> ## Documentation Index
> Fetch the complete documentation index at: https://docs.corvic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Platform Architecture

> Understand the architecture of the Corvic Platform

## Overview

The Corvic Platform is designed as an end-to-end solution for transforming enterprise data into actionable insights through multi-modal embeddings and AI agents.

## Architecture Components

### Data Ingestion Layer

The platform ingests both structured and unstructured data:

* **Structured data**: Parquet, CSV, DSV, and TSV tabular files
* **Unstructured data**: documents, spreadsheets, images, audio, and video

Data is brought in via file upload, storage connectors, web sources, or programmatic API fetches, then processed through workflows that clean, transform, and prepare it for embedding. See [Data Types](/features/data-types) for the full list of supported formats.

### Embedding Generation Layer

The platform generates and manages multi-space embeddings:

* **Corvic Tables**: Define what entities to embed (distributed processing tables for data transformations)
* **Spaces**: Transform Corvic Tables into embeddings using state-of-the-art algorithms
* **Quality Metrics**: Monitor embedding quality with real-time metrics

### Storage Layer

Embeddings are stored in a distributed, high-performance storage system that supports:

* Vector search and retrieval
* Multi-space query capabilities
* Scalable storage for large datasets

### Agent Layer

The platform supports agent creation and configuration:

* **LLM Integration**: Leverage multiple LLM providers
* **Multi-space Traversal**: Agents can query across multiple embedding spaces
* **Dynamic Policies**: Generate insights from ingested data

## Data Flow

Here's how these concepts work together in Corvic, across four stages:

**Connect**
→ Bring in your data from files, object stores, databases, live web sources, and external APIs into rooms.

**Explore**
→ Investigate and query your data with an agent to surface insights in plain language.

**Transform**
→ Apply distributed processing to clean, join, enrich, and embed data into reusable Corvic Tables and Spaces.

**Deploy**
→ Assemble grounded agents and workflows, share chats and artifacts, and integrate via MCP to drive operational decisions.

## Key Features

### Multi-Modal Embeddings

Corvic creates embeddings across modalities — **Semantic** (text), **Tabular** (structured), **Image**, and **Relational** (graph/relationship) — so you can search and reason over any kind of data.

### Distributed Processing

The platform uses distributed high-performance computation:

* 10x-100x faster than alternative implementations
* Optimized proprietary algorithms
* Efficient resource utilization

### Security & Privacy

* Data isolation between organizations and rooms
* State-of-the-art cryptography
* Compliance with privacy standards
* Secure development practices

## Integration Points

The platform provides multiple integration points:

* **REST APIs**: Direct API access to embeddings and agents
* **MCP Integrations**: Model Context Protocol support
* **Python SDK**: Native Python integration
* **Framework Integrations**: OpenAI, Langchain, CrewAI, FastAPI

<Card title="API Integrations" icon="plug" href="/integrations/overview">
  Learn about integrating with the Corvic Platform.
</Card>

## Scalability

The architecture is designed for scale:

* Horizontal scaling of compute resources
* Distributed storage for large datasets
* Efficient embedding generation at scale
* Support for multiple concurrent users and rooms

<Card title="Get Started" icon="rocket" href="/get-started/quickstart">
  Start building with Corvic.
</Card>