> ## Documentation Index > Fetch the complete documentation index at: https://docs.avra.ai/llms.txt > Use this file to discover all available pages before exploring further. # The Graph Advantage > Why relationships are more predictive than records. ## From records to relationships Traditional intelligence treats entities as rows in a database. Each company, each individual — isolated, defined only by its own history. The economy is not a spreadsheet. Companies are nodes in a network, and the network is the signal. A company is defined by who it does business with, who owns it, who it employs, who sues it, and how all of that changes over time. A clean-looking company doing business with a fraud ring is not clean — it is a risk the spreadsheet cannot see. The Graph Foundation Model sees those connections. It treats relationships as structure, not enrichment. ## Two foundations, not one Generic foundation models give you general intelligence and no view of your business. Internal models give you a view of your business and no view of the world around it. Avra pre-trains two foundations and composes them. * The **Graph Foundation Model** brings the relational economy — counterparties, supply chains, judicial events, sector dynamics. * Your **Relational Foundation Model** brings your business — your customers, accounts, transactions, and the connections between them. * **Downstream models** inherit from both, and feed signal back into the RFM with every training run. You do not pick between general and specific. You get both, composed. ## Seeing the invisible Traditional systems require history to make predictions. No history, no score. In a graph, no entity is truly isolated. Even a brand-new company has context — the track record of its founders, the health of its sector, the stability of its region, the patterns of similar entities. We infer from those connections. This is how we score the unscorable. ## A flywheel, not a snapshot A frozen foundation is a one-time gift. A foundation that learns from every downstream task is an asset that appreciates. Every model you train on Avra generates signal about what predicted the outcomes you care about. That signal flows back into your RFM. The next model starts from a stronger base. The next one after that, stronger still. The longer you run on Avra, the larger the gap between what you can predict and what anyone else can. ## One foundation, every decision The same understanding that detects fraud also assesses credit risk and prioritizes leads. Lead Scoring, Paid Media Optimization, Field Sales Ranking Fraud Prevention, Risk Assessment, Entity Verification Credit Decisions, Portfolio Monitoring, Relationship Intelligence Churn Prediction, Lifetime Value, Custom Prediction Tasks Different questions, same foundations. When you solve one problem with Avra, the next is already half-solved.