I will build a rag chatbot with gemini, pinecone and ai agents

Most AI chatbots can generate an answer. The real challenge is making them retrieve the right information, use your data correctly, and know when to escalate.

I build RAG chatbots and multi-agent AI systems using Gemini, Pinecone, LLMs, APIs, and databases.

I have built a natural-language-to-database system that lets users query 100K+ MySQL records without writing SQL, achieving under 1.5s average response time and reducing repeat-query latency by ~65% using semantic caching.

I also developed a multi-agent customer support system with intelligent routing, specialized agents, sentiment-based escalation, and real-time decision logic.

What I Build

RAG chatbots over documents, databases & APIs

Gemini / LLM-powered AI agents

Pinecone vector search & semantic retrieval

Multi-agent routing and escalation

Structured + unstructured data Q&A

Database and API integrations

Semantic caching for lower latency and cost

Custom chat interfaces and admin dashboards

Process

1. Understand your data and use case
2. Design the RAG/agent architecture
3. Build against your real data
4. Test accuracy, latency and reliability
5. Deliver with documentation

Message me before ordering Premium for complex multi-agent systems