Early access

Your dataset is about to get a lot bigger.

LibGem's aggregation platform pools anonymized signals from across your industry. Your models train on the whole market, not just your slice of it.

Request access How it works
Aggregation pools

Contribute your slice. Train on everything.

LibGem · Training Workspace  ·  refreshed 10s ago
Total pooled records
7.2M
Pools you're in
3
Avg data multiplier
78x
Synthetic fidelity
97.3%
Total credits
98,440
Pool Your contribution What you train on Pool growth
Churn
Customer Churn
Behavioral + subscription lifecycle signals
47K records
2.4M synthetic
51x your contribution
+4.2%
this week
Forecasting
Demand Forecasting
Supply chain + order volume, seasonal patterns
12K records
1.1M synthetic
92x your contribution
+2.8%
this week
Fraud
Payment Fraud Detection
Transaction anomalies, cross-network patterns
8K records
890K synthetic
111x your contribution
+6.1%
last 7 days
LTV
Customer Lifetime Value
Engagement depth + revenue expansion signals
31K records
1.8M synthetic
58x your contribution
+1.9%
this month
LibGem

How it works

LibGem pools anonymized signals across companies with the same prediction challenges, then generates synthetic training data at the scale your models actually need. Your raw data never leaves your system. What comes back is a dataset built from your whole market, not just your slice of it.

01

Connect your data

Pipe your data through our secure ingestion layer. It's anonymized on arrival, aggregated with similar companies, and never stored in any identifiable form. You stay in control of everything you contribute.

02

We generate the dataset

LibGem synthesizes training data that preserves the statistical patterns of the pooled set, without exposing any single contributor. The result carries signals from your full market segment, not just your own slice.

03

Train from day one

Browse available pools by prediction task and spend credits to access them. The synthetic dataset exports directly into your training pipeline in the format your framework expects. Run your first job.

Use Cases

What teams build with it

LLM Post-Training & Fine-Tuning

Domain-specific training corpora built from pooled, anonymized signals across your vertical. More variety in, fewer blind spots out.

Churn & Retention

Train on complete customer journeys from across the industry, including edge cases you've never personally seen. Your model stops guessing at patterns it hasn't lived.

Demand Forecasting

Category-level demand signals pooled across companies with shared supply dynamics. Better forecast from broader market visibility than any team generates alone.

Fraud Detection

Cross-company transaction patterns that surface fraud signals invisible in a single dataset. Attack patterns that hit others first become part of your training set.

Data Pools Available

POOL anonymized Your LLM Customer event sequences Churn & retention signals Demand patterns Fraud transactions Behavioral sequences One agreement · All pools · No individual licensing

Retention Threshold

With pool Alone earlier intervention Low risk High risk Intervene before customers commit to leaving

Forecast Confidence

Without pool With pool Now Historical 12-week forecast → Pooled signals narrow uncertainty. Right stock, right time.

Attack Pattern Propagation

Co. A Co. B Co. C You 6-week lead time Pool alert Arrives Wk 2 Wk 4 Wk 6 Wk 8 Attacks that hit peers first enter your training set immediately

Stop waiting for enough data. The pool is here.

We're onboarding AI and data teams working on model training, forecasting, and fraud detection.

Request access