warehouse & logistics robotics

Fleet-scale data, without fleet-scale chaos.

Logistics robots generate more data per week than most labs see in a year. Agentuor turns that volume into coverage you can measure, labels you can trust, and capacity you can scale ahead of peak season.

The challenge

Warehouse programs face a volume problem and a variety problem at once. Millions of picks look alike, yet the failures cluster around rare SKUs, damaged packaging, unusual shelf configurations, and lighting changes at shift boundaries. Annotating everything is wasteful; annotating a random sample misses exactly the cases that matter. Teams also need throughput to spike before peak season without quality dropping.

how agentuor helps

Built for this data

SKU and scene coverage

Agents cluster picks by item, packaging, shelf configuration, and lighting so you annotate the rare cases, not the redundant ones.

LiDAR and camera fusion

Point-cloud and image labels for pallets, totes, people, and forklifts on a shared frame.

Exception mining

Failed picks, mis-grasps, and near-collisions surfaced automatically for review and evaluation.

Managed capacity

Burst annotation and review throughput to our managed team while keeping your quality SLAs.

Evaluation by facility

Policy performance sliced by site, aisle type, SKU class, and shift so rollouts are gated on real conditions.

Governance across sites

Role-based access per facility and vendor, with full audit trails for safety reviews.

Data and labels

  • Pick, place, and navigation episodes from fleet logs with outcome telemetry
  • 2D boxes and masks for items, totes, and packaging states
  • 3D cuboids and point labels for pallets, racks, people, and vehicles
  • SKU, facility, aisle, and shift metadata
  • Exception flags from the robot's own controller

Typical workflow

1

Stream fleet data

Continuous ingestion with automatic episode extraction and exception tagging.

2

Prioritize

Agents select the picks that add coverage; redundant footage is deprioritized.

3

Annotate at scale

Your team, our managed team, or both — one guideline, one quality standard.

4

Gate rollouts

Evaluation by facility and SKU class decides when a policy version ships.

what changes

Outcomes teams work toward

Lessbudget spent labeling redundant picks
Site-levelevaluation before each rollout
Elasticannotation capacity for peak periods

Common questions

How quickly can managed capacity scale?

Programs are typically staffed within weeks; peak-season plans are agreed in advance.

Can data stay in our region?

Yes. Region selection is standard; hybrid deployments keep raw data in your storage.

Do you handle people in the footage?

Face and body blurring can be applied at ingest and is logged like any other operation.

Show us an episode. We'll show you the gaps.

Start with a walkthrough on your own data.