AI Automation Expert | AI Agents, Chatbots, n8n, Make, Zapier & RAG

We are looking for an experienced AI Automation Expert / Full-Stack Developer to build a small AI-powered customer support application. ‎The application will allow a business to provide its FAQs, company information, products, services, shipping details, return policies, refund policies, warranty information, and other business knowledge. ‎Customers will then be able to ask questions through a chat interface and receive answers based on the information provided by the business. ‎The application should also include a basic AI automation workflow that can connect the chatbot and knowledge-base functionality with external services using tools such as n8n, Make, or Zapier. ‎The goal is to build a focused and professional MVP that works reliably and can be expanded with more advanced AI agents and automation workflows in future phases. ‎ ‎ ‎Example ‎A business can provide: ‎Orders are normally delivered within 3–5 business days. ‎A customer could ask: ‎How long does delivery take? ‎The system should find the relevant information from the knowledge base and provide a clear answer. ‎The system could then use an automation workflow to perform an additional action, such as: ‎Sending information to another application ‎Triggering a webhook ‎Sending a notification ‎Creating a support task ‎Connecting with another business tool ‎The exact automation workflow can be discussed during the project. ‎ ‎ ‎What You Will Build ‎The MVP should include: ‎Clean and responsive frontend ‎Simple admin dashboard ‎Business information management ‎FAQ and knowledge-base management ‎Document/text upload functionality ‎Document and text processing ‎Searchable knowledge base ‎RAG-based information retrieval ‎OpenAI API integration ‎Customer-facing AI chatbot ‎Basic AI automation workflow ‎n8n, Make, or Zapier integration ‎API/webhook integration ‎AI-generated answers based on business information ‎Loading states ‎Basic error handling ‎Ability to add and update business information ‎Clean frontend and backend architecture ‎Testing of the complete workflow ‎ ‎How the Application Should Work ‎The main workflow should be: ‎Business Information → Add Knowledge → Process Information → Store/Search Knowledge → Customer Asks Question → Retrieve Relevant Information → AI Generates Answer → Display Answer → Automation Workflow ‎The business should be able to provide information such as: ‎Product information ‎Shipping details ‎Return policy ‎Refund policy ‎Warranty information ‎Payment information ‎Frequently asked questions ‎Company information ‎General customer-support information ‎The customer can then ask questions through the chatbot. ‎The system should retrieve relevant information from the knowledge base before generating an answer. ‎The chatbot should not make up information when the required information is not available. ‎ ‎ ‎AI Chatbot ‎The customer-facing chatbot should include: ‎Clean chat interface ‎User messages ‎AI assistant responses ‎Text input ‎Send button ‎Loading indicator ‎Error handling ‎Responsive design ‎Context-aware answers based on the business knowledge base ‎The chatbot should use the provided business information as its primary source of information. ‎ ‎ ‎AI Agent / Automation ‎The project should include a basic AI-powered automation workflow. ‎The developer should be able to use n8n, Make, or Zapier, depending on which solution is most appropriate. ‎The automation should demonstrate how the AI application can interact with external services. ‎Possible examples include: ‎Triggering an automation after a customer interaction ‎Sending chatbot information to another application ‎Sending notifications ‎Creating a support request ‎Calling an external API ‎Sending data through a webhook ‎Connecting the application with a CRM or business tool ‎Processing information through an automated workflow ‎We are open to your recommendation regarding which automation platform is best for this MVP. ‎n8n, Make, or Zapier does not necessarily need to be used for every workflow. The priority is demonstrating a clean and reliable automation flow that can be expanded later. ‎ ‎ ‎Knowledge Base & RAG ‎The application should allow the business to provide information that the chatbot can use when answering customer questions. ‎The developer should implement a suitable RAG approach for: ‎Processing uploaded information ‎Extracting text ‎Splitting content into smaller sections/chunks ‎Creating embeddings ‎Storing searchable information ‎Performing semantic search ‎Retrieving relevant information ‎Providing relevant context to OpenAI ‎Generating answers based on retrieved information ‎We are open to using: ‎PostgreSQL + pgvector ‎Supabase + pgvector ‎Pinecone ‎Qdrant ‎Weaviate ‎Another suitable vector-search solution ‎Please recommend the most appropriate solution for this MVP based on simplicity, cost, performance, and scalability. ‎ ‎Admin Dashboard ‎The admin area should include: ‎Simple dashboard ‎Business information section ‎Knowledge/document upload area ‎List of added information ‎Add/edit/update functionality ‎Knowledge-base management ‎Basic chatbot testing area ‎Basic automation configuration or testing area, where appropriate ‎The dashboard does not need to be complex. ‎The priority is functionality and a clean user experience. ‎ ‎Automation Integrations ‎The application should be structured so it can communicate with external services through: ‎REST APIs ‎Webhooks ‎n8n ‎Make ‎Zapier ‎The developer should have a good understanding of connecting applications and automation platforms through APIs and webhooks. ‎For the MVP, we only need a basic working automation workflow. ‎More advanced workflows can be added in future phases. ‎ ‎Example Use Case ‎An online store could provide: ‎Product details ‎Shipping policy ‎Return policy ‎Refund information ‎Warranty details ‎Payment methods ‎Frequently asked questions ‎A customer could ask: ‎Do you offer refunds? ‎or: ‎How long does shipping take? ‎or: ‎Does this product have a warranty? ‎The RAG system should retrieve the relevant information and provide an answer. ‎Depending on the workflow, an automation could then: ‎Customer Interaction → AI Processing → n8n/Make/Zapier → External Service ‎For example, a customer request could trigger a notification or create a support task. ‎ ‎Preferred Tech Stack ‎We are open to recommendations, but our preferred stack is: ‎Frontend ‎React.js ‎Next.js ‎TypeScript ‎Backend ‎Node.js / Express.js or Python ‎REST APIs ‎AI ‎OpenAI API ‎OpenAI Embeddings ‎RAG ‎Prompt Engineering ‎AI Agents ‎Database / Search ‎PostgreSQL ‎Supabase ‎pgvector or another suitable vector database ‎Automation ‎n8n ‎Make ‎Zapier ‎The developer can recommend the best combination based on the requirements. ‎The main priority is having a clean, reliable, secure, and working MVP. ‎ ‎Responsibilities ‎The developer will be responsible for: ‎Building the frontend ‎Building the backend ‎Creating the admin dashboard ‎Building knowledge-base management ‎Implementing document/text processing ‎Implementing the RAG pipeline ‎Connecting the application with OpenAI ‎Building the customer chatbot ‎Implementing semantic search ‎Building a basic AI automation workflow ‎Connecting n8n, Make, or Zapier ‎Implementing API/webhook integrations ‎Connecting frontend and backend APIs ‎Handling loading and error states ‎Making the application responsive ‎Keeping API credentials secure on the backend ‎Testing the complete workflow ‎Fixing issues before delivery ‎Providing clean and organized source code ‎Providing basic setup/documentation instructions ‎ ‎Required Skills ‎We are looking for someone with experience in: ‎AI automation ‎AI agent development ‎Chatbot development ‎OpenAI API ‎RAG ‎n8n ‎Make ‎Zapier ‎API integrations ‎Webhooks ‎React.js ‎Next.js ‎Node.js ‎TypeScript / JavaScript ‎PostgreSQL ‎Vector databases ‎Semantic search ‎Full-stack development ‎ ‎Nice to Have ‎Experience with the following is a plus: ‎Supabase ‎pgvector ‎Pinecone ‎Qdrant ‎Weaviate ‎LangChain ‎LlamaIndex ‎OpenAI Embeddings ‎Streaming AI responses ‎Document processing ‎Knowledge-base applications ‎Customer-support chatbots ‎SaaS applications ‎CRM integrations ‎Email automation ‎Third-party API integrations ‎AI workflow automation ‎Multi-step AI agents ‎ ‎Project Requirements ‎Budget: $500 fixed price ‎Target completion: 7 days ‎Earlier delivery is preferred ‎Application must be functional and tested before delivery ‎Code should be clean and maintainable ‎API keys must remain secure on the backend ‎AI responses should be based on the business information provided ‎RAG workflow must be functional ‎At least one automation workflow must be functional ‎API/webhook integration should be properly tested ‎Complete workflow should be tested using sample business data ‎ ‎Scope of the MVP ‎The first version should remain focused on the agreed requirements. ‎The MVP should primarily include: ‎Admin dashboard ‎Business information management ‎Knowledge/document upload ‎Knowledge processing ‎RAG / semantic search ‎OpenAI integration ‎Customer chatbot ‎Basic AI automation ‎n8n, Make, or Zapier integration ‎API/webhook integration ‎Responsive UI ‎Testing and bug fixes ‎We are not looking for a large enterprise platform in the first version. ‎The priority is a clean, working MVP that can be expanded later. ‎ ‎Future Features ‎If the first version works well, we may add additional features in future phases, such as: ‎AI agents with multiple tools ‎Advanced workflow automation ‎User accounts ‎Multiple businesses ‎Multiple knowledge bases ‎Conversation history ‎Usage tracking ‎Subscription plans ‎Payment integration ‎Custom chatbot branding ‎Website chatbot widget ‎Website integration ‎CRM integrations ‎Email integrations ‎Human support handoff ‎Additional messaging integrations ‎Advanced analytics ‎Automated knowledge-base updates ‎More advanced n8n / Make / Zapier workflows ‎These features are not required for the initial MVP. ‎ ‎What We Expect From You ‎We are looking for someone who can: ‎Understand the requirements ‎Recommend the appropriate technical approach ‎Build the MVP independently ‎Communicate clearly ‎Write clean and maintainable code ‎Properly secure API keys and credentials ‎Test the complete workflow ‎Suggest practical improvements where appropriate ‎Deliver within the agreed timeline ‎Please do not over-engineer the MVP. ‎We value working functionality, clean implementation, reliability, and good communication. ‎ ‎Application Requirements ‎Please include: ‎Your recommended approach for building the RAG knowledge base. ‎Which automation platform you would recommend for this MVP n8n, Make, or Zapier and why. ‎Please include relevant portfolio examples rather than unrelated web-development projects. ‎ ‎