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Scalar Prime

Enterprise multimodal and
physical AI data pipelines

High-throughput egocentric, conversational & spatial data for AI models.

Bypass synthetic data gaps and public dataset noise. We source, anonymize, and annotate high-fidelity video, audio, and spatial streams directly into your ML pipeline.

Request a sample batchExplore data schema & API
Pipeline statusAll layers active
  • 01Collection layer
  • 02Quality layer
  • 03Delivery layer

A sourcing and annotation layer built for teams that cannot afford noisy inputs.

Modalities and outputs

  • Egocentric video
  • Conversational audio
  • Spatial scans
  • LiDAR point clouds
  • Depth maps
  • Speaker diarization
  • Temporal tags
  • Bounding boxes
  • PII blurring
  • Parquet and JSON manifests
  • S3 and GCP export

(01)The input layer

We replace synthetic gaps and scraped noise with real-world data your model can learn from. Every asset is rights-cleared, consent-verified, anonymized, and annotated before it reaches your bucket.

  • IP-cleared

    100%

    IP-cleared sourcing and commercial use.

  • Contributors

    Vetted

    Verified contributors with annotation background.

  • Privacy

    PII-safe

    Frame-by-frame face and plate blurring.

  • Delivery

    S3 / GCP

    Direct pipeline and bucket export.

(02)Services

The data layer your model actually needs.

Source, access, and structure high-value inputs without stitching together disconnected vendors.

  • 01

    Custom data sourcing

    Tell us your spec. We deploy vetted contributors to capture edge-case egocentric and spatial video.

    • Built to spec
    • Egocentric video
    • Spatial capture
    • Edge cases
  • 02

    Pre-built datasets

    Instant access to IP-cleared, consensual multi-speaker and physical interaction feeds.

    • IP-cleared
    • Consent-verified
    • Multi-speaker
    • Physical interaction
  • 03

    Data annotation & labeling

    Frame-level temporal tagging, bounding boxes, and active speaker diarization exported directly to S3.

    • Temporal tags
    • Bounding boxes
    • Diarization
    • S3 export

(03)Pipeline architecture

From raw signal to training-ready asset.

One accountable layer for sourcing, quality, privacy, annotation, and delivery.

  1. 01Collection layer

    Edge video capture

    Real video from vetted contributors, collected across egocentric, conversational, and spatial environments.

  2. 02Quality layer

    QA & anonymization engine

    Quality checks, PII blurring, active speaker diarization, temporal tags, and frame-level annotation before delivery.

  3. 03Delivery layer

    Your ML model / S3 bucket

    Structured Parquet and JSON manifests sync directly to your model stack, S3, GCP, or downstream API.

(04)Built for the hard parts

Data for systems that operate beyond the benchmark.

Targeted collection and annotation for high-value edge cases across physical and multimodal AI. Hover a card to see the raw frame behind the dots.

Request a sample batch
Three industrial crates standing on a concrete warehouse floor
01

AMR / Manipulation

Autonomous mobile robots

First-person egocentric motion, object gripping, and industrial floor navigation.

  • Egocentric motion
  • Object gripping
  • Floor navigation
A woman looking straight into the camera against a plain wall
02

Audiovisual speech

Conversational and human-facing AI

Direct-to-camera speech, facial expression streams, diarization, and localized dialogue.

  • Direct-to-camera
  • Expressions
  • Diarization
A shopper with a cart between fully stocked store shelves
03

Retail CV

Retail and inventory vision

Shelf scans, price tag recognition, and multi-angle indoor spatial telemetry.

  • Shelf scans
  • Price tags
  • Indoor telemetry
A person wearing a mixed reality headset in a bright room
04

3D / Depth

Spatial computing and LiDAR

Point clouds and depth map pairs for spatial understanding and AR models.

  • Point clouds
  • Depth maps
  • AR
Car tail lights blurred by rain on a wet street at night
05

Robustness

Edge-case mobility feeds

Variable lighting, outdoor transitions, and unscripted obstacle feeds.

  • Low light
  • Outdoor transitions
  • Obstacles
A studio microphone on a white background
06

VLA / Foundation

Multimodal model training

Synchronized video, audio, and transcript datasets for pre-training and fine-tuning.

  • Video
  • Audio
  • Transcripts

(05)Show, do not tell

Every asset arrives with context.

Our delivery layer turns raw media into structured, queryable training inputs with synchronized metadata and verification states.

  • Frame-level temporal tags
  • Synchronized transcripts and speaker IDs
  • PII verification and sensor quality scores
Visual tracking2 tracked subjects
Two hands holding green forks next to a grater and a yellow pepper
Frame 01842 / 0298000:12.480
Tracking active
Status
Verified
Sync
Locked
Score
0.984
First-person motion capture
spatial_004821_manifest.json
{"asset_id": "spatial_004821","modality": "egocentric_video","duration_s": 42.8,"pii_status": "verified_clear","imu_stability": 0.984,"annotation": "action_temporal_v2"}
Format
Parquet + JSON
Sync
S3 / GCP / API

Sample deliveries. Hover the frame to see the raw footage.

(06)Ready for a better input layer?

Build with data you can trust.

Tell us about your project and data requirements. Our team will reach out to schedule a consultation and build a sourcing plan tailored to your model.

Request a pipelineJoin the contributor network
  • Rights-cleared, consent-verified data
  • Secure, compliant data handling
  • Global multilingual sourcing network
  • Rapid turnaround, dedicated support
  • hello@scalarprime.xyz
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