Annotation and training data pipelines for production ML
Annotation & Training Data as a repeatable delivery practice — applied with tooling, governance, and metrics your team can sustain.
Cut the cost, not the quality
High-quality labels at scale — with guidelines, QA, and tooling your team can operate.
Annotation guideline design
Clear rubrics, edge-case examples, and inter-annotator agreement targets.
Labeling operations
Managed annotation workflows with QA sampling and escalation paths.
Active learning loops
Prioritize the examples that most improve model performance.
Dataset versioning
Reproducible snapshots tied to model training runs and audits.
Supply chain intelligence at 10,000+ farmer scale
Traceability, demand forecasting, and operational automation for India's largest farmer-owned food processing enterprise.
“We needed systems that could survive peak harvest volume — not another spreadsheet layer.”
Operations leadership, Sahyadri Farms
Annotation platforms — built for your domain
We configure or build labeling UIs suited to your data type — images, documents, audio, or structured fields — with reviewer workflows and export pipelines.
Key outcomes:
- Custom review stages for specialist vs generalist annotators
- Integration with training pipelines and model-assisted pre-labeling
- Export formats compatible with PyTorch, TensorFlow, and cloud ML services
Quality assurance that scales
Gold-standard items, consensus rules, and adjudication flows catch label drift before it reaches training.
Key outcomes:
- Inter-annotator agreement tracked per class and annotator
- Automated flagging of outlier submissions
- Audit reports for compliance and model governance
Farm-to-market operations that survive peak harvest
Traceability, cold chain monitoring, and export workflows engineered for high-volume perishable supply chains — from intake through dispatch.
Read story
Threat signal SOC teams can act on
ML-driven detection and compliance workflows that surface ranked incidents — signal over noise for security operations.
Read storyAnnotation & training data — common questions
How does Aqeeq embed this methodology into existing ML workflows?+
We map your current pipelines, define ownership and checkpoints, then implement tooling and runbooks so the practice continues after our engagement.
Can you work with our existing MLOps stack?+
Yes. We extend Kubeflow, MLflow, SageMaker, Vertex, or custom stacks — introducing new components only where they reduce operational risk.
What deliverables do we receive?+
Documented workflows, automated gates, dashboards, and trained internal owners — not slide decks.
Move forward with annotation & training data
Book a conversation — we will scope the engagement or tell you if another path is a better fit.