Custom Data Visualization & Analytics Dashboards

We build executive KPI dashboards, operational monitoring tools, embedded analytics within SaaS products, public data journalism pieces, financial reporting interfaces, geospatial maps, and real-time event streams. If you have data and need humans to understand it, we can build the visualisation layer.

Our primary library is D3.js for bespoke, highly custom charts that no off-the-shelf solution handles well. For standard charts in React we use Recharts, Apache ECharts, and Visx depending on bundle size and customisation requirements. We choose the library based on your specific chart types, interaction complexity, and performance constraints.

Yes. We connect to Snowflake, BigQuery, Redshift, Databricks, ClickHouse, PostgreSQL, and MySQL. We use dbt for transformations, keeping business logic version-controlled and testable. If you do not yet have a data warehouse, we can help you set one up as part of the project.

We address performance at multiple layers: materialised views and pre-aggregated rollup tables in the database, query result caching with Redis, pagination and virtual scrolling in the frontend, and web workers for client-side computation. The goal is dashboards that feel instant even on datasets with billions of rows.

Yes. We implement live-updating dashboards using WebSockets for sub-second streaming data, or configurable polling intervals (5s, 30s, 5m) for less time-sensitive metrics. Users can toggle between live and historical views without leaving the dashboard.

Yes. Our designers create a complete data visualisation style guide: colour scales for categorical and sequential data, typography, grid and axis conventions, tooltip design, and accessibility contrast ratios. Charts are branded to match your product identity rather than looking like default library output.

Yes. Embedded analytics — charts and dashboards hosted within your SaaS application and scoped to the logged-in user's data — is a common pattern we implement. We handle row-level security so every user sees only their own data, and we design the charts to match your product's design system.

We follow WCAG 2.1 AA guidelines for data visualisation: colour palettes designed for colour-blind users (using pattern fills as a secondary encoding), keyboard-navigable chart elements, ARIA labels on SVG elements, and data table alternatives for screen readers. Accessibility is tested with axe-core in our CI pipeline.

Yes. We build choropleth maps, point cluster maps, route visualisations, and heat maps using Mapbox GL JS, Leaflet, and Deck.gl. We handle GeoJSON processing, tile server configuration, and performance optimisation for rendering millions of geographic data points smoothly in the browser.

A focused dashboard with an existing clean data source takes 4–8 weeks. A full analytics platform with a data pipeline, custom chart library, and embedded multi-tenant analytics takes 3–6 months. The largest time variable is data quality — messy source data that needs significant cleaning and modelling adds 4–8 weeks.

A single-page dashboard connected to an existing clean data source starts at $12,000–$25,000. A multi-section analytics platform with custom charts, real-time data, and embedded analytics typically ranges from $40,000–$120,000. Ongoing data pipeline maintenance and chart additions are available on a monthly retainer.

Yes. Every project includes a handoff session covering the chart component API, how to add new data sources or chart types, the dbt transformation layer, and the deployment workflow. We also deliver written documentation and a video walkthrough so new team members can onboard independently.