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+990 123 456 789 info@dataxworks.com

The Data Infrastructure Behind AI That Ships.

Built for Teams That Build AI Products

Foundation Model Teams

Pre-training corpora, instruction tuning datasets, RLHF pipelines, and large-scale human evaluation workflows

Vertical AI Builders

Domain-specific datasets, fine-tuning pipelines, and expert validation across healthcare, finance, retail, and more

Multimodal AI Teams

Cross-modal data annotation and evaluation across image, video, audio, text, and sensor datasets

Data Operations Is the Bottleneck Most AI Teams Don't Plan For

Building a competitive AI product requires more training data, higher-quality evaluation, and more rigorous production monitoring than most teams anticipate at the start. The data infrastructure problem compounds as models scale and the cost of poor-quality training data, inconsistent human evaluation, or unmonitored production drift is measured in model performance, not just engineering time.

    70% of AI development effort is spent on data sourcing, cleaning,

    30% of model variance is tied directly to training data quality, not architecture, compute, or fine-tuning strategy

    8 - 12% - of generative AI outputs in production require correction or escalation without structured human evaluation in the loop

    Where DXW Fits in AI Development

    DXW operates across the full AI lifecycle, from training data creation to production validation, enabling teams to build, evaluate, and scale AI systems with structured, high-quality data infrastructure.

    Training dataset engineering
    Benchmark & evaluation design
    Scalable annotation workflows
    Bias & drift detection
    Human evaluation pipelines
    Continuous validation
    01 STEP

    Training Data & Dataset Creation

    Schema-aligned, bias-aware datasets engineered for supervised, fine-tuning, and multimodal AI training across domains.

    02 STEP

    Data Annotation at Scale

    AI-assisted annotation workflows with QA layers, IAA benchmarking, and direct integration into MLOps pipelines.

    03 STEP

    Human Evaluation & Preference Data

    Domain expert evaluation generating preference datasets, ranking signals, and RLHF-ready outputs for model alignment.

    04 STEP

    Model Evaluation & Production Validation

    Evaluation frameworks, drift detection, and continuous human validation ensuring reliable performance in production environments.

    Frequently asked questions

    DXW supports annotation across all major modalities including images, video, text, audio, time series, 3D point clouds, LiDAR, and sensor data. We also handle cross-modal and multimodal datasets that combine multiple data types within a single training program.

    DXW implements multi-level quality assurance including inter-annotator agreement (IAA) benchmarking, structured review hierarchies, randomized audit sampling, and continuous calibration cycles. All quality controls are documented and auditable.

    Yes. DXW annotated datasets are structured for direct ingestion into modern MLOps platforms including MLflow, Amazon SageMaker, Azure ML, Google Vertex AI, and custom Kubernetes environments. We support dataset versioning, metadata tracking, and feedback loop integration.

    Where appropriate, DXW integrates model-assisted pre-labeling to accelerate throughput in high-volume programs. This is combined with confidence thresholds and active learning loops to prioritize human review where model uncertainty is highest, ensuring precision is never sacrificed for speed.

    All annotation is executed within secure, access-controlled environments aligned with enterprise data governance standards including HIPAA, GLBA, FCRA, and relevant state privacy laws. DXW maintains clear data lineage, ethical sourcing frameworks, and audit-ready documentation.
    START YOUR AI JOURNEY

    Build Production AI That Performs Where It Counts.

    Tell us your use case. We’ll design the right data strategy for it.

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