Data Augmentation

Targeted data expansion to improve model performance, coverage, and robustness without manual labeling.
We create synthetic examples tailored to your domain, edge cases, and performance gaps, grounded in real usage, not generic templates.
We surface underrepresented patterns and failure cases, ensuring your model sees more of what it needs to succeed.
Every augmented example is tied to specific evaluation goals, so you don’t just get more data, you get better insight.

Our Process

We begin by identifying where your current data falls short: missing segments, underperforming tasks, or ambiguous edge cases.
Using structured heuristics, generative models, or domain-informed simulations, we generate augmentation data that fills those specific gaps.

Our process avoids noisy bulk generation and instead focuses on precision: each new example is grounded in evaluation insights and real-world scenarios.

Sample Timeline

  • Week 1: Data audit and evaluation linkage
  • Week 2: Prototype augmented dataset + stakeholder review
  • Week 3–4: Full dataset generation and quality validation
  • Week 5+: Optional support for retraining, fine-tuning, or prompt design
  • Sample Deliverable

  • Augmented Dataset Package including:
    • Synthetic samples with source rationale and intended test use
    • Coverage maps showing improved representation by segment or task
    • Evaluation alignment matrix linking data to performance goals
  • Optional: Integration with training pipelines or validation tools
  • Get started with
    Data Augmentation
    Use-Case-Driven Synthetic Data Generation
    Weak Signal Amplification and Scenario Coverage
    Evaluation-Linked Data Design
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