Enterprise AI Pipeline
End-to-end data annotation pipelines that plug into your MLOps stack — with API integration, automated QA, and continuous delivery of production-ready training data.
From Raw Data to Trained Model — Seamlessly
Enterprise AI teams waste weeks managing data handoffs between collection, annotation, validation, and training. Centric Labs builds end-to-end annotation pipelines that integrate directly with your data lake, annotation workflows, quality gates, and model training infrastructure — eliminating bottlenecks and delivering labeled data on a continuous basis.
- REST API for programmatic task submission and retrieval
- Webhook notifications for real-time pipeline triggers
- S3, GCS, and Azure Blob storage connectors
- Automated quality gates with configurable thresholds
- Compatible with MLflow, Weights & Biases, and DVC
Every Stage of Your Data Pipeline
Modular components that integrate with your existing MLOps infrastructure.
Data Ingestion
Automated data intake from cloud storage, databases, and streaming sources. We handle format conversion, deduplication, and pre-processing so raw data flows seamlessly into annotation queues without manual intervention.
Pre-Labeling
Model-assisted pre-annotation using your existing models or our pre-trained classifiers. Reduce human annotation time by 30-60% by bootstrapping labels that annotators refine and correct rather than create from scratch.
Quality Assurance
Multi-stage QA with consensus scoring, golden set benchmarking, and automated outlier detection. Configurable quality gates reject sub-threshold annotations before they enter your training pipeline.
Analytics Dashboard
Real-time visibility into annotation progress, quality metrics, throughput rates, and cost tracking. Exportable reports for stakeholder communication and SLA monitoring with customizable KPI dashboards.
API Integration
RESTful APIs for task creation, status polling, and result retrieval. Python SDK for programmatic pipeline orchestration. Native integrations with Airflow, Kubeflow, and custom DAG schedulers.
Continuous Delivery
Scheduled or event-driven delivery of labeled data batches to your model training infrastructure. Version-controlled datasets with diff tracking so you know exactly what changed between training runs.
Streamline Your AI Data Pipeline
Stop managing spreadsheets and file transfers. Build a production-grade annotation pipeline that scales with your AI program.