CM1 supercell storm simulation
Data infrastructure for the physical world
Give agents and your products a cloud backend for simulation, scan, and sensor data.
Agents that answer engineering questions
An agent cannot open a transient run or a lidar scan. On CoDim an agent inspects, probes, and compares the full data in the cloud, then posts the answer to your team's scene.
How agents connect →Where does wall shear peak in Run 42, and how does it compare with Run 41?
I'll look at both runs in the Mirror study project.
codim dataset_describe(Run 42)
8.2M cells, wall_shear up to 42.9 Pa on the front tires
codim filter_apply(wall_shear, region = mirrors)
Run 42 mirror peak 14.3 Pa, Run 41 16.5 Pa
codim scene_actor_set(Run 42)
Colored by wall shear, 0 to 8 Pa
codim view_camera_set
Camera on the left mirror
codim annotation_create(mirror peak)
Pinned at 14.3 Pa for the team
On the body, wall shear peaks on the outer face of the mirror housing at 14.3 Pa, 13% below Run 41. The scene is colored by it and the peak is annotated.
A cloud backend for 3D data
CoDim stores simulation, scan, and sensor data at full resolution, runs filters and analysis next to the data on cloud GPUs, and streams results to agents, browsers, Python, and your product.
Produced by
CFD and FEA solvers, AI surrogates, lidar and photogrammetry scans, and robot fleets.
Carried by CoDim
Streamed, filtered, probed, and rendered on cloud GPUs and CPUs, next to the data.
Used by
Agents over MCP, engineers in a browser, ML pipelines in Python, and the users of your product.
Build CoDim into your product
The viewer, comparison, timeline, comments, and probes are built. The streaming, compute, and analysis behind them come with the SDK. Your users never leave your app.
For developers →import { createClient } from '@codimensional/client'import { CoDimView } from '@codimensional/react'// Your backend issues the session. Users sign in to your app.const client = createClient({ embed: { acquire: acquireSession } })const workspace = await client.viewer.workspaces.open({})await workspace.addDataset(run.datasetId)export function Results() {return <CoDimView client={workspace.allocation.primaryView} />}One core for simulation, physics AI, geoscience, and robotics
Carotid artery blood flow, with particles advected through a 3D vector field.
Simulation and CAE
CFD, crash, and structural results post-processed and reviewed at full resolution by everyone on the program.
Physics AI
Surrogate predictions next to solver ground truth, at the scale of the training data.
Geoscience
Ensembles, drillholes, and uncertainty volumes for geologists, engineers, and partners.
Robotics and digital twins
Scans, toolpaths, and sensor data streamed into one scene, inside the tools operators already use.
Live solver streams
A running solver sends each step over Apache Arrow Flight. It connects outbound, so it works from behind a firewall, and every run can be saved as a dataset.
Filter pipelines
Clip, slice, threshold, contour, streamlines, gradients, vorticity, and Q-criterion run next to the data, chained into pipelines that update when an input changes.
Storage built for time steps
Mesh structure is stored once per run and each time step as its own cloud object, so scrubbing reads only the step on screen.
Works with PyVista out of the box
Install codim and any PyVista dataset goes to a shared scene in one line, from the scripts and
notebooks you already run.
import codimimport pyvista as pvmesh = solver.result() # any PyVista datasetmesh.codim.upload("Run 42", project="Wing study")mesh.codim.show()baseline = pv.DataSet.codim.load("Run 41")- Send solver output to CoDim from any Python script
- Pull CoDim datasets back into Python for analysis
Nothing to deploy
CoDim runs the GPUs, render servers, and job queues and scales them with your users and jobs. Your team installs a Python package or the SDK.
Jobs and pipelines
Chain filters, inference, and post-processing into jobs. Each step runs on the hardware it needs, and results stream back into the scene.
One backend, every interface
Start a job from Python, review it in the app, embed it with the SDK, or hand it to an agent. Each one works on the same compute and the same results.
Point an agent at the dataset that breaks your stack.
Send us the run or scan your team cannot open, and we will connect your agent to it.
