RAG & AI Chatbots

Enterprise chatbots powered by your internal data

Overview

Build AI chatbots that truly understand your business. Using Retrieval-Augmented Generation (RAG), we create chatbots that can accurately answer questions from your internal documents, knowledge bases, and databases.

The Problem We Solve

Generic AI chatbots often provide inaccurate or generic responses. Businesses need chatbots that understand their specific context, policies, and data—while maintaining accuracy and preventing hallucinations.

Use Cases

Our Approach

A structured methodology to deliver results efficiently.

1

A structured methodology to deliver results efficiently.

2

RAG Architecture Design: Choose optimal chunking, embedding, and retrieval strategies

3

Knowledge Base Setup: Process, index, and organize your documents

4

Chatbot Development: Build conversational interface with context management

5

Testing & Refinement: Iterate on accuracy, relevance, and user experience

6

Deployment & Monitoring: Launch with feedback loops and continuous improvement

Technology Stack

Comprehensive AI and GenAI services to accelerate your business transformation.

OpenAI / Azure OpenAI

FAISS / Pinecone / ChromaDB

LangChain

Python / FastAPI

React / Next.js

PostgreSQL / MongoDB

Expected Outcomes

Sample Engagement

Enterprise Knowledge Chatbot

Problem

Support teams spent 40% of time answering repetitive internal questions about policies, processes, and tools.

Solution

Built a RAG-powered chatbot over 10,000+ internal documents with role-based access, source citations, and escalation workflows.

Impact

60% reduction in internal support tickets, 4x faster information retrieval, 95% user satisfaction.

Ready to Get Started?

Share your use case with us and let’s explore how we can help.