Python NLP Customer Support Chatbot , face recognise system and ai management system .
Budget: $15 – $25 USD
I need a production-ready customer support chatbot that can understand and respond to user queries in natural language, all powered by Python. The end goal is to off-load routine customer questions, triage more complex issues, and hand off seamlessly to a live agent when required.
What I already have
• A clear set of FAQ-style dialogues and real chat logs for training
• Access credentials for the support ticketing API and live-agent handover webhook
• An AWS account (preferred host), though I am open to GCP or Azure if your tool-chain demands it
What I need from you
1. A conversational NLP model—transformer-based or fine-tuned LLM—that can detect intent, extract entities, and keep short-term context across turns
2. A dialogue manager written in Python (FastAPI or Flask) that routes intents, fires API calls, and logs interactions
3. Integration hooks for my existing ticketing system so unresolved queries are escalated automatically
4. Deployment scripts (Docker and CI/CD) plus concise readme so I can reproduce the environment in one command
Acceptance criteria
• ≥90 % intent classification accuracy on my held-out test set
• Average response latency ≤1 s under a 50-concurrent-user load test
• All source code, models, and documentation delivered via private Git repo
If you have previous chatbot development experience—particularly in customer support—let’s talk specifics.
What I already have
• A clear set of FAQ-style dialogues and real chat logs for training
• Access credentials for the support ticketing API and live-agent handover webhook
• An AWS account (preferred host), though I am open to GCP or Azure if your tool-chain demands it
What I need from you
1. A conversational NLP model—transformer-based or fine-tuned LLM—that can detect intent, extract entities, and keep short-term context across turns
2. A dialogue manager written in Python (FastAPI or Flask) that routes intents, fires API calls, and logs interactions
3. Integration hooks for my existing ticketing system so unresolved queries are escalated automatically
4. Deployment scripts (Docker and CI/CD) plus concise readme so I can reproduce the environment in one command
Acceptance criteria
• ≥90 % intent classification accuracy on my held-out test set
• Average response latency ≤1 s under a 50-concurrent-user load test
• All source code, models, and documentation delivered via private Git repo
If you have previous chatbot development experience—particularly in customer support—let’s talk specifics.