warehouse.
Warehouse modelling
Metrics defined once and trusted everywhere, semantics your finance team will actually sign off on.
BI dashboards
Executive and operational surfaces tuned per audience, answers in seconds, not spreadsheet archaeology.
Event analytics
Product and marketing events instrumented properly, so funnels and cohorts reflect reality.
Reporting automation
The weekly and monthly reports generated, checked and delivered without human assembly.
ML-ready foundations
Clean, documented data layers that analytics and AI work can build on without re-plumbing.
Scope and acceptance criteria are agreed for your project.
Good experiences need strong foundations.
Look beyond the interface. Explore how access, quality and ongoing care fit into the plan.
Read the technical approach
Source systems, ingestion, transformations and reporting are mapped as one flow. Metric definitions, versioned logic and traceability help your team understand where a number comes from and how it changes.
Access & data
Access controls, sensitive data, authentication and relevant abuse paths are considered in the technical plan. Regulatory obligations and any specialist audit or security review are scoped explicitly with your team.
Quality & review
The plan can include schema, freshness and reconciliation checks, with owners for exceptions and changes. Reports are reviewed against source data and the questions they are intended to answer.
After delivery
Handover, documentation and any ongoing monitoring or improvement work are planned with your team. Support responsibilities and service arrangements are agreed for the engagement, rather than assumed to be included indefinitely.
Connections that matter
- Databases & SaaS sources
- CRMs & ERPs
- Product analytics
- Spreadsheets & files
- BI tools
- Alerting & digests
Technical requirements, checks and responsibilities are agreed as part of the project scope.
From first conversation to what’s next.
Explore the stages. Each one creates the clarity needed to move forward.
Map
Sources, consumers and current pain mapped; the metric definitions that matter agreed.
Model
Warehouse and semantics designed, one definition per metric, lineage documented.
Build
Pipelines, tests and dashboards delivered iteratively with your team in the loop.
Institutionalise
Alerts on data quality, training for users and a rhythm of refinement as questions mature.
The right tools for the job.
We select the stack around your requirements, existing systems and the team that will maintain the result.
Explore our technology approach
PostgreSQL- dbt
- Airflow
- Metabase
- Power BI
Python
Explore the work and thinking behind it.
Case studies and practical perspectives on the decisions that shape a project.
View case studiesResources to support the decisions behind your project.
Your sector. Your systems. Your priorities.
The application should reflect the people, processes and requirements of the business it serves.
A few things you might be wondering.
Clear answers before you decide how to move forward.
Have another question?Cost depends on scope, complexity, integrations and the level of ongoing involvement. We review your requirements before proposing an approach. Deliverables, assumptions, pricing and change handling are agreed in the proposal and contract.
We consider requirements, dependencies, content and data readiness, review cycles and testing. A delivery plan is agreed once those factors are understood. Staged work can help make progress visible without treating an early estimate as a guarantee.
Start with a recurring business decision and the data it needs. We map those sources and definitions, then build a focused pipeline or report before expanding to a wider platform.
Yes. We consider your current tools, licences, team familiarity and data environment. A new platform is recommended only when it solves a clear limitation.
Analytics engineering focuses on dependable data models, pipelines and reporting. Forecasting and machine learning build on those foundations, with additional requirements for evaluation and data suitability.
Handover, documentation, training, maintenance and further improvements can be included according to the engagement. Support coverage, response arrangements and responsibilities are agreed separately, so both teams know what happens after the initial work.
Data & Analytics frequently asked questions
Let’s build the right solution.
Tell us what you want to achieve, what you have today and where you need help. We’ll start with a conversation about the approach that fits.