What this engagement includes
We connect dashboards to authoritative systems so people stop debating which export is correct.
Engagements often pair with data cleanup or a warehouse initiative; we flag upstream prerequisites explicitly.
How we deliver
We tailor and sequence these workstreams around your priorities, timeline, and internal constraints.
Metric design
Definitions, dimensions, refresh expectations, and ownership for each KPI.
Data access
Secure queries, caching, and performance tuning for interactive use.
Visualisation layer
Charts, tables, filters, drill paths, and mobile-friendly layouts.
Reliability
Monitoring on pipelines, anomaly alerts, and documentation for analysts.
Our work
View our recent work below - each card links through to the live site.
We can walk through relevant case studies and references on a call - many of our clients ask for NDA-backed detail before we share specifics.
Typical flow
A reference sequence; we adapt depth and gates to your organisation.
- 01Frame
Questions & audiences
Who needs to see what, how often, and what decisions the UI should support.
- 02Source
Data readiness
Assess freshness, gaps, and whether a warehouse or API layer is required.
- 03Build
MVP dashboards
A focused set of views with validation against known benchmarks.
- 04Expand
Rollout & training
Additional cuts by role, self-serve guardrails, and iteration backlog.
| # | Stage | What happens |
|---|---|---|
| 01 | Frame Questions & audiences | Who needs to see what, how often, and what decisions the UI should support. |
| 02 | Source Data readiness | Assess freshness, gaps, and whether a warehouse or API layer is required. |
| 03 | Build MVP dashboards | A focused set of views with validation against known benchmarks. |
| 04 | Expand Rollout & training | Additional cuts by role, self-serve guardrails, and iteration backlog. |
Who we work with
Ops teams, revenue leaders, and executives - from growth-stage companies to multi-division enterprises.
Infrastructure
AWS, Microsoft Azure, Google Cloud, hybrid topologies, and mainstream SaaS - selected against your security, residency, latency, and cost constraints.
Deliverables
Concrete outputs, documented and handed over with the build.
- Role-appropriate views for different audiences
- Charts, tables, filters, and export where useful
- Alerts for thresholds or failures
- Documentation of data sources and refresh logic
Engagement model
Partnership patterns we document in the SOW or master agreement.
- -Start with a focused set of KPIs, then expand
- -Can follow a separate data project if sources need cleanup first
Commercial model
Effort follows metric definitions, source systems, refresh latency, and how many distinct audiences you serve. We quote after discovery.
We start with a focused discovery (paid or unpaid, depending on complexity). You receive a written scope or SOW: milestones, acceptance tests, and a defined change process. NDAs and your procurement steps are routine.
Fixed scope
Documented requirements, milestones, and acceptance criteria. Delivery targets an agreed release or go-live.
When it applies
A focused KPI set, trusted upstream data, and one or two consumer roles.
Phased programme
Successive increments with checkpoints, integrations, and change control as scope evolves.
When it applies
Many metrics, several sources, or prerequisite data modelling and warehouse work.
Ongoing partnership
Retained monthly capacity for maintenance, incremental features, releases, and operational support.
When it applies
Ongoing definition changes, new cuts by role, and pipeline reliability after first release.
Fees are quoted per engagement after discovery. Third-party cloud, licensing, and usage charges are usually billed to your accounts unless we agree otherwise.
Talk to our team