Carraggon CarraFggrr — the Union platform
Cloud and robotics, finally one platform.
CarraFggrr joins accelerated compute, site-accurate simulation, embodied agents and mixed-vendor fleet control into a single governed substrate — so intelligence trained in the cloud can move real machines safely on your floor.
Union
Cloud + Robotics
Sim to real
One model artefact
Fleet scale
1 to 10,000 machines
Control
Policy on every action
Most robotics programmes stall in the gap between the model and the machine. CarraFggrr closes that gap by making the cloud, the simulator and the fleet the same system with the same rules.
The platform
Six layers. One union.
Each layer is useful on its own and considerably more useful together — a policy trained on CarraFggrr Cloud is simulated, certified, shipped and audited without ever leaving the platform.
01
CarraFggrr Cloud
Accelerated compute built for embodied workloads, not just chat tokens.
On-demand and reserved GPU capacity with high-throughput storage, fast interconnect and job orchestration tuned for perception training, reinforcement learning and large-scale simulation rollouts.
- —On-demand & reserved GPU clusters
- —Distributed training orchestration
- —Dataset lake for sensor & video
- —Region pinning and residency control
02
CarraFggrr Robotics
Fleet control for arms, AMRs, drones, humanoids and fixed cells.
Register a machine once and get teleoperation, mission dispatch, health telemetry, OTA policy updates and safe-stop guarantees across mixed-vendor hardware — with the same API for every form factor.
- —Mixed-vendor fleet registry
- —Mission planning & dispatch
- —Teleop with latency budgets
- —OTA policy and firmware rollout
03
CarraFggrr Sim
Millions of hours of practice before a single real motion.
Photoreal and physics-accurate environments generated from your own sites, so policies are trained against your geometry, lighting, clutter and edge cases — then scored on a fixed evaluation suite before release.
- —Digital twins from site scans
- —Domain randomisation at scale
- —Deterministic replay of failures
- —Release gating on eval suites
04
CarraFggrr Embodied Agents
Language-driven task planning wired to real actuators.
Vision-language-action models turn an instruction into a grounded plan, decompose it into skills the fleet already has, and execute under a supervisor that refuses anything outside the approved envelope.
- —VLA task planning
- —Reusable skill library
- —Human-in-the-loop approval
- —Grounded refusal & safe stop
05
The Union Layer
Why it is called a union — one identity, one policy, one ledger.
Cloud jobs, simulated runs and physical machines share the same identity model, quota system and audit ledger. A policy written once applies to a training job, a sim rollout and a robot on the floor.
- —Single identity & RBAC
- —Unified quota and spend caps
- —One audit ledger end to end
- —Signed model provenance
06
CarraFggrr Edge
The site keeps running when the network does not.
Inference runtime that lives on-prem next to the machines, syncing models and telemetry opportunistically. Degraded connectivity downgrades autonomy gracefully instead of stopping the line.
- —On-prem inference runtime
- —Store-and-forward telemetry
- —Graceful autonomy degradation
- —Air-gapped deployment option
How we keep autonomy boring.
Safety is a gate, not a setting
Every policy release passes a fixed evaluation suite and a signed sign-off before it can reach a machine that moves.
Hardware agnostic
Vendors change. The registry, skill library and control API do not, so a new arm or AMR is an integration, not a rebuild.
Simulation is the default
Nothing ships to steel until it has survived your own twin, including the failures you have already had once.
Governed by default
Residency, role-scoped access, spend caps and a complete audit of every job, rollout and physical action.
Where it runs
Built for operators with real floors.
Manufacturing
Pick, place, inspect and kit with cells that retrain on their own defect history.
Warehouse & logistics
AMR routing, dock scheduling and mixed-SKU handling under live throughput targets.
Energy & infrastructure
Autonomous inspection rounds, thermal anomaly detection and confined-space work.
Labs & healthcare
Sample handling and supply logistics with chain-of-custody kept on the ledger.
How it works
From provisioned GPUs to certified motion.
01
Provision
Spin up GPU capacity and connect your data lake, PLCs, cameras and existing fleet controllers.
02
Twin & train
Generate a twin of the site, train perception and control policies against it, and score them on your eval suite.
03
Certify
Gate the release: safety envelope, refusal tests, human sign-off and signed provenance on the artefact.
04
Operate
Roll out over the air, run missions with teleop fallback, and feed every real run back into the twin.
Compare
CarraFggrr vs. stitching it together yourself.
| Capability | CarraFggrr | Assembled stack |
|---|---|---|
| GPU cloud for training | Included, tuned for sensor workloads | Separate vendor |
| Site-accurate simulation | Twins built from your scans | Generic sample scenes |
| Mixed-vendor fleet control | One registry and API | Per-vendor consoles |
| Safety release gating | Mandatory eval suite + sign-off | Manual, ad hoc |
| Audit across cloud and steel | Single ledger | Split logs |
| Offline operation | Edge runtime, air-gap option | Cloud dependent |
Bring us one cell, one line or one site.
We twin it, train against it and certify the first policy — then you decide how far the union goes.