Specialized engagement
Make the hard data legible
When the dataset is genuinely complex — graphs, time series, networks, geospatial, real-time — off-the-shelf charts give up. Custom, performant visualization a real person can read and act on, designed and engineered as one thing.
From $40,000
Scoped per dataset and surface. Most ship in 4–8 weeks.
What you get
- Information design grounded in the actual data and the decision it serves
- Custom, performant rendering (Canvas / WebGL / SVG) that holds up at real scale
- Interaction designed for exploration — filter, drill, compare — not a static poster
- Accessible and responsive: legible on a laptop and on a boardroom screen
- Production code in your stack, owned by you
Why this, from me
Most data viz is either pretty and useless or accurate and unreadable. I design the encoding and write the render code, so performance and legibility are the same decision. Hand-tuned rendering keeps 100k points smooth where a charting library would choke.
- Design and render engineering in one senior
- Built for real data volume, not a sample
- The decision drives the encoding, not the library defaults
How it runs
01
Fit call
The data, the scale, and the decision it has to support.
02
Map
Data shape and the question the user is actually asking.
03
Prototype
A working render of the hardest view, moving with real data.
04
Build
Production component integrated into your stack.
Who it's for
A fit
- Data products where the chart IS the product
- Teams past the limits of a charting library
- Dashboards executives genuinely need to read
Not a fit
- A standard bar-and-line dashboard — use a library
- One-off report graphics
Start with a fit call
Tell me what you have in mind. If it's a fit, we book a call and I scope it. If it isn't, I'll point you somewhere better.
Questions
D3, Canvas, or WebGL — chosen by the shape and scale of the data, not habit.
Yes. Streaming and live updates are part of the render budget from the start.
I'll tell you honestly if a charting library is enough — custom is only worth it when the data demands it.