Standard BI charts stop working when data gets large, spatial, or live. We design and build custom visualizations, from interactive charts and maps to IoT views and 3D digital twins, that stay readable and fast on millions of data points.
The data is there, but the chart hides the point. We pick the right chart for each question and strip out the noise.
Network graphs, custom maps, and unusual chart types don’t fit Power BI or Tableau. We build them in code.
Browsers choke when millions of points render as SVG. We switch to canvas or WebGL and load detail only where users zoom in.
Sensor and transaction streams need views that update without a reload. We build streaming charts that keep up.
Off-the-shelf widgets look out of place inside your app. We build chart components in your own design system.
Color-only encoding and tiny labels shut people out. We design for color blindness, keyboard use, and screen readers.
Good visualization starts before any code. Our designers decide which chart answers which question, what users see first, and which numbers deserve attention. Then they turn those rules into a chart design system your team can reuse, built on the same research methods as our product design work.
When a BI tool can’t draw it, we build it with D3.js, ECharts, or Vega-Lite inside React. Filters, zoom, brushing, and linked views behave the way your users expect.
Rendering a million points takes real engineering. We use canvas and WebGL, server-side aggregation, and progressive loading, and we push live updates over WebSockets. The feeds behind these views run on streaming data pipelines we build and monitor.
Assets, deliveries, and sensors make more sense on a map. We build interactive maps with Mapbox, deck.gl, or Leaflet, with heatmaps, clusters, and route layers.
Sites and equipment are easier to monitor when you can see them. We build 3D views and digital twins in Three.js and WebGL and connect them to live telemetry from your IoT devices. Operators see which unit is failing and where it sits.
Your customers see these charts inside your app, so they should feel native. We build chart components in your design system. If you need a whole analytics module, we deliver it through custom dashboard development.
Board decks, investor updates, and public reports land better with a clear narrative. We build scrollable, interactive reports that walk readers through the numbers step by step.
Have charts that confuse people or load slowly? We review them for clarity, speed, and accessibility and give you a prioritized list of fixes. If you want, we implement them too.
We talk to the people who’ll use the visuals and list the decisions they make and the questions they ask.
We review sources, volumes, and refresh rates, because they decide which rendering approach will work.
Designers sketch the views and set the chart rules: colors, type, states, and accessibility.
Users click through a working prototype with your data, and we fix whatever confuses them.
Engineers build the components, connect live data, and test speed at your real volumes.
You get the code and documentation, and we add new views as your needs grow.
Clinical trends, patient flow, and procurement data shown on de-identified records with role-based access. The visual layer sits on the same controls we use in healthcare analytics.
Grid load, generation, and consumption on maps and live charts, fed by smart meter and sensor streams. It pairs well with the energy monitoring software operators already rely on.
Device fleets, equipment health, and production lines in 3D views and live charts built from telemetry. Operators spot a failing unit before it stops the line.
Market moves, portfolio risk, and transaction patterns in dense, interactive charts that stay fast on large time series.
Routes, deliveries, and harvest data on live maps. For Wish Farms, we built real-time visibility into harvest data across 50,000+ scans a day.
Native chart components inside your product, matched to your design system and filtered per customer. Teams building a new product can add them as part of SaaS development.
Visualization designers, frontend developers, and data engineers work together. The chart you approve is the chart that ships.
We plan rendering and aggregation before the first chart. Views keep up as your data grows.
We’re ISO 27001:2022 certified, GDPR covered, and HIPAA-ready. That matters when your charts show patient or financial data.
AI-assisted development cuts our delivery time by up to 50% and bugs by up to 20%. Senior engineers review every release.
Within 14 days, you get a scoped plan and cost estimate for your visualization project.
Our employee retention is 96%. The team that builds your charts is likely the one that’ll extend them.
Most projects start when the numbers are right but people still misread or ignore the charts. A visualization project produces:
As a self-check, write down the three decisions people make from your current charts. A chart that serves none of those decisions is a candidate for redesign or removal. If you can’t name the decisions yet, the first workshop maps them with you, and you don’t need to prepare anything.
Each discipline solves a different problem:
To place your problem, compare the same metric in two reports. A mismatch points to BI. If the numbers match but people still struggle with the charts, the work is visualization. Many projects touch two of the three, and the discovery call shows where to start.
You can estimate the main cost drivers yourself:
List your views, note the row count behind the heaviest one, and note how often the data changes. Those three facts cover most of the estimate. If you don’t have row counts, a sample export works too. An audit or a design sprint costs far less than a digital twin. Within 14 days of the first call, you get a scoped estimate. To start, send us a screenshot and a data sample.
An audit or a design sprint takes a few weeks. Custom interactive charts and maps take longer, and 3D views and digital twins take the longest. Every project begins with a 14-day roadmap. Views then ship in phases, and the most-used chart reaches users first. Relevant delivers 99% of projects on time.
To find your fastest first release, pick the one chart people complain about most. That chart is usually the best first deliverable. If no single chart stands out, the roadmap ranks them by usage for you.
Built-in BI visuals cost less and suit standard charts that your users already open in the BI tool. Custom visualization pays off in four cases:
As a self-check, list the visuals your team has requested and the tool couldn’t produce. If the list is empty, staying in the BI tool is the cheaper choice, and that’s the recommendation you’ll get. If you’re not sure what the tool can do, a short review settles the question.
Browsers slow down when a chart draws tens of thousands of SVG elements. Three changes solve most cases:
To check your chart, multiply the rows it shows by the number of series. A result in the tens of thousands or more points to rendering as the bottleneck. If the count is hard to get, a performance audit measures it for you.
Accessible charts are readable by everyone, including people with color vision deficiency and screen reader users. The European Accessibility Act has applied to many digital services in the EU since June 2025. Run these checks today:
If you’re not sure how your charts score, an accessibility audit covers all three and checks them against WCAG.
Inherited charts rarely need a full rebuild. The takeover starts with an audit of:
The audit produces a prioritized list of fixes, and you decide which ones get implemented. A quick self-check: ask two people what the main chart on your most-used screen shows. Different answers mean the chart needs a redesign. If you can’t run that check yet, the audit includes short interviews with your users. When messy data turns out to be the real cause, the fix moves to data engineering first.
Live IoT views and 3D models need two parts working together: telemetry that arrives on time and a renderer that updates without reloading. Sensor values drive status colors on a 3D model in Three.js or WebGL, and each alert points to the exact unit.
To gauge the scope, count your device types and how often each one reports. Those two numbers set the data volume. If you don’t have telemetry yet, the project starts with device connectivity. For a Canadian R&D company, Relevant built a cloud platform that monitors robotic devices, collects logs and errors, and runs remote reboots and installs.
Send us a screenshot and a sample of the data. We’ll suggest a clearer way to show it and estimate the build.
Design
Design
Healthcare
