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DATA LABELING FOR PHYSICAL AI · LOGISTICS

Human-grade labels for logistics video.

Wearable and fixed-camera video of picking, packing and palletising in live warehouses. Labelled frame by frame by an in-house annotation team, backed by computer-vision and data engineers, working your guidelines in your formats.

SCOPE A PILOT BOOK A 30-MIN CALL
150+
engineers behind the team
2 passes
of review on every clip
Overnight
turnaround for US teams
EGO-CAM · FACTORY 0412 · WORKER 0087F 018,442 · 30 FPS
TORQUE WRENCH · TOOLR-HAND · 21 KPTS · CONTACTPASS 2 / 2 · REVIEWED
GOLD-SET CHECK ✓
ACTION SEGMENTS00:10:14 → 00:10:22“Right hand grasps torque wrench and tightens the second bolt on the bracket.”
TRUSTED BY TEAMS AT
TuringBlackRockDellAirtel AfricaReliasClaritev+ AI research labs
8label types on one clip
2human review passes
6 hrsoverlap with East Asia
150+engineers behind the team

THE NEW BOTTLENECK

Collecting video is solved. Labelling it isn’t.

Physical-AI teams now capture human work at a scale no one had two years ago. The constraint has moved downstream, to people who can turn those hours into labels.

PROBLEMNo annotators

→ Unlabelled hours pile up

WITH BRANDSMASHERSDedicated annotation team

→ Every release labelled on schedule

PROBLEMA rotating crowd

→ Labels drift, models learn the drift

WITH BRANDSMASHERSOne trained, scored team

→ Consistent labels, clip after clip

PROBLEMSlow hiring

→ Missed dataset releases

WITH BRANDSMASHERSPods live in 48–72 hours

→ Scale up for each drop, down after

WHAT WE LABEL

Every signal a robot learns from a human hand.

Eight label types, one team. Stack several on the same clip and get one set of guidelines and one delivery.

  • Hand pose & keypoints

    21-point hand skeletons per frame, left and right, with occlusion flags.

  • 3D pose review

    Check 3D hand and body pose outputs; flag frames that break your error thresholds.

  • Action segmentation

    Start and end of every step: reach, grasp, tighten, release, inspect.

  • Hand–object contact

    Which hand touches which object, and when contact begins and ends.

  • Tools & objects

    Boxes, masks and tracks for tools, parts and workpieces across the clip.

  • Language narration

    Step-by-step captions written for vision-language-action training.

  • Clip curation

    Score clips on hand visibility and manipulation density; drop the rest.

  • Privacy redaction

    Blur faces, badges and screens before data leaves the factory.

HOW IT WORKS

People where judgement matters, models where it doesn’t.

SCOPE A PILOT
  1. WITH YOU01

    Calibrate

    Guidelines, edge cases and a gold set, agreed with you.

  2. MODEL02

    Pre-label

    Model-assisted first pass, so people correct rather than draw.

  3. HUMAN03

    Label

    Trained annotators work in your tool or ours.

  4. HUMAN04

    Review

    A second reviewer checks every clip; a lead settles disagreements.

  5. AUTOMATED05

    Score

    Each batch is scored against the gold set before it ships.

  6. PIPELINE06

    Deliver

    WebDataset sidecars, COCO, JSON or your own schema.

THE FULL HUMAN-DATA STACK

Labellers, trainers, engineers. One contract.

48–72 hrs
to a live pod
150+
engineers to draw on
1
contract for it all
ANNOTATION TEAMS

Dedicated labellers

Trained on your guidelines and scored weekly. The same people on your data, release after release.

  • Hand & pose
  • Video segments
  • Boxes & masks
  • Redaction
AI TRAINERS & EVALUATORS

Human feedback for models

Reviewers who rank, critique and grade model outputs against your rubric.

  • Preference ranking
  • Rubric evaluation
  • Video Q&A writing
  • Error analysis
DATA & ML ENGINEERS

The pipeline around the labels

Engineers who ingest your shards, run pre-label models and ship labels in your schema.

  • Data pipelines
  • Pre-label models
  • CV engineering
  • Format & QA tooling
OPS & COMPLIANCE

Handled end to end

Contracts, NDAs, payroll, access control and offboarding, so you manage output, not people.

  • NDA & IP
  • Contracts
  • Payroll
  • Access & offboarding

QUALITY YOU CAN AUDIT

You set the bar. We show you we cleared it.

Acceptance thresholds are yours. Every batch is reported against them: gold-set accuracy, reviewer agreement and per-annotator scorecards, so quality is a number you can check.

  • Gold-set accuracy
  • Reviewer agreement
  • Annotator scorecards
  • Weekly report
Weekly quality reportILLUSTRATIVE
GOLD-SET ACCURACY BY TASK
Hand keypoints
Action segments
Contact states
Tools & objects
Black tick = your acceptance threshold
REVIEWER AGREEMENT · 8 WEEKSTrending up as edge cases are settled
ANNOTATOR SCORECARDS
A1ON TRACK
A2ON TRACK
A3COACH

WHY BRANDSMASHERS

A labelling vendor that’s also an engineering team.

  • One team, two skill sets

    Annotators and the engineers who build your pipelines and pre-label models sit together. No hand-offs between vendors.

  • Security from day one

    NDAs, role-based access, least-privilege credentials and clean offboarding. Footage never sits on personal devices.

  • Scale with every release

    Grow the team for a dataset drop and shrink it after: hourly, monthly or full-time.

150+ ENGINEERSREPLACEMENT GUARANTEEBHOPAL · INDORE
ONE DAY, SHOWN IN INDIA TIMEStandard working hours
US West CoastPDT · UTC−7WORKDAY
Brandsmashers · IndiaIST · UTC+5:30WORKDAY
East AsiaUTC+8WORKDAY

6 hoursof live overlap with East Asia every working day

Overnightbatches sent at US close are labelled by the next morning

BY INDUSTRY

One playbook, tuned to every floor.

Worked examples of how we’d run a labelling programme in five industries: the footage, the labels and the rules that matter. Illustrations of our approach, not past client results.

ENGINEERING TRACK RECORD

Data, AI and QA work for Dell, Relias, Claritev, Saagas.AI and Airtel Africa.

Read the case studies
ILLUSTRATIVE ENGAGEMENT · WAREHOUSING & FULFILMENT

Logistics

Pick, pack and place footage for warehouse robotics

THE FOOTAGEWearable and fixed-camera video of picking, packing and palletising in live warehouses.

INDUSTRY SIGNAL76%of logistics operations face notable workforce shortagesSource: Descartes, 2024
LABEL FOCUSGrasp typeshow each item is held, by category
WHAT WE'D LABEL
  • Hand–object contact and grasp type
  • Item and container boxes
  • Pick, pack and place segments
HOW WE'D RUN ITClip curation up front, so labelling hours go to frames with real manipulation in them.

START SMALL

Scope a pilot in under a minute.

Pick the tasks and a sample size. We label it to your guidelines and hand your team a quality report, so you judge us on real output before any long-term commitment.

  1. WEEK 1Calibrate

    Guidelines, edge cases and a gold set, agreed together.

  2. WEEK 2Label & review

    Production on your sample, two passes per clip.

  3. WEEK 3Report

    Accuracy against gold, agreement, throughput and a plan to scale.

1 · WHAT SHOULD WE LABEL?
2 · PILOT SAMPLE (HOURS OF VIDEO)
YOUR PILOT TEAM50 hours of video · hand pose & keypoints, action segmentation and hand–object contact
  • Trained annotators
  • Second-pass reviewers
  • QA lead
  • CV engineer · model-assisted pre-labels
  • Data engineer · your formats

We reply within one working day with a pilot plan and NDA · info@brandsmashers.com