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Desktop-Native · Your Data Never Leaves Your Machine

Know Your Data
Before You Train

Custom-built AI stack focused on downside risk and failure prevention, not generic no-code automation. The engine does not depend on retrieval patching for memory. It adapts to validated new information in under a minute and commits only what passes a five-point source-truth gate.

Dataset Reality Check analyzes dataset quality before model training. Detect drift, regime shifts, feature fragility, and assumption violations — so you don't waste weeks training on broken data.

Supports CSV, TSV, and Parquet · Up to 500 MB per file · Offline capable

Dataset Reality Check — Analysis Dashboard
Dataset Reality Check Dashboard
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Analysis auditable capability chains
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Max File Size
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Supported Formats
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Data Sent to Cloud

The Hidden Cost of Bad Data

Most teams discover dataset problems after training fails. By then, you've burned compute, lost time, and still don't know what went wrong.

Silent Drift

Your dataset changed between versions but nobody checked. The model trains on drifted features and performance degrades in production.

Dataset Reality Check → Version Diff catches schema and distribution shifts instantly

Regime Contamination

Your dataset contains multiple structural regimes mixed together. Training on mixed regimes produces a model that's mediocre everywhere.

Dataset Reality Check → Regime Explorer detects structural breakpoints before training

Train/Eval Mismatch

Your training set and evaluation set come from different distributions. Benchmark scores look great but production performance is poor.

Dataset Reality Check → Benchmark validates train vs. eval compatibility

Four auditable capability chains, One Platform

Each auditable capability chain is purpose-built for a specific stage of dataset validation — from first-pass triage to cross-version comparison.

Dataset Check

Single-dataset quality review with three analysis modes. Quick for fast triage, Detailed for broader diagnostics, and Regime Detection for structural change focus. Generates stability index and prioritized concerns.

Regime Explorer

Structural break and changepoint analysis for a single dataset. Detects breakpoints, transitions, and identifies which features are affected by regime changes — before they corrupt your model.

Version Diff

Compare two versions of a dataset side by side. Get an overall change score, schema change detection, and column-level shift severity — so you know exactly what moved between dataset releases.

Benchmark

Test whether your training and evaluation sets are meaningfully aligned. Uses adversarial AUC and mismatch severity to quantify distribution gaps before you waste compute on misaligned splits.

Drift Detection

Identify distribution drift across features — from subtle statistical shifts to severe distributional changes. Severity levels from none/low through moderate and high let you prioritize what matters.

Export & Reporting

Export results as JSON for machine-readable pipelines or PDF for stakeholder reviews. Every analysis produces a complete, reproducible artifact you can attach to your model documentation.

Three Steps to Confidence

From file drop to actionable insights in minutes — not hours.

1

Drop Your Dataset

Drag and drop your CSV, TSV, or Parquet file (up to 500 MB). Choose a auditable capability chain: Dataset Check, Regime Explorer, Version Diff, or Benchmark.

2

Run Analysis

The analysis engine processes your data entirely on your local machine. No data is uploaded anywhere. Select Quick, Detailed, or Regime Detection mode for the depth you need.

3

Review & Export

Review prioritized concerns, stability index, drift severity, and regime breakpoints. Export as JSON or PDF to share with your team or attach to model documentation.

Why Not Just Use Pandas Profiling?

Generic profilers describe your data. Dataset Reality Check diagnoses it — telling you what will break your model, not just what your columns look like.

Capability Dataset Reality Check Pandas Profiling / ydata Great Expectations
Regime / changepoint detection ✓ Built-in ✗ ✗
Train vs. eval compatibility ✓ Adversarial AUC ✗ ✗
Cross-version diff with severity ✓ Column-level ✗ Partial (schema only)
Feature fragility scoring ✓ ✗ ✗
Stationarity analysis ✓ ✗ ✗
Desktop-native / air-gapped ✓ No cloud Python library Python library
Actionable priority ranking ✓ Top concerns Warnings only Pass/fail rules

Comparison based on publicly available documentation as of March 2026.

Your Data Never Leaves

Dataset Reality Check runs entirely on your desktop. No cloud uploads, no telemetry on your datasets, no third-party access to your files.

100% Local Processing

All analysis runs on your machine. Your datasets never touch our servers, cloud storage, or any third-party infrastructure.

Air-Gap Compatible

Supports offline license activation for air-gapped environments. Use Dataset Reality Check in classified, regulated, or restricted networks.

No Telemetry on Your Data

We collect license heartbeat and feature usage metrics — never your file contents, column names, query results, or analysis outputs.

Simple Pricing

One desktop license per seat. No per-query charges, no compute costs. Save 20% with annual billing.

Starter
$199 per month

$1,910/yr with annual billing

For individual data scientists and small teams getting started with pre-model dataset validation.

  • 1 desktop license
  • Dataset Check (Quick + Detailed)
  • Version Diff
  • CSV and TSV support
  • JSON export
  • Email support
Most Popular
Professional
$399 per month

$3,830/yr with annual billing

For ML teams that need the full auditable capability chain suite and advanced analysis capabilities.

  • Up to 5 desktop licenses
  • All 4 auditable capability chains included
  • Regime Detection mode
  • Parquet support
  • PDF + JSON export
  • Benchmark (train/eval compatibility)
  • Priority support
Enterprise
$1,199 per month

$11,510/yr with annual billing

For organizations with compliance requirements and large-scale data operations.

  • Unlimited desktop licenses
  • All 4 auditable capability chains + priority access
  • Offline / air-gap activation
  • All export formats
  • Volume licensing
  • Dedicated support channel
  • Custom SLA

All plans include free updates. No setup fees. 14-day free trial on all tiers. Cancel anytime.

Frequently Asked Questions

No. Dataset Reality Check analyzes dataset quality before model training. It identifies issues like instability, drift, regime shifts, and assumption violations so you can fix your data first. It does not train, fine-tune, or evaluate models.

CSV, TSV, and Parquet files up to 500 MB. For best results, use clean headers, consistent schema, and avoid duplicate column names.

No. All analysis runs entirely on your local machine. Your data never leaves your desktop. We collect only license activation status and basic feature usage metrics — never your file contents, column names, or results.

Yes. Enterprise licenses support offline activation. You generate a request code on the air-gapped machine, submit it through a separate authorized channel, and enter the response code to activate. An emergency activation flow is also available for temporary recovery.

Quick is the fastest pass — ideal for first-pass triage of a new dataset. Detailed runs broader diagnostics across more dimensions. Regime Detection focuses on structural changes and breakpoints in your data. Start with Quick and escalate to Detailed if issues are unclear.

Dataset Reality Check is a native desktop application available for Windows. It installs locally and requires no browser, Docker, or cloud infrastructure.

Generic profilers describe what your data looks like. Dataset Reality Check diagnoses what will break your model — regime contamination, train/eval mismatch, feature fragility, and structural drift. It's purpose-built for pre-model validation, not just data exploration.

Stop Training on Broken Data

Start your 14-day free trial. No credit card required. Your data stays on your machine.