// AI Data Services

Training data pipelines built for production models

We build and operate the annotation, curation, and evaluation pipelines that keep machine learning teams shipping — dedicated pods, documented quality gates, and audit trails on every batch.

Scope a pilot batch See client results

AI neural network processing annotated training data

How it runs

A pipeline you can audit, end to end

Ingest

Secure intake, schema mapping, and gold-standard set creation.

Annotate

Trained pods label to your guidelines across every modality.

Review

Two-stage QA with inter-annotator agreement scoring.

Deliver

Versioned batches with audit trails and quality reports.

Capabilities

Six services, one governed delivery layer

Data Annotation

Image, video, text, audio, and 3D point cloud labelling at volume.

multimodal

Data Curation

Sourcing, deduplication, and dataset balancing for clean corpora.

datasets

Model Training

Fine-tuning support, benchmarking, and structured error analysis.

fine-tune

Human in the Loop

Live review queues and escalation workflows for production systems.

hitl

LLM Services

RLHF, preference ranking, red teaming, and rubric-based evaluation.

rlhf

AI Analytics

Quality dashboards and model performance reporting per batch.

reporting

98.2%
Average annotation accuracy: 4.2M
Frames labelled per cycle: 300+
Specialists on delivery: 24/7
Coverage across regions

Quality is a contract, not a promise

Every engagement runs against acceptance criteria we agree before a single item is labelled — so quality is measurable from the first batch.

1Dedicated QA layer with inter-annotator agreement scoring on every batch

2ISO-aligned data handling inside NDA-backed secure delivery facilities

3Documented quality gates and acceptance criteria agreed before ramp-up

4Named delivery manager accountable for SLA performance and reporting