Facteus for

Artificial Intelligence (AI)

Behavioral Ground Truth at Scale

Facteus delivers real-world consumer behavior data to power smarter, real-time, highly accurate enterprise AI systems and foundational model development. From personalization to predictive modeling, our transaction-based datasets bring AI products closer to how people actually spend, shop, and live.

Train AI Models on Ground-Truth Behavioral Signals

  • Ingest real-world credit and debit card transaction data—no surveys, no models.
  • Improve model accuracy by training on behavioral truths instead of inferred intent.
  • Build embeddings and personas based on actual purchases across time, category, and location.
  • Reduce model hallucination and bias by anchoring outputs to verified economic activity.
  • Access high quality, exclusive data sources ready for analysis and training.

Build Smarter Retail and Restaurant Enterprise AI Products

  • Train pricing, forecasting, or inventory models with product- and store-level data.
  • Model trade-down behavior and price sensitivity across brands and regions.
  • Forecast category-level demand using real-time purchasing patterns.
  • Fine-tune vertical-specific LLMs with domain-rich transaction narratives.
  • Benchmark consumer engagement across competitive brands and channels.

Power Hyper-Personalization at Scale

  • Map customer behavior across all retail channels and categories.
  • Understand wallet share dynamics internal data cannot capture.
  • Predict customer lifecycle changes based on spending pattern shifts.
  • Predict preferences and next-best actions based on real-life purchase sequences.
  • Integrate seamlessly into LLM or RAG frameworks for intelligent user interactions.

Fuel Product Discovery and Category Trend Modeling

Detect new category breakouts based on product-level sales data.

Surface micro-trends by demographic, region, or channel.

Rank emerging product types based on velocity and share-of-wallet.

Inform retail media, merchandising, or innovation pipelines with trendline data.

Map cross-category affinities for deeper consumer understanding.