Automation
AI Chatbot & Automation
Production RAG chatbots and LLM-powered automation pipelines. Retrieval-augmented generation, vector databases, LangChain/LlamaIndex, and workflow automation.
- RAG
- LLM integration
- automation pipelines
What is included
- Production RAG chatbot development
- LLM integration with vector databases
- LangChain and LlamaIndex workflows
- Automation pipelines for repeatable operations
Typical projects
A support or documentation chatbot grounded in your own content
An internal knowledge assistant backed by a vector database
LLM workflows that automate a repeatable manual process
Retrieval pipelines with evaluation so answers can be checked
How I Work
Inquiry
Tell me what you are building and the outcome you want.
Scope Call
A short call to pin down goals, constraints and timeline.
Proposal
A written scope, timeline and price before any work starts.
Build
Iterative delivery with regular progress updates.
Delivery
Deployed, documented and handed over cleanly.
Questions
- What is a RAG chatbot?
- Retrieval-augmented generation looks up relevant passages from your own content and gives them to the language model, so answers are grounded in your data instead of the model's memory alone.
- Which tools do you use?
- LangChain and LlamaIndex for orchestration, vector databases for retrieval, and FastAPI for serving.
- How long does a RAG chatbot take to build?
- A working prototype typically takes 2-4 weeks. A production system with evaluation and deployment takes 1-3 months.
- Can you automate workflows that are not chatbots?
- Yes. The automation work covers LLM-powered pipelines for repeatable operations, not only conversational interfaces.
Other services
Not sure which service fits or have more questions? Let's talk - I read everything