Build, analyze, and repair training data and evaluation benchmarks
- Models are only as good as their training data and evaluation benchmarks.
- Dataset defects are a major cause of costly reworks, unexpected model behaviour, and compliance obstacles.
- Data Lab brings data science and AI agents together to build, assess, and repair training datasets and evaluation benchmarks.
Score a dataset you already have. Ends in a report, not a changed dataset.
Debug the data before using it
In a dataset of millions of examples, even a small percentage of defective examples can teach the model wrong behaviours. Recent research: Goodfire’s Anatomy of Post-Training showed that industry-standard post-training datasets implicitly taught models to erode their own safety guardrails, fabricate authoritative-looking links, and flatter users. These behaviors were traced to specific data clusters that are difficult to identify without the right tools.
Dataset defects are orders of magnitude cheaper to catch in the data than to discover in a trained model. Calibrion Data Lab exposes and repairs many of these problems early on, before they find their way into model training.
Inside the lab
The lab combines statistical tests, probability distributions, panels of LLM judges, and ML-based evaluators to examine a dataset from various angles — methods grounded in published research on anomaly detection and distributional analysis in NLP datasets.
Detecting & fixing bias
Detect various types of biases using ML and statistical models. Repair (when possible) or filter those biases to ensure an unbiased data.
Distributional analysis
Measure entropy, imbalance, coverage, and divergence from the target distribution to identify and fix overrepresented regions and missing data.
Anomaly detection
Statistical, probabilistic, and ML models surface unusual examples and clusters and flag or remove them from data to increase training accuracy.
Trained data evaluators
Task-specific models score safety, coherence, cadence, and other properties at example and corpus level.
Agent juries
Independent agents review the same data. Inter-agent agreement supports stable findings; disagreement is used as uncertainty signal and can trigger expert review.
Integrity and leakage
Detect duplicates, corruption, schema violations, train–test leakage, contamination, and PII before deeper analysis begins.
Synthesis
Describe the data you need and expert agents find and recommend public datasets that fit, calibrate an existing dataset to your specification, or build a new one from scratch or from a handful of seed examples.
Self-improving data loop
The loop runs behind the lab’s operations: it trains or probes a model on the resulting dataset, evaluates it, traces weak results back to the data, and reworks the dataset until the quality threshold is met.
Every pass is expert-verified and logged — click to pause.
Main dataset operations
Measure Dataset Quality — score a dataset you already have. Integrity scans, distribution analysis, evaluators, and panels of LLM judges produce a quality report and catalogue rather than a changed dataset.
Repair Dataset Defects — diagnose issues such as bias, duplication, and leakage, then repair what is recoverable and filter out what is not.
Curate & Build — turn raw documents, logs, and transcripts into a structured, annotated, training-ready dataset, or build one from a task description, specification, or seed examples. Experts and agents create the pairs and labels your task needs.
Calibrate & Adapt — reshape an existing or public dataset for a target domain, task, or use case by pruning redundancy, expanding thin areas, and adjusting balance and difficulty. The same operation shrinks an oversized, noisy corpus to the smaller, high-signal subset that trains an equally good model for a fraction of the compute.
Dataset Provenance & Auditability
We provide an extensive report documenting provenance, lineage, diagnostics, evaluator scores, and expert-review decisions. Filtering, repair, and synthesis operations are logged as transformations, creating an auditable record for reproducibility, governance, and reporting.