Quaternion Studios — Applied AI & Interactive Engineering

We build the systems
most people never
notice are possible.

Applied AI, embodied autonomy, and real-time 3D — researched, engineered, and shipped to production for teams working on genuinely hard problems.

Shipped case studies
7
Practice areas
4
Where we're strongest
0→1
Based in Texas
TX

Ethos

We are the few
in the middle.

A quaternion rotates through a dimension you can't see — the axis between the real and the imaginary. That is where the interesting engineering lives: not in the model and not in the product, but in the narrow space between them where the two have to actually agree.

It is a small space, and not many people work in it. Research groups build things that are true but not usable. Product teams build things that are usable but not true. We sit in the middle and make the two meet, which is slower to explain and considerably faster to ship.

How we work

What we do

From applied AI to real-time 3D, shipped to production

Six things we are genuinely good at. If your problem is not one of them, we will tell you that in the first conversation rather than the third invoice.

01

Applied AI & LLMs

Retrieval, semantic search, and multi-agent systems paired with real domain solvers — models that reason against ground truth instead of around it.

02

Embodied AI

Vision-language-action policies, imitation learning, and the inference plumbing that gets a 3B-parameter policy running inside a 20 ms control loop.

03

Real-time 3D & WebGL

Three.js, WebGPU, GLSL, WASM — configurators, BIM tools, and visualizations that render thousands of elements without dropping a frame.

04

Data Visualization

Large, messy datasets turned into fast, interactive, GPU-accelerated views that stay responsive at scale rather than at demo size.

05

Systems & Infrastructure

Production backends under load — transaction engines, knowledge graphs, distributed processing, cloud automation, and the observability around them.

06

Engineering & CAD

Constraint-aware CAD editing, BIM/Revit pipelines, and automation that turns hours of manual drafting into minutes of review.

Practice areas

Where the depth is

Four bodies of work we keep returning to, and the specific problems inside each one that we have had to solve more than once.

01

Embodied AI & robot autonomy

Policies that move real hardware. We work across the whole stack — from what the robot sees to what the actuators do 20 milliseconds later.

  • Vision-language-action policies: tokenized-action and flow-matching heads
  • Imitation learning, action chunking, temporal ensembling
  • Teleoperation rigs and the data flywheel that feeds them
  • Sim-to-real: domain randomization, system identification, residual policies
  • On-robot inference: quantization, compilation, latency budgeting
  • Runtime safety envelopes and the monitors that enforce them
02

Agent systems & multi-agent workflows

Most agent failures are not model failures. They are context, contract, and evaluation failures. We design for those first.

  • Orchestration topologies: supervisor, pipeline, blackboard, market
  • Tool contracts — schemas, idempotency, error surfaces, retries
  • Context engineering: compaction, memory hierarchies, budget accounting
  • Retrieval that survives real corpora — hybrid search and reranking
  • Trajectory-level evaluation harnesses with statistical power
  • Human handoff points, audit trails, and cost ceilings
03

Real-time graphics & simulation

Interfaces that hold tens of thousands of elements and still feel immediate. The frame budget is the specification.

  • WebGL / WebGPU renderers, custom GLSL and WGSL
  • Signed-distance fields, raymarching, procedural geometry
  • Instanced and GPU-driven scene graphs, one context per page
  • deck.gl and large geospatial / factory-scale scenes
  • BIM, CAD, and scaffolding viewers with constraint feedback
  • Profiling: draw-call budgets, overdraw, memory ceilings
04

Data & systems infrastructure

The unglamorous half that decides whether any of the above survives its first real week in production.

  • Event-sourced transaction engines and ledger correctness
  • Knowledge graphs and semantic layers over messy source data
  • Distributed pipelines, batch and streaming, backfills that finish
  • Vector and hybrid stores sized for the actual corpus
  • Cloud automation, IaC, reproducible builds
  • Observability: traces, evals, and alerting that names the cause

How we work

Four steps, in this order, every time

Engagements differ in subject matter far more than they differ in shape.

01

Read the actual literature

Before a line of code, we find out what is already known. Most 'novel' problems have three papers and a reference implementation behind them, and the fastest path is knowing which one is load-bearing.

02

Build the smallest thing that proves it

A narrow, honest prototype against your real data — not a demo dataset. It either shows the approach works or it kills it in days instead of quarters. Both outcomes are worth paying for.

03

Harden it into a system

Evaluation harnesses, failure modes, latency budgets, cost ceilings, observability. This is where a promising notebook becomes something you can put in front of users.

04

Hand it over properly

Documented, instrumented, and legible to your engineers. We would rather your team owns it in six months than that you keep paying us to be the only ones who understand it.

Elsewhere on this site

There is a map under all of this.

The Field is the same studio, laid out as a surveyed plane instead of a page. You load standing in an ink-grass hero, rise out of it, and land on a map: one origin and six destinations, each exactly the same distance out, with a real-time fractal lab and the case studies orbiting in between.

Everything out there is rendered live. Nothing out there is decoration.

Enter the Field

Let's build

Have a hard problem?

Tell us what you're solving — applied AI, embodied autonomy, real-time 3D, or the systems underneath. We read every message, and we'll tell you honestly if it isn't ours.