ANNOTATION ACROSS EVERY MODALITY

High-fidelity training data across every modality,governed in the EEA

Image, video, text, audio, LiDAR, and sensor-fusion annotation: 100% human QA, measured by the metrics your ML team already uses, documented to EU AI Act Article 10.

Scope your annotation pilot Read the data residency brief
Image, video, text, audio, LiDAR, sensor fusion
100% human QA on every label
EEA jurisdiction (Norway AS)
EU AI Act Article 10 documentation
  • EU AI Act Article 10
  • GDPR-native
  • EEA jurisdiction (Norway AS)
  • A workforce you can name

ANNOTATION OR COLLECTION

Already have the data? Annotate it. Missing the data? We collect it.

Annotation labels and enriches data you already hold. Collection sources data that does not yet exist. The distinction drives scoping, vendor choice, and Article 10 representativeness.

Raw asset

Labeled asset

class
vehicle
attributes
occluded, truncated
provenance
per-item logged

You have the data

We label it to production accuracy across modality, with provenance per item. This is the annotation hub.

You are missing the data

We scope and collect the missing segments, languages, or edge cases, then feed them into the same annotation pipeline.

Scope a data collection

Annotation logs reveal the collection gaps, so the two route into each other.

START FROM YOUR PROBLEM

We route you to the modality and the metric that proves it

Perception for autonomous systems leans on image, video, LiDAR, and fusion. Conversational AI leans on text and audio. Start from your problem; each route opens its service page.

Working in a modality not listed here? We scope custom annotation protocols across data types. Scope a project

BUILT FOR THE HARD CASES

The failure modes your data will hit, handled by design

Cheap labeling collapses on the hard 20 percent: occlusion, low light, crowds, mixed scripts. These are the failure modes our annotation guidelines and QA are built to hold.

  • Occlusion

    Pedestrians behind parked vehicles, boxes held through partial visibility

  • Low light

    Night street scene, masks segmented under glare and shadow

  • Crowd density

    Dense pedestrian crossing, instance IDs kept distinct

  • Mixed scripts

    Multilingual signage, entity spans across scripts

Schematic previews. Production work runs against your raw data under your engagement DPA.

Image annotation schema v3.2 client data masked
Label panel and QA-reject flow, client data masked

ONE PLATFORM, EVERY MODALITY

One platform, every modality, every label versioned and traceable

One unified platform across all six modalities, not loosely-glued tools. The human stays accountable; the model assists.

  • Versioned label schemas Every schema and guideline version preserved for audit and reproducibility.
  • Configurable QA routing Sampling and multi-pass review routed by risk, with adjudication on disagreement.
  • Audit trails and access control SSO, RBAC, and per-item audit logs for every label and review action.
  • EEA residency and segregation EEA data residency, per-project data segregation, no reuse of your data for internal models.

QUALITY YOU CAN AUDIT

Quality you can audit: kappa-gated annotators, gold-set benchmarking, error taxonomy

Inter-annotator agreement is measured with Cohen's and Fleiss' kappa, interpreted on the Landis-Koch bands. Annotators are kappa-gated on a gold set before production, sampling routes QA by risk, and every error is classified by type and severity.

Inter-annotator agreement (Cohen's / Fleiss' kappa) 0.88 kappa, almost-perfect band
Slight (0.00 to 0.20) Fair (0.21 to 0.40) Moderate (0.41 to 0.60) Substantial (0.61 to 0.80) Almost-perfect (0.81 to 1.00) Gold-set gate (0.70) Onboarding Gold-set gate Calibration Production Refined

Illustrative of method. The Landis-Koch bands are the standard interpretation; the curve shows the kappa-gated onboarding arc, not a published per-project figure.

Kappa-gated onboarding: annotators clear a gold-set threshold before they touch production data.

Error taxonomy

Every defect is classified by type (boundary, class, attribute, miss) and severity (critical, major, minor) and fed back into guideline refinement.

Error type Critical Major Minor
Boundary rare low tracked
Class rare low tracked
Attribute rare low tracked
Miss rare low tracked

Confusion matrix

Per-class agreement with diagonal dominance shows where classes are confused and where the guideline needs tightening.

100% human QA on every label, with a 90% usable rate target, not a self-serve marketplace burden pushed onto your team.

ARTICLE 10, SATISFIED AT THE LABEL

EU AI Act Article 10, satisfied at the label. GDPR-native by design.

Article 10 obligations cascade to the annotation partner. Each clause maps to a concrete deliverable you can hand your conformity assessor.

Regulator Control Evidence Status
Article 10 representativeness Ontology and sampling design Demographic and segment distribution report Mapped
Article 10 error-freeness Kappa-gated QA and gold sets Per-class agreement and defect report Mapped
Article 10 bias examination Label-level bias audit Bias-examination notes per dataset Mapped
Article 10 provenance Per-item provenance logging Dataset datasheet and provenance log Mapped
GDPR Articles 9 and 25 Lawful basis, minimization, data protection by design Signed DPA, 30-day erasure SLA, sub-processor list Mapped

DPA always included. EEA processing reduces transfer risk. Compliance is evidenced through EEA jurisdiction, named regulations, and audit-ready documentation, not third-party certification badges.

SCALE ON PROOF, NOT PROMISES

Start with a measured pilot. Scale on proof, not promises.

Engagement is a staged process with a named exit gate. You see the quality before you commit to scale.

  1. 01

    Scope

    Objectives, modalities, taxonomies, quality targets, risk level, and DPA defined with your team.

  2. 02

    Pilot

    A measured pilot with iterative guideline refinement and transparent reporting against the agreed metrics.

  3. 03

    Production

    Scale only after the exit gate: metric targets met and a stable, repeatable process, with SLAs on throughput and defect ceilings.

  4. 04

    Monitor

    Continuous QA monitoring, relabeling campaigns, and an annotation-to-model feedback loop.

The exit gate is named before scale: metric targets plus a stable process, not a single number.

Scope your annotation pilot

SCOPE YOUR PILOT

We scope the modalities, taxonomies, and quality targets your deployment needs. If YPAI is not the right fit, we will say so directly. A managed European partner complements your in-house team and absorbs the QA, PM, and compliance overhead a marketplace pushes back onto you.

Scope your annotation pilot