SaaS product for building data infrastructure for generative AI applications. It helps enterprises create data fabrics, empowers AI teams with mission-critical tools, and offers plug-and-play infrastructure. Provides features for data catalogs, ETL pipelines, and security.
Features
Knowledge Bases
Helps enterprises build data fabric for their AI applications, providing structured data management.
AI Labs Onboarding
Facilitates the integration of AI labs into the data infrastructure for scalable AI solutions.
Machine Learning Ontology
Creates structured ontology for machine learning processes, enhancing data organization.
Read The Docs Processing
Automates document processing, making it easier to handle large quantities of data efficiently.
Vector Database Support
Context Data supports multiple vector databases, allowing for flexible data transformation and embedding solutions.
Data Pipeline Setup
Allows users to set up a data pipeline quickly, typically within a specified time frame.
Data Security
Implements measures to ensure data security, safeguarding sensitive information.
Real-time Data Processing
Handles real-time data processing for timely insights and actions.
Data Lineage Tracking
Supports tracking of data lineage to maintain data integrity and traceability.
Multi-tenancy and Data Isolation
Manages multi-tenancy in SaaS environments while ensuring data isolation for different users.
Fault Tolerance and High Availability
Uses mechanisms to ensure fault tolerance and high availability, maintaining service reliability.
PostgreSQL Integration
Integrate with PostgreSQL for accessing and managing database information.
MySQL Integration
Connect with MySQL databases for data retrieval and management.
Snowflake Integration
Utilize Snowflake for cloud-based data warehousing solutions.
Amazon Redshift Integration
Interface with Amazon Redshift for big data analytics and warehousing.
Google BigQuery Integration
Access Google's serverless, highly scalable data warehouse.
Amazon S3 Integration
Retrieve and store data using Amazon S3 cloud storage.
Google Cloud Storage Integration
Utilize Google Cloud Storage for storing and retrieving data.
Microsoft Azure Integration
Integrate with Microsoft Azure for comprehensive cloud services.
Shopify Integration
Connect with Shopify for ecommerce data and operations.
Salesforce Integration
Sync with Salesforce for CRM data and functionality.
Stripe Integration
Utilize Stripe for online payment processing and data.
Zendesk Integration
Integrate with Zendesk for customer service and ticketing.
Dropbox Integration
Manage and access files stored in Dropbox.
HubSpot Integration
Sync with HubSpot for inbound marketing and sales data.
Intercom Integration
Connect with Intercom for customer communication solutions.
Pinecone Vector Database
Implement Pinecone for managing vector data.
Weaviate Vector Database
Utilize Weaviate for handling vector storage and retrieval.
Chroma Vector Database
Use Chroma for storing and organizing vector data.
Milvus Vector Database
Implement Milvus for efficient vector data management.
SingleStore Vector Database
Integrate SingleStore for vector database solutions.
LanceDB Vector Database
Utilize LanceDB for database operations on vector data.
neo4j Vector Database
Connect with neo4j for graph-based vector data management.
OpenAI Embedding and Chat Models
Implement OpenAI models for embedding and chat functionalities.
Gemini Embedding and Chat Models
Use Gemini for embedding and chat model integration.
Meta Embedding and Chat Models
Integrate with Meta for AI chat and embedding services.
Cross Platform Data Transformations
Build fully semantic data models where analysts and developers can quickly and easily build contextual relationships across multiple data systems.
Managed Vector Database
For clients that don't want the hassle of managing multiple vector databases, offers managed vector databases and indexes backed by Pinecone which clients can set up in a few minutes.
Data Lineage Graph
Automatically generate data ontologies which map all of source data, embedding models, vector databases and how they all connect and relate with each other.
Chat with your Vector Database
Once data has been written to the vector database, clients can quickly connect to all vector databases and start chatting with the data in a few minutes.
Modular Architecture
Easily integrate new data sources, embedding models, and vector databases.
Batch Processing & Configurable Chunking
Efficiently handle large datasets with configurable batch sizes while maintaining semantic context.
Multiple Data Source Support
Support for a wide range of data sources from PostgreSQL to Amazon S3.
Plug-and-Play Embedding Module
Integrate with leading models from OpenAI, Cohere, and Google Gemini.