Question 1

What is a LangChain chatbot and how is it different from a regular chatbot?

Accepted Answer

A LangChain chatbot uses the LangChain framework to connect a large language model (like GPT or Claude) to external tools, APIs, databases, and memory systems. Unlike a regular rule-based chatbot that follows fixed scripts, a LangChain chatbot can reason, retrieve live data, call APIs, and maintain conversation context across sessions. We use LangChain to build chatbots that understand your business data and take real actions - not just answer FAQs.

Question 2

What is a RAG chatbot and when should I use one?

Accepted Answer

A RAG (Retrieval-Augmented Generation) chatbot combines a vector database with an LLM so the AI can search your own documents, PDFs, knowledge bases, or databases before answering. This means the chatbot gives accurate, cited answers from your actual content rather than hallucinating. Use a RAG chatbot when you need the AI to answer questions from your internal documentation, product manuals, legal contracts, or support knowledge base. We build RAG systems using Qdrant as the vector store and LangChain for orchestration.

Question 3

What is a LangGraph agent and how does it work?

Accepted Answer

LangGraph is a framework built on top of LangChain that lets you build stateful, multi-step AI agents as directed graphs. Each node in the graph is an action (like calling an API, searching a database, or running code), and the agent decides which path to take based on the current state. LangGraph agents are ideal for complex workflows that require branching logic, human-in-the-loop approval, or multi-agent coordination. We use LangGraph to build enterprise AI agents that handle end-to-end business processes autonomously.

Question 4

What is the difference between a chatbot, a RAG chatbot, and an AI agent?

Accepted Answer

A standard chatbot follows pre-defined conversation flows. A RAG chatbot adds retrieval - it searches your documents to give accurate answers. An AI agent goes further: it can use tools, call APIs, write and execute code, make decisions, and complete multi-step tasks autonomously. For example, a RAG chatbot answers 'What is our refund policy?' from your docs. An AI agent can process the refund, update your CRM, and send the confirmation email - all in one go. We build all three depending on your use case.

Question 5

Can you integrate OpenAI, Claude, or Gemini APIs into our existing software?

Accepted Answer

Yes - API integration is one of our most common engagements. We connect OpenAI, Anthropic Claude, and Google Gemini APIs to your existing web apps, mobile apps, CRMs, and enterprise systems. This includes prompt engineering, context management, streaming responses, cost optimization, and fallback handling. Most integrations are completed in 2–4 weeks.

Question 6

What is agentic AI and why should my business care?

Accepted Answer

Agentic AI refers to AI systems that autonomously plan, decide, and act to complete complex goals without step-by-step human instruction. Unlike traditional AI that responds to prompts, agentic AI uses tools, browses data, writes code, and coordinates with other agents. Gartner projects 40% of enterprise applications will have agentic AI by end of 2026. Businesses deploying agentic AI now are gaining a significant competitive advantage in automation and operational efficiency.

Generative AI Development Services

What's included in our Generative AI Development Services

Custom LLM Development & Integration

RAG System Development

AI Agent Development

Agentic Workflow Automation

OpenAI / Claude / Gemini API Integration

From discovery to live product

Discovery

Architecture

Development

Launch & Support

Common questions

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