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AI, Data & Emerging Technology · Data visualization

Charts and data tools people read correctly

We design and build data visualizations, from interactive explorers and embedded customer analytics to report graphics, choosing chart types, color and layout so people understand the numbers quickly and do not misread them.

  • Custom interactive charts
  • Embedded customer analytics
  • Data explorers
Start your projectHow we work

Overview

Design decisions that make data readable

Data visualization is the step where numbers reach people, and most misreadings happen here. The chart type, scale, color, ordering and labeling all change what a reader concludes. A well-designed view answers a specific question at a glance and lets the reader dig deeper only when they want to. Everything else belongs one click away, in a detail view or tooltip.

The core build decision is between a BI platform and custom code. Tools like Power BI or Tableau are quick to build and easy for analysts to maintain, but limited in branding, interaction and embedding. Custom charts built with D3 or similar libraries can match your product exactly and handle unusual views, at the cost of more engineering. Many organizations use both.

Good visualization is accurate, accessible to color-blind and keyboard users, fast on large data and consistent from one screen to the next. It also stays honest: axes that start at sensible values, consistent scales across related charts, and clear labels that say exactly what a number measures and over which period.

Who it’s for

Built for teams like yours

  • 01

    SaaS companies showing customer data

    Software products that need analytics screens or reports inside the app, styled to match the product and fast enough for every customer account. Each account sees only its own data.

  • 02

    Executive and board reporting

    Leadership teams whose monthly reports are dense tables or inconsistent slides, and who want a small set of clear, comparable views of the business. Every view uses the same definitions.

  • 03

    Researchers and public-facing groups

    Nonprofits, agencies and publishers who need to explain data to a general audience through interactive charts, maps or story-driven pages on the web. Pages work on phones and with screen readers.

Why it matters

Clarity is a design problem

The same numbers can inform or mislead depending on how they are drawn. Truncated axes, too many colors and cluttered dashboards cause bad decisions. Visualization is a design discipline: knowing which comparison matters, picking the chart that makes it obvious, and removing everything else so the point lands in seconds.

We pair that design thinking with engineering, so custom charts stay fast, accessible and correct when the data changes, the volume grows or new metrics and filters are added later on.

Every engagement includes

  • Audience reviewwe learn who reads the visuals, what they decide and how often they look.
  • Chart designsketches and prototypes that test which visual makes the key comparison clear.
  • Visual systemconsistent colors, typography and chart styles that match your brand.
  • Developmentresponsive, tested components connected to your live data sources.
  • Accessibility checkscontrast, keyboard access and screen-reader descriptions verified.
  • Handovercomponent library, data contracts and documentation for future charts.

Features

Visualization work we do

  1. 01

    Custom interactive charts

    Bespoke visuals built with D3, Recharts or ECharts when standard chart types cannot tell the story.

  2. 02

    Embedded customer analytics

    Usage and performance dashboards inside your product, secured so each client sees only their data.

  3. 03

    Data explorers

    Filter, drill-down and comparison tools that let non-analysts answer their own follow-up questions.

  4. 04

    Maps and geospatial views

    Choropleths, heatmaps and route maps built with Mapbox or Leaflet for location-based data.

  5. 05

    Accessible color and labeling

    Colorblind-safe palettes, direct labels and text alternatives so charts work for every reader.

  6. 06

    Large dataset performance

    Aggregation, canvas or WebGL rendering that keeps charts responsive with millions of points.

In practice

Visual tools we design and build

  • In-app analytics module

    Your customers get a reporting area inside your product with filters, date ranges, comparisons and exports, built as reusable components that respect each account’s data boundaries. Usage of the module can be tracked to guide future features.

  • Leadership metrics view

    A focused set of key measures with trends, targets and short annotations explaining changes, designed to be read in minutes and printed or exported for board packets. Definitions are shown alongside each measure.

  • Interactive map explorer

    Location data such as service areas, sales territories or asset positions is shown on a map with layers, filters and summaries for any region a user selects. Large point datasets are clustered to stay readable.

  • Explanatory data story

    A scrolling web page walks readers through a dataset step by step, combining charts, annotations and text to explain a finding to a non-technical audience. It works on phones and can be embedded in your site.

Process

How we work

  1. 1

    Reader research

    We interview the people who will use the views, learn which questions they ask and what they do next, and collect the reports they rely on today. Unused reports are noted for retirement.

  2. 2

    Chart sketching

    Several chart options for each question are sketched with real data and tested with readers, looking for misreadings and the fastest route to the answer. Options that cause repeated misreadings are dropped early on.

  3. 3

    Design system

    Color palettes, type, spacing, number formats and chart rules are set once and documented, so every new view looks and behaves like the rest. Dark mode and print versions are covered as part of the same system.

  4. 4

    Component engineering

    Charts are built as reusable components or BI templates, connected to your data with defined contracts and tuned to render quickly on large result sets. Loading and empty states are designed, not left as defaults.

  5. 5

    Accessibility review

    Every view is checked for color contrast, color-blind safety, keyboard navigation and screen reader descriptions, with data table alternatives where charts alone are not enough. Issues are fixed before handover, not logged for later.

Deliverables

What you receive

  • Reader questions and requirements summary
  • Tested chart sketches and prototypes
  • Documented chart design system
  • Reusable chart components or BI templates
  • Data contracts for each view
  • Accessibility review notes and fixes
  • Developer and analyst usage guide

Tools & methods

Libraries

  • D3.js
  • Observable Plot
  • Apache ECharts
  • Recharts
  • deck.gl
  • Mapbox GL

BI platforms

  • Power BI
  • Tableau
  • Looker
  • Metabase
  • Apache Superset

Design methods

  • Figma
  • user interviews
  • first-click testing
  • WCAG review

FAQ

Frequently asked questions

Anything else about Data visualization? Ask us directly.

  1. BI tools like Power BI, Tableau and Looker are faster for internal reporting with standard charts. Custom builds make sense for customer-facing analytics, unusual chart types, branded experiences or when per-user licensing gets expensive. We often combine them, and we will recommend the simpler route when it fits.

Let’s work together

Have a project in mind?

Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.

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