✨ Enterprise RAG Pipelines

Intelligent SearchAcross All Media

Discover how Retrieval-Augmented Generation enables sophisticated search across documents, databases, images, and real-time data sources with enterprise-grade accuracy.

What is RAG?

Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge sources to provide accurate, contextual responses based on your organization's data.

Access to up-to-date information beyond training data

Reduced hallucinations through grounded responses

Domain-specific expertise without model retraining

Cost-effective compared to fine-tuning large models

Transparent source attribution for answers

Supported Media Types

Text Documents

PDFs, Word docs, plain text files, and web pages

Examples:

Company policies
Technical documentation
Research papers

Structured Data

Databases, spreadsheets, and API responses

Examples:

Customer records
Product catalogs
Financial data

Images & Media

Visual content with OCR and multimodal processing

Examples:

Scanned documents
Charts and diagrams
Product images

Real-time Sources

Live data feeds and streaming information

Examples:

News feeds
Social media
IoT sensor data

Supported Data Stores

Connect to your existing data infrastructure with support for popular databases, cloud storage, and document management systems.

Microsoft SQL

Database

PostgreSQL

Database

Oracle

Database

MySQL

Database

MongoDB

NoSQL

Firestore

NoSQL

DocumentDB

NoSQL

DynamoDB

NoSQL

Couchbase

NoSQL

AWS S3

Cloud Storage

Google Cloud Storage

Cloud Storage

Azure Blob Storage

Cloud Storage

SharePoint

Document Management

Google Drive

Document Management

Dropbox

Document Management

Confluence

Document Management

Elasticsearch

Search Engine

Redis

Cache

Ready to Build Your RAG Pipeline?

Let's create intelligent RAG pipelines that connect your data with AI to deliver accurate, contextual responses for your users.