Technology Capabilities

LlamaIndex RAG & Data Connections

Connect your private company documents securely with AI models to build highly accurate search engines and chatbots.

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LlamaIndex RAG & Data Connections
About

What is LlamaIndex?

LlamaIndex is a data framework for large language model (LLM) applications. It provides data ingestion pipelines, indexing tools, and query interfaces to connect private data sources (like PDFs, Notion pages, or SQL databases) to AI models, powering Retrieval-Augmented Generation (RAG).

Benefits

Why Choose LlamaIndex for Data Projects?

Accelerate your product delivery and ensure platform stability with these core benefits.

Connects Private Data

Allows AI models to search company manuals, product guides, and transaction tables accurately.

Optimized Search Indexing

Indexes unstructured documents, returning matching text snippets to the model in milliseconds.

Eliminates Model Hallucinations

Forces models to base answers strictly on your uploaded files, preventing false info.

Multiple File Connectors

Features pre-built parsers for PDFs, Excel sheets, Word files, and database tables.

Use Cases

What Can We Build with LlamaIndex?

Explore the types of applications and integrations we can build for your operations.

Document Search Engines

Knowledge Base Chatbots

Internal Policy Search Bots

Automated Product Matchers

Interactive Manual Guides

Services

Our LlamaIndex Development Services

Professional engineering services tailored to match your specific requirements.

RAG Pipeline Configuration

We design and configure data ingestion scripts to index your company files.

Document Parser Integrations

Configure connectors to parse and clean PDFs, text documents, and SQL tables.

Vector Database Setup

Set up vector storage (like Pinecone or pgvector) to store search indexes.

Query Speed Tuning

Optimize index segmentation and search parameters to return fast answers.

Technical Stack

Key Capabilities

Core framework capabilities and integrations we set up for this technology.

Retrieval-Augmented Gen (RAG)Document Ingestion PipelinesVector Indexing SchemasPinecone/pgvector StorageQuery Retrieval InterfaceHallucination Prevention
Partner

Why Choose Chandak Infotech?

At Chandak Infotech, we combine technology expertise with a business-focused approach to develop reliable and scalable digital solutions. Our team works closely with clients to understand their requirements and select the right technology for their application.

  • Experienced development team
  • Custom software solutions
  • Scalable architecture
  • Business-focused development
  • API and third-party integrations
  • Responsive design
  • Ongoing maintenance and support
Process

Development Process

01

Requirement Analysis

Understand business goals and technical requirements.

02

Planning & Architecture

Define the application structure and development approach.

03

UI/UX & Development

Build the solution using the selected technology.

04

Testing

Test functionality, performance and responsiveness.

05

Deployment

Deploy the application to the required environment.

06

Support

Provide ongoing maintenance and improvements.

FAQ

Frequently Asked Questions

LlamaIndex is used to index private data sources (like PDFs or Notion) so that AI models can search them and write accurate answers.

It extracts facts from your private files and prompts the AI model to write answers based *only* on that verified text.

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