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.
Translation layer
A narrow interface shaped for that one domain. The AI doesn't need to learn your team's internal tools to be useful, this layer translates between your data and the AI.
Reasoning layer
A frontier AI model running on that interface, reading and writing within how your team already works. Not a separate tab. Not a chat window bolted to the side.
How it fits.
Designed around how your organization works.
It starts with how your team actually operates, not a generic template. The system is shaped to your workflows, your data, and the decisions your team makes every day.
Built on your actual data.
Modeled against the live systems your team relies on, not a sanitized export. As your data evolves, so does what the AI works from.
One team, end to end.
The people who learn your domain are the same ones building the AI that reasons over it. Context stays with the work.
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, this is the work: putting frontier intelligence inside that loop.
Start a conversationAdjacent work
Agent Infrastructure
Agent Internet Runtime
The substrate underneath the systems work. How autonomous agents inspect, delegate, and earn trust as they move across the open internet.
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.