EDGEFOLD
EDGE AI, OPERATIONAL

Fold frontier
intelligence into
every device.

EdgeFold turns multimodal models into reliable device agents—optimized for your hardware, orchestrated across edge and cloud, governed as one fleet.

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Target runtimes ARM64CUDAx86WEBGPU
EDGEFOLD / FLEETFoundry North
OPERATIONAL
MODELvision-agent v3.8
EXECUTIONEdge-first
ROLLOUTCanary · 12%
Deployment simulation12 / 100 devices
ILLUSTRATIVE PRODUCT INTERFACE
01 / OPTIMIZE02 / ORCHESTRATE03 / OPERATE

The physical world has constraints.

Your agent shouldn’t notice them.

Cloud-native intelligence breaks when bandwidth drops, latency spikes, or sensitive data cannot leave the device. EdgeFold continuously adapts models and execution to the reality of each deployment target—without splitting your team across disconnected toolchains.

One edge intelligence plane

From frontier model
to field-ready fleet.

Three tightly connected systems turn experimental models into controllable, resilient device agents.

01

FOLD ENGINE

Fit intelligence to the hardware—not the other way around.

Profile the device, workload, and operating envelope. EdgeFold evaluates precision, memory, thermal, and latency tradeoffs, then builds a hardware-aware execution package with traceable decisions.

  • Target-aware model selection and compression
  • Multimodal pipeline partitioning
  • On-device evaluation before release
FRONTIER
EDGE
01 profile target 02 search execution graph 03 validate output parity PACKAGE READY
02

HYBRID ORCHESTRATOR

Keep decisions local. Escalate only what belongs in the cloud.

Define exactly which perception, reasoning, and action steps run on device. EdgeFold routes higher-compute work when a connection exists, then preserves safe behavior when it doesn’t.

  • Policy-based edge and cloud routing
  • Offline queues and graceful degradation
  • Local-first privacy boundaries
LIVE EXECUTION GRAPH 42 ms loop
01PERCEIVEdevice
02REASONdevice NPU
03ACTlocal policy
↗ESCALATEcloud, when needed
03

FLEETGUARD

Operate one intelligent device—or every device.

Observe model health, hardware conditions, drift, and software versions in one control plane. Stage changes by cohort, require approvals, and roll back without a field visit.

  • Fleet cohorts and staged rollouts
  • Model, data, and device health signals
  • Signed releases with instant rollback
RELEASE / V3.8CANARY ACTIVE
COHORTSTATEVERSION
Inspection lineUpdatingv3.8
Pick cellsHealthyv3.7
Mobile unitsQueuedv3.7
Approval gateOperator review required

The EdgeFold loop

Continuously adapt intelligence
to the real world.

Deployment isn’t a handoff. EdgeFold creates a governed loop from hardware profile to live fleet evidence.

  1. 01

    Profile

    Capture compute, memory, power, sensors, network, and response requirements.

  2. 02

    Fold

    Search the model and execution space for the best-fit deployment package.

  3. 03

    Stage

    Validate on target hardware, sign the release, and deploy to a controlled cohort.

  4. 04

    Adapt

    Feed drift, performance, and device conditions back into the next safe release.

Built for intelligence in motion

One platform. Very different edges.

ROBOTICS01

Robots that perceive and act without waiting on a round trip.

Partition vision, reasoning, and control loops around hard latency and connectivity boundaries.

INDUSTRIAL02

Inspection agents that keep working through network loss.

Run anomaly detection locally, escalate uncertain events, and manage model versions across sites.

SMART SPACES03

Multimodal experiences with privacy designed into the runtime.

Keep sensitive streams on premise while sending only policy-approved events to cloud workflows.

MOBILITY04

Intelligence that adapts across moving, heterogeneous fleets.

Coordinate staged releases across device generations and recover cleanly from interrupted updates.

Human authority, by design

Autonomous where it should be. Controlled where it must be.

Define what each agent can observe, decide, and execute. Every release and remote action passes through explicit policy, identity, and approval boundaries.

Request an architecture review
A
Workload identityUnique device and agent identity
VERIFY
B
Signed artifactsTraceable model and runtime releases
ATTEST
C
Execution policyBounded actions and data movement
ENFORCE
D
Human gatesApproval for high-impact changes
CONTROL
POLICY CHAININTACT

Bring your hardware map

We’ll map the fastest path
from model to machine.

Schedule a technical working session with the EdgeFold team. We’ll review your target devices, agent workload, operating constraints, and rollout plan.

Schedule a technical demo