Deployable systems
Intelligence built into how you already work.
Most AI tools expect your organization to adapt to them. Arcede builds complete systems shaped around how your team already operates, using the data you already have
The layers that make up each system.
Domain model
A faithful representation of how your organization actually works. Geometry, dependencies, value, and constraints, structured so an AI can reason over them without flattening the nuance.
Live integration
Connected to the data your team is working with day to day. Not a sanitized export, not a snapshot, not a stand-in. The model evolves with the systems it represents.
Protocol surface
An MCP shaped for that one domain. A narrow, well-defined interface between the model and the AI, so the AI doesn't need to learn your team's internal tools to be useful.
Reasoning layer
A frontier AI model running through the protocol, reading and writing within how your team already works. Not a separate tab. Not a chat window bolted to the side.
What deployable means here.
Designed around how your organization works.
Every system starts with how your team actually operates, not a generic template. Arcede builds each layer from the ground up, shaped to your workflows, your data, and the decisions your team makes every day.
Built on your actual data.
Modeled against your live systems of record, not a sanitized export. The model evolves as your underlying data does, so what the reasoner sees is what is actually true about your data today.
One studio, start to finish.
Whatever Arcede builds comes from the same team. The people who understand your domain are the same ones building the reasoning. No patchwork of contractors to manage.
Two systems, the same shape.
Each one shaped to a different operator and a different domain. The pattern repeats; the work inside it does not.
Volumetric, heterogeneous data
Optimization across volumetric models
A system that takes existing spatial datasets, builds a model of the domain, and lets a frontier AI reason over geometry, value, and constraints at scale. Sits inside the workflow the team already trusts.
Scenario passes that used to take a specialist a long turnaround can now happen in minutes.
Scheduled work, dependency graph
Reasoning across scheduled work and its constraints
A system that builds a faithful model of work, dependencies, and resource constraints from a team's planning data, then opens that model to a frontier AI for diff, critique, and revision, working alongside how schedules are already written.
Conflicts and second-order effects can be surfaced in the same pass the change is authored, instead of caught downstream.
If you operate something with real complexity in the data and real consequence in the decisions, the studio's work is putting frontier intelligence inside that loop.
Start a conversationAdjacent work from the studio
Agent Infrastructure
Agent Internet Runtime
The protocol thinking underneath the systems work. How autonomous reasoners interact with the live web: delegation, inspection, trust at the protocol layer.
Community Intelligence
Impact Data Bridge
The same craft applied to public-good intelligence. Translating dense scientific and community knowledge into something field operators can actually use.