Build with Embeddings
Use Avra's learned representations as features in your own models.
What are embeddings?
1024-dimensional vectors that encode everything the Graph Foundation Model and your Relational Foundation Model understand about an entity: network position, relationships, behavioral patterns, risk profile, trajectory.
Compressed intelligence you can plug into any ML model.
Why Use Our Embeddings?
| Your Approach | Time | Coverage | Signal Quality |
|---|---|---|---|
| Manual feature engineering | Months | Your data only | Limited to what you can imagine |
| Traditional data providers | Days | Partial coverage | Static, lagging indicators |
| Avra Embeddings | Days | Full coverage | Multi-hop relationships, temporal patterns |
Our Graph Neural Network captures patterns you can’t manually engineer: second-degree counterparty risk, regional clusters, ownership network anomalies, behavioral trajectory.
See It In Action
Drag the slider to see how model confidence grows as onboarding data arrives.
- 0%Unknown company registeredOnly CNPJ available
- First transaction dataPayment history: 1 month
- Network relationships detectedSupplier connections mapped
- Growth patterns identifiedRevenue trajectory visible
- Behavioral profile completeFull pattern analysis
As onboarding data arrives (payment history, supplier relationships, transaction patterns), the model’s confidence in the entity’s outcome grows. Even with limited initial data, it can make useful predictions by placing the company near similar companies in the 1024-dimensional embedding space.
What You Receive
| Asset | Details |
|---|---|
| Embedding API | Deterministic endpoint returning 1024-d vectors; slice client-side to 512 / 256 / 128 / 64 / 32 / 16 |
| Metadata | Model snapshot, version, and quality flags for provenance tracking |
| Explainability | Optional attribution payloads highlighting top factors |
| Support | Dashboard insights, webhook notifications, solution engineering |
Delivery Patterns
Low-latency retrieval for onboarding, underwriting, and interactive analytics. See the technical guide for request schema.
Submit large portfolios for asynchronous processing. See Batch Inference for setup.
Persist embeddings in your warehouse or feature store to power ML pipelines and experimentation.
When to Use Embeddings
- You need predictive signal for entities with little proprietary history
- You want to centralize intelligence for multiple initiatives without duplicating work
- You’re building models that must remain explainable across teams
Get Started
- Review integration options in the Technical Guide
- Explore use cases in Applications
- Configure monitoring using Model Lifecycle