---
title: Build with Embeddings
description: 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.

:::info
The same embeddings power Avra's credit, fraud, and growth solutions. When you use embeddings directly, you get the same intelligence with full control over your model architecture.
:::

## 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

<SimpleDiscovery />

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.

> **Thin-file ready**
>
> Pre-training means even new or sparse entities arrive with meaningful representations.

> **Cross-functional**
>
> Risk, growth, fraud, and analytics teams share a common signal without rebuilding pipelines.

> **Private**
>
> Your data never leaves your workspace. Your RFM produces a customer-specific 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

**Real-time API**

Low-latency retrieval for onboarding, underwriting, and interactive analytics. See the [technical guide](/pt-BR/use-cases/embeddings/technical-implementation) for request schema.

**Batch Jobs**

Submit large portfolios for asynchronous processing. See [Batch Inference](/pt-BR/integrate/batch-inference) for setup.

**Feature Stores**

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

1. **Review integration options** in the [Technical Guide](/pt-BR/use-cases/embeddings/technical-implementation)
2. **Explore use cases** in [Applications](/pt-BR/use-cases/embeddings/embedding-applications)
3. **Configure monitoring** using [Model Lifecycle](/pt-BR/integrate/model-lifecycle)