Business AI Chatbots That Use Your Trusted Information
Ready-made AI tools may not know enough about your business, may give incorrect answers, or may create risks for private company data. Our chatbot uses trusted documents and clear instructions to answer questions and handle tasks.
< 50 ms
Webhook response time
?68%
Fewer support tickets
99.9%
Uptime
Anavya Infotech builds custom AI chatbots with Retrieval-Augmented Generation (RAG). RAG helps an AI find relevant information in your documents before answering. We connect secure Large Language Models (LLMs) to your business data to support customers, find internal information, and help with work tasks.
AI Answers Based on Your Business Information
General chat tools may not know what is in your business systems. RAG lets a chatbot look up trusted information before it responds:
- Answers Based on Verified Documents: Instructions tell the AI to answer using information from your verified company documents.
- Find Information by Meaning: Private company information is organized so the AI can find relevant details even when a visitor uses different words.
- Clear Safety Rules: Content checks help prevent off-topic replies, data leaks, and actions the user is not allowed to take.
- Connect to Your Business Tools: The AI can use secure APIs to update CRM records, request refunds, or book appointments.
How the AI System Works
Our AI systems organize information so they can find answers and carry out tasks:
- Organize and Store Documents: Converts PDFs, Notion pages, and SQL data into searchable formats and stores them in vector databases such as Pinecone, Qdrant, and PGVector.
- Search by Meaning and Keywords: Combines meaning-based search with keyword search (BM25) to find useful information in long business documents.
- Rank the Most Useful Information: Picks and sorts useful text with Cohere Rerank before sending a question to the LLM. This can keep responses under 50 ms and reduce API usage.
- Connect to Other Services: Connects the AI to HubSpot, Salesforce, Zendesk, or Stripe so it can handle tasks in those services.
Business Use Cases Engineered for Immediate ROI
- Customer Support at Any Time: Answers common support questions, product questions, and technical issues without needing a support agent for each one.
- Find and Qualify Leads: Reviews questions from potential customers, collects project details, and books meetings in sales calendars.
- Search Company Information: Helps staff find information in employee handbooks, engineering documents, and company policies by asking questions in everyday language.
- Send Requests to the Right System: Turns customer requests into organized information and sends them to your ERP or ticketing system.
General AI Chatbots Compared With Custom RAG Chatbots
| Feature | Generic AI System (ChatGPT / Wrappers) | Anavya RAG Chatbot |
|---|---|---|
| Response Accuracy | May give incorrect answers | Uses information found in your business documents |
| Data Privacy | May use public cloud services | Private cloud; user input is not used for training |
| System Action Execution | Can only reply with text | Can use APIs and webhooks to take actions |
| Knowledge Updates | Requires model retraining | Updates when you add a new document |
| Response Time | 2.5–5 seconds | Under 50 ms for API responses |
Automixa AI: Results in Use
- Client Need: Automixa AI needed software that could manage complex workflows for more than 10,000 creators around the world at the same time.
- What We Built: Anavya Infotech built a RAG system using LangChain, FastHTML/Python, Qdrant, and Redis to organize and manage requests.
- Results: Webhooks responded in under 50 ms, support tickets fell by 68%, and the service stayed available 99.9% of the time while handling many requests.
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