Data is the new oil.
But most of it is crude.

80% of AI failures come from bad biased data. Models trained on unreliable datasets hallucinate, confabulate, and fail in production.

We score data reliability with uncertainty metrics before training starts by running relational models and time series coverage analysis parallels. Know what's trustworthy. Train models that actually work.

Why Your AI Keeps Making Things Up

You scrape data from the internet. Mix formats. Ignore quality scores. Then wonder why your model confidently states that your CEO founded Amazon.

The solution isn't just more data - it's better data. Uncertainty scoring shows you which examples are reliable before training starts.

Step 1

Find Quality Data, Fast

Search Hugging Face, Kaggle, APIs, or your own files. Every dataset comes with an uncertainty score - know what's reliable before you train.

Stop guessing if your data is good enough. We grade it automatically so you train on signal, not noise.

CSV

customer_id,date,amount
CUS-2847,03-15,129.99
CUS-9384,03-16,89.50

JSON

{
  "event": "page_view",
  "user_id": "usr_847",
  "page": "/checkout"
}

Text

Customer complaint:
"Product stopped working
after two weeks..."

Logs

[14:23:01] ERROR Failed
[14:23:02] INFO Retry
[14:23:05] SUCCESS OK

Step 2

Automatic Normalization

Different schemas? Mismatched types? Missing fields? We detect and fix it all. Your messy data becomes clean training sets automatically.

Before: Chaos

CSV: customer_id,amount,date
     CUS-2847,129.99,2024-03-15

JSON: {"user_id": "usr_847fh3",
       "timestamp": 1710547200}

Text: "Customer complaint about
       product quality..."

Logs: [ERROR] Database failed
      [INFO] Attempting retry...

After: Clarity

{
  "input": "Customer CUS-2847 with 
           purchase history and 
           recent support ticket",
  "context": "High-value, at risk",
  "label": "churn_likely",
  "confidence": 0.87,
  "features": [
    "purchase_frequency",
    "complaint_sentiment",
    "support_interactions"
  ]
}

Under the Hood

• Uncertainty scoring: Each example gets a reliability score
• Schema alignment: Different formats mapped to unified structure
• Outlier detection: Weird data flagged before it breaks training
• Deduplication: Similar examples merged intelligently

Step 3

Synthesize Data From Descriptions

No historical data for that new feature? Missing edge cases? We synthesize high-quality training data from descriptions.

This isn't random generation. We create data that matches your distribution, preserves relationships, and covers edge cases.

Proven at Scale

OpenAI, Anthropic, and Google all use synthetic data. Now you can too, without the $100M budget.

Privacy First

Synthetic data contains no PII. Train on millions of examples without touching a single real customer record.

Data augmentation is here

Step 4

Know Your Coverage Before Training

See exactly what scenarios your data covers and what's missing. We analyze distributions, detect gaps, and show uncertainty scores so you never train blind.

Distribution Analysis

Visual heatmaps show data density. See gaps and imbalances before they become model failures.

Quality Scoring

Every example gets an uncertainty score. Train on high-confidence data, validate on the rest.

Augmentation Suggestions

We tell you exactly what synthetic data to generate to improve model performance.

Relational Model Benefits

• Complete entity mapping with no orphaned records
• Guaranteed referential integrity across datasets
• Semantic consistency validation at scale
• Automatic detection of data quality issues

Why This Matters

gpt-5 hallucinates because it trained on the entire internet - including all the garbage. Your models don't have to.

By scoring data quality upfront, you train models that are accurate, reliable, and actually useful in production. No more "it worked in the demo."

Score Quality

Every dataset gets uncertainty scores automatically

Fill Gaps

Generate synthetic data for missing scenarios

Train Confidently

Models that work in production, not just demos

Stop training on garbage

Find quality data, see uncertainty scores, generate what's missing. Train models that actually work in production.

Book a demo

Data is the new oil.But most of it is crude.