Aman G.

I build production-ready AI agents and automation systems for SaaS companies, startups, agencies, and SMBs that need to reduce manual work, connect disconnected tools, improve knowledge access, and scale operations with reliable AI. My core focus is AI Agent Development across Agentic AI, AI Workflow Automation, RAG, API Integration, and Python Back-end Development. I turn repetitive or fragmented processes into dependable AI-powered workflows that improve response times, reduce operating effort, and make internal systems easier to use. 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 𝐭𝐲𝐩𝐢𝐜𝐚𝐥𝐥𝐲 𝐡𝐢𝐫𝐞 𝐦𝐞 𝐟𝐨𝐫: • Custom AI Agent Development for research, support, sales, operations, and internal workflows • AI Workflow Automation using n8n, APIs, web-hooks, and business logic • RAG Development for document Q&A, semantic search, and internal knowledge assistants • OpenAI API Integration, Claude API Integration, and third-party SaaS integrations • LangGraph and LangChain workflows with tool calling, memory, state, and human-in-the-loop steps • Multi-Agent Systems, agent orchestration, and MCP / Model Context Protocol integrations • Python and FastAPI backends for agent APIs, background jobs, databases, and deployment • CRM, sales, customer support, document, and data automation I bring 10+ years of experience and a production-first engineering approach. I design systems for real workflows with: • Retries, fallback, validation, and error handling • Logging, observability, security, and maintainability • Clean deployment and handover 𝐈’𝐦 𝐞𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐥𝐲 𝐰𝐞𝐥𝐥 𝐬𝐮𝐢𝐭𝐞𝐝 𝐟𝐨𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐢𝐧𝐯𝐨𝐥𝐯𝐢𝐧𝐠: • Production-ready AI agents • AI agents with RAG or LangGraph • n8n Automation • Custom RAG applications and RAG chatbots • API-heavy AI integrations • CRM and Customer Support Automation • AI integration into existing software My stack includes Python, FastAPI, LangGraph, LangChain, OpenAI API, Claude API, RAG, MCP, n8n, REST APIs, Webhooks, PostgreSQL, Supabase, Pinecone, Qdrant, pgvector, Docker, AWS, Tool Calling, and Vector Search. 𝐖𝐡𝐚𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐞𝐬 𝐦𝐲 𝐰𝐨𝐫𝐤: • End-to-end ownership across agent logic, retrieval, back-end engineering, APIs, databases, deployment, and integrations • Strong Python engineering when complexity and control matter • n8n automation when speed and maintainability make more sense • Production-focused orchestration with state, memory, tool use, and human-in-the-loop flows • RAG systems built around ingestion, retrieval, vector search, grounding, and usable knowledge access • Reliability-first delivery with retries, fallback, validation, logging, and monitoring The goal is to reduce manual workload, improve lead response, strengthen customer support, speed up knowledge retrieval, and create scalable AI workflows your team can rely on. If you need a custom AI agent, RAG system, AI automation workflow, or Python-based AI integration, send me your project brief, current workflow, or desired outcome.