The value of time depth
A snapshot shows how work looked once. A multi-year history shows:
- How the same tasks are handled under different conditions.
- How processes evolve and improve.
- Rare situations that only appear over long periods.
Why buyers pay for it
- Models trained on longitudinal data handle variation better.
- Evaluation against historical records tests robustness, not just peak performance.
- Long histories cannot be synthesized convincingly.
The company profile
This is why SourceX looks for established companies: 50+ full-time employees at peak, with years of accumulated records across their systems. Time in operation is itself a data asset.
What this means for partners
The longest-established companies in your network may hold the most valuable data. Introduce one to SourceX; if a deal closes and SourceX collects its fee, you earn 25% of that fee, up to $100,000 per company.
Refer a company · How rewards work
Common questions
Why is multi-year data more valuable for AI training?
Multi-year data shows how tasks evolve, processes improve, and rare situations appear over time. This depth helps AI models handle variations better and provides a more robust basis for training compared to single snapshots.
What kind of companies does SourceX look for?
SourceX looks for established companies with 50+ full-time employees at their peak. These companies should have years of accumulated records across their systems, as their time in operation represents a valuable data asset.
How can partners earn rewards with SourceX?
Partners can earn rewards by introducing established companies to SourceX. If a deal closes and SourceX collects its fee, the partner earns 25% of that fee, up to $100,000 per company.
What makes historical data so important for AI models?
Historical data enables models to handle variation effectively and allows for robust evaluation against past records. It tests how well models perform under diverse conditions, rather than just at peak performance, and cannot be convincingly synthesized.
