Platform Architecture

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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.

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Architecture Components

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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 for the full list of supported formats.

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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

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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

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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

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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.

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Key Features

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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.

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Distributed Processing

The platform uses distributed high-performance computation:

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

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Security & Privacy

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

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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

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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