E-commerce AI Agent Development
Budget: $15 – $25 USD
I’m ready to build an AI agent from the ground up to enhance my ecommerce operation and I need it written in Python with Retrieval-Augmented Generation, LangChain orchestration, and custom fine-tuning baked in. Because of time-zone overlap and regulatory considerations, I can only proceed with developers based in the United States, Canada, or Australia.
Scope
My store is already live; what I need now is a partially integrated agent that can sit beside the existing stack, communicate through well-defined APIs, and gradually assume more responsibilities as we prove its value. You will start from a clean slate—no legacy models or datasets are waiting for you—so architecture decisions, data pipeline setup, and model training are yours to drive.
Key expectations
• Design and code a RAG-powered pipeline in Python, orchestrated with LangChain
• Implement domain-specific fine-tuning so the agent understands my products, policies, and brand voice
• Deliver a clear interface layer (REST or GraphQL) for partial plugin-style integration with the current ecommerce backend
• Supply concise setup and deployment documentation so I can replicate the environment in staging and production
Acceptance
The project is complete when the agent can answer live product questions with ≥90 % factual accuracy against a test set I provide and the interface layer can be called from my platform without manual workarounds. Source code, trained weights (or checkpoints), environment files, and usage documentation must be handed over.
If this lines up with your skill set and you meet the location requirement, let’s talk timelines and next steps right away.
Scope
My store is already live; what I need now is a partially integrated agent that can sit beside the existing stack, communicate through well-defined APIs, and gradually assume more responsibilities as we prove its value. You will start from a clean slate—no legacy models or datasets are waiting for you—so architecture decisions, data pipeline setup, and model training are yours to drive.
Key expectations
• Design and code a RAG-powered pipeline in Python, orchestrated with LangChain
• Implement domain-specific fine-tuning so the agent understands my products, policies, and brand voice
• Deliver a clear interface layer (REST or GraphQL) for partial plugin-style integration with the current ecommerce backend
• Supply concise setup and deployment documentation so I can replicate the environment in staging and production
Acceptance
The project is complete when the agent can answer live product questions with ≥90 % factual accuracy against a test set I provide and the interface layer can be called from my platform without manual workarounds. Source code, trained weights (or checkpoints), environment files, and usage documentation must be handed over.
If this lines up with your skill set and you meet the location requirement, let’s talk timelines and next steps right away.