Custom AI Agent

About this Gig

I build custom AI agents that go beyond basic chatbots. These systems can reason through tasks, maintain memory, use external tools, connect with APIs and databases, retrieve knowledge from business data, and automate real-world workflows. Whether you need an intelligent business assistant, research agent, customer support system, inbox assistant, internal knowledge agent, workflow automation solution, or an AI-powered MVP, I can design and develop a solution tailored to your specific use case. What I can build: • Custom AI agents tailored to your business workflows • AI agents with persistent memory and user context • Tool-using agents that can perform actions and complete tasks • RAG systems connected to documents, PDFs, and business knowledge • Multi-agent systems for complex collaborative workflows • AI workflow automation for repetitive business processes • API-connected agents integrated with external platforms and services • AI backends and production-ready APIs using Python and FastAPI • Telegram and messaging-based AI assistants • AI MVPs for startups and new product ideas Core capabilities include: ✓ Persistent conversational and user memory ✓ Custom tools and function calling ✓ REST API and third-party integrations ✓ RAG and document retrieval pipelines ✓ Vector databases and embeddings ✓ Multi-agent orchestration ✓ Structured outputs and data validation ✓ Database integration ✓ Workflow automation ✓ Streaming AI responses ✓ FastAPI backend development ✓ Dockerized deployment ✓ Cloud deployment support ✓ Maintainable and scalable architecture My technical stack includes Python, FastAPI, Agno, CrewAI, LLM APIs, Gemini, OpenAI-compatible models, Hugging Face, PyTorch, PostgreSQL, PgVector, SQLite, vector databases, embeddings, RAG pipelines, REST APIs, MCP, Docker, Railway, Render, and Vercel integrations. I focus on building practical AI systems, not simple prompt wrappers. My experience includes intelligent inbox management agents, persistent-memory AI assistants, multi-agent HR automation systems, RAG-powered knowledge assistants, Telegram-native AI agents, MCP integrations, AI backends, vector search systems, and deployed AI applications. My development process typically includes understanding your requirements, designing the agent architecture, implementing tools and memory, integrating APIs or knowledge sources, testing workflows and edge cases, and preparing the system for deployment. Every AI project is different. I will work with you to identify the right architecture based on your goals, workflow complexity, data sources, integrations, and deployment requirements.

Requirements

To get started, please provide as much of the following information as possible: • A brief description of your idea, business problem, or workflow you want to automate • The main goal of the AI agent and what you expect it to accomplish • A step-by-step description of your current workflow, if one already exists • The target users of the system, such as customers, employees, administrators, or internal teams • Any required integrations, including APIs, databases, websites, SaaS platforms, Telegram, email services, CRMs, or internal systems • Any documents, PDFs, knowledge bases, FAQs, datasets, or business information the AI agent should use • Your preferred LLM or AI provider, if applicable. For example: OpenAI, Gemini, open-source models, or another provider • Whether the agent requires persistent memory, user-specific context, conversation history, or long-term personalization • Whether the agent needs to perform actions using tools, APIs, functions, or external services • Your preferred deployment environment, if any, such as Railway, Render, Docker, Vercel-connected backend, or your own infrastructure • Any existing codebase, repository, API documentation, architecture, or technical requirements relevant to the project • Expected timeline, important milestones, and any specific constraints If you are unsure about the technical architecture, frameworks, database, memory system, RAG approach, or deployment setup, that is completely fine. Simply describe your idea, current problem, and expected outcome, and I can recommend a suitable implementation approach. Please do not share production passwords, private API keys, or sensitive credentials in the initial message. Secure access requirements can be handled appropriately once the project scope is established.

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