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AI Chatbot Development

AI chatbot development that works in production, not just in demos.

I'm Dharanidharan — I build RAG chatbots and AI agents trained on your own data myself, engineered to give accurate, cited answers instead of confident guesses.

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There's a gap between a ChatGPT wrapper and an AI chatbot that actually runs reliably in a production environment. I build on the right side of that gap. My AI chatbot work covers two areas: RAG systems (chatbots trained on your documents that give accurate, cited answers) and AI agents (LLM-powered workflows that take actions in your existing tools — creating records, sending messages, escalating to a human). If you need broader process automation beyond a chatbot, that's a separate offering — see workflow automation. I don't sell AI for the sake of it — if a simpler solution fits, I'll tell you.

Technologies I use
OpenAI APILaravel AI SDKpgvectorLangChainPineconeRedis
What you get
RAG chatbot trained on your docsAn AI assistant that answers questions accurately from your knowledge base, with source citations.
AI agent with tool useAn LLM that can take actions — create records, send messages, escalate to a human — based on user input.
LLM features in existing appsEmbedding AI capabilities (summarisation, classification, generation) into your current product.
Prompt engineering + evaluationStructured prompts with test suites so you know the AI is behaving consistently.
Monitoring + cost controlsUsage dashboards, token cost tracking, and fallback logic for when the API is unavailable.
How it works

From brief to shipped.

01

Discovery

I map your goals, users, and technical requirements into a clear scope before any code is written.

02

Planning & Design

High-fidelity mockups and a technical architecture plan. You approve every screen before development starts.

03

Build & Test

Full-stack development in focused sprints with QA built into every milestone not bolted on at the end.

04

Launch & Support

Production deployment, performance audit, and a handover doc plus ongoing support after you ship.

Questions

Common questions about this service.

For RAG systems, I test retrieval quality, chunk sizing, and re-ranking strategies before delivery. I also build in source citations so users can verify answers.

A chatbot answers questions. An agent takes actions — it can call APIs, update records, send emails, or trigger workflows based on what the user asks. I build both.

Not necessarily. I can use Azure OpenAI (with data privacy agreements), run open-source models locally, or architect the system to anonymise data before it reaches any external API.

I build model-agnostic where possible, using abstraction layers (Laravel AI SDK, LangChain) that let you swap providers without rewriting the integration.

Ready to start your ai chatbot development project?

Book a free 30-minute call. I'll figure out the right service for your project together.

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