Retrieval-Augmented Generation — Enterprise AI That Knows Your Business
RAG (Retrieval-Augmented Generation) is the gold standard for enterprise AI knowledge systems. Instead of relying on a generic LLM's training data, RAG connects AI to your specific documents, policies, and databases — so answers are always accurate, current, and traceable to real sources.
ChittorTech builds private, secure RAG systems deployable on your own infrastructure — zero data leakage, 100% control.
Everything Your Enterprise Knowledge System Needs
Production-ready RAG systems with enterprise security, source attribution, and seamless integrations.
Instant Semantic Search
Find answers across thousands of documents in milliseconds using vector similarity search — no keyword matching, pure meaning-based retrieval.
Source Attribution & Citations
Every AI answer includes links to the exact source documents and page numbers — building trust and enabling verification.
Role-Based Access Control
Granular permissions ensure employees only access documents they're authorized for — HR policies for HR, financial data for finance.
Real-Time Document Sync
Connect to Google Drive, Confluence, SharePoint, or any file system for automatic syncing — knowledge base always stays current.
Multi-Language Support
Query in English, Hindi, or any regional language. Our RAG systems support multilingual document ingestion and cross-language retrieval.
Private Cloud Deployment
Full on-premise or private VPC deployment available. Your documents and queries never touch external AI services without your consent.
Usage Analytics & Insights
Track what employees are searching for, identify knowledge gaps, and see which documents get the most queries — continuously improve your KB.
Conversational AI Interface
Natural chat interface with memory — ask follow-up questions, drill deeper, and get contextual answers across a full conversation thread.
API & Webhook Integration
Embed RAG-powered search into your existing apps, Slack bots, websites, or custom portals through a clean REST API.
Who Uses RAG Knowledge Base AI?
Any organization with more documents than people can read deploys RAG to unlock its institutional knowledge.
Internal Employee Helpdesk AI
Let employees ask HR policies, IT procedures, benefits information, and onboarding FAQs in natural language — reducing HR ticket volume by 60%+.
Customer Support Knowledge Base
Power your support agents or public-facing chatbot with your entire product documentation, so every answer is instant and accurate.
Legal & Compliance Document Search
Search contracts, regulations, case law, and internal compliance policies in seconds — reduce legal research time from hours to minutes.
Technical Documentation Assistant
Let developers and engineers query API docs, architecture documents, runbooks, and error logs through conversational AI.
Medical & Clinical Knowledge Search
Hospital staff can query clinical protocols, drug interactions, treatment guidelines, and patient histories through a secure AI assistant.
Sales Intelligence & Product Knowledge
Equip your sales team with AI-powered access to case studies, competitor analysis, pricing sheets, and product specs during live demos.
Manufacturing Process Documentation
Maintenance engineers can ask detailed questions about equipment manuals, safety procedures, and troubleshooting guides on the shop floor.
Financial Research & Report Search
Let analysts query thousands of financial reports, market research documents, and regulatory filings through natural language search.
Your RAG System Live in 4 Steps
We audit your existing documents, define the scope of the knowledge base, and build the ingestion pipeline for all data sources.
Documents are semantically chunked, converted to vector embeddings, and indexed in a high-performance vector database (Pinecone, Weaviate, or pgvector).
We build the retrieval pipeline, connect to the chosen LLM (GPT-4, Claude, Gemini, or local open-source), and implement prompt engineering for accuracy.
A polished chat interface with RBAC, audit logs, and your branding — deployed on your cloud infrastructure or on-premise servers.
