Data Infrastructure Platform

Datalier

Unified data infrastructure platform embedding data layers to fine tune data to be AI-ready, rationalizing model training and enhancing model accuracy

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Azure AWS Google Cloud PostgreSQL Any Open Source
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Datalier
Integrate

Upload large multimodal data in minutes.

Connect Amazon S3, Google Cloud Storage, Azure Blob Storage, Hugging Face Hub, PostgreSQL, Snowflake or from any other open sources directly. Datalier validates structure, normalises fields, and registers every dataset with schema metadata and source provenance before getting into any layer.

See Cloud Integration →
Layer 1 of 5

Five layers, in sequence.

Raw data becomes training-ready data through five sequential layers — each one adding structure, accuracy, and traceability before the next begins. Scroll to step through them.

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Clean. Validate. Prepare.
Transform

Remove duplicates, detect personally identifiable information (PII), validate field integrity, normalise LLM schema, score data quality and many more, before any labeling begins.

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Annotate. Verify. Certify.
Label

A multi-engine orchestration layer that routes each data point to the appropriate AI Engine. Labels are backed by confidences and those below the threesold go to annotation reviews before pushing it into next layer. Enriched with automation and focused on quality across four data modalities.

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Snapshot. Trace. Export.
Version

Every processed AI-ready dataset is frozen into an immutable, hash-verified snapshot, with a full lineage for traceability and get AI-ready datasets exported in relevant form.

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Monitor. Detect. Correct.
Observe

Get Models registered, to pull prediction logs from deployed models back into the platform. Detect drifts to identify when and where performance is degrading, and route rectified dataset back through labeling pipeline back into the model training enhancing accuracy.

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Control. Audit. Comply.
Govern

Role-based permissions, an immutable audit log, and regulatory tags that travel with the data wherever it moves. Ensuring trust in data binding the focus on reliability and sustainability of AI models.

Explore Govern →
Dataset inventory, data volume, and pipeline worker health cards on the Datalier dashboard Dataset inventory, data volume, and pipeline worker health cards on the Datalier dashboard
AI engine routing panel assigning Vision and Segmentation models to a data modality AI engine routing panel assigning Vision and Segmentation models to a data modality
Pipeline recipe builder showing a fixed execution order — Validate, Deduplicate, PII Detection, Speech-to-Text Cleanup Pipeline recipe builder showing a fixed execution order — Validate, Deduplicate, PII Detection, Speech-to-Text Cleanup
Model evaluation metrics — accuracy, error distribution by type, and confidence calibration Model evaluation metrics — accuracy, error distribution by type, and confidence calibration
Historical PSI drift trend and inference volume, with a per-record drift history log Historical PSI drift trend and inference volume, with a per-record drift history log
Dataset version history with correction rounds and before/after accuracy per round Dataset version history with correction rounds and before/after accuracy per round
Governance panel flagging PII scan and version-freeze issues, with a downloadable audit trail Governance panel flagging PII scan and version-freeze issues, with a downloadable audit trail

Unified 5-Layer Multi-modal Fabric

Dataset inventory, data volume, and pipeline worker health cards on the Datalier dashboard Dataset inventory, data volume, and pipeline worker health cards on the Datalier dashboard
AI engine routing panel assigning Vision and Segmentation models to a data modality AI engine routing panel assigning Vision and Segmentation models to a data modality
Pipeline recipe builder showing a fixed execution order — Validate, Deduplicate, PII Detection, Speech-to-Text Cleanup Pipeline recipe builder showing a fixed execution order — Validate, Deduplicate, PII Detection, Speech-to-Text Cleanup

Automated Closed-Loop Remediation

Model evaluation metrics — accuracy, error distribution by type, and confidence calibration Model evaluation metrics — accuracy, error distribution by type, and confidence calibration
Historical PSI drift trend and inference volume, with a per-record drift history log Historical PSI drift trend and inference volume, with a per-record drift history log

Cryptographic Lineage & Governance

Dataset version history with correction rounds and before/after accuracy per round Dataset version history with correction rounds and before/after accuracy per round
Governance panel flagging PII scan and version-freeze issues, with a downloadable audit trail Governance panel flagging PII scan and version-freeze issues, with a downloadable audit trail
Automated Closed-Loop Remediation

Loop that streamlines training and improves model accuracy.

We do not stop after exporting AI-ready data straight into your required storage entity. We connect to your training infrastructure, to ensure every train run on AI models are streamlined and evaluate the model for any form of drifts. Observability layer measures prediction logs in production for any kind of drift. Data behind any degradation routes straight back to Datalier for re-labeling loop without manual intervention dimensing every drift to ensure complete model accuracy.

See how the loop works →
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Datalier
SageMaker Vertex AI Azure ML HuggingFace LangSmith Webhook REST API
Train → Predict → Detect drift → Re-label
Automated Pipelines

Automate your data pipeline, with Datalier.

Integrate the right data layers to automate data workflows enabling accurate data preparation, data labeling, routing by reasoning, evaluation and more. Orchestrate bulk data operations without increasing headcounts, while maintaing progress visiblity across teams. Saving 1000s of hours on manual data tasks.

New Pipeline
Transform Steps — pick any of 9
Validate Deduplicate PII Detection Schema Normalize Semantic Cluster Confidence Route OCR Extract Speech-to-Text Cross-Modal Align
Label Engine
Task TypeClassification ▾
Modelgpt-4o-mini ▾
Confidence0.85
▶ Run continuously

Why Datalier ?

Datalier's Data layers and AI Engines are emborded to endorse the trust from ML and AI teams to fine-tune their data to be training ready while accelerating the development of their AI models.

Quality

Datalier can provide the core to any datasets making it AI ready with high level quality metric fom AI assitance and Human review system.

Cost Effective

Build lucrative AI Models training data, while being able to easily find, categorize, and fix AI model failures with our Data layers and AI engines. Then, optimize operational spend for creating high-value curated data for AI model training until attaining required level of model accuracies.

Scalability

Datalier can support any ML project from lower-volume experiments to high-volume AI-ready data production projects, being flexible according to the engineering needs.

Diversity

Datalier is capable of working with the greatest variety and diversity of data to help deliver the greatest value to your model training performance through delivery of AI-ready data.

Get data infrastructure for training AI models

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