AI Automation for Customer Support
Budget: ₹600 – ₹1,500 INR
I want to deploy an AI-driven solution that reliably takes over much of our day-to-day customer service and support workload. The core objective is task automation: the system should understand incoming queries, draft or deliver accurate responses, and escalate to a human only when needed, all while keeping response times low and customer satisfaction high.
Here’s what I need from you:
• End-to-end design and build of the automation workflow, including model selection, training, and integration with our existing help-desk or live-chat platform
• A user-friendly dashboard or reporting layer that lets my team monitor conversations, review escalations, and fine-tune the model as our knowledge base changes
• Clear documentation covering setup, maintenance, and hand-off processes
Acceptance criteria:
1. At least 80 % of routine support tickets are handled end-to-end by the AI with no human intervention.
2. Average customer response time is cut by 60 % or more compared with our current baseline.
3. All data privacy and security requirements for our industry are met, with logs and transcripts stored in an auditable manner.
I’m open to your recommendations on the exact natural-language tools, frameworks, and hosting stack you prefer—whether that’s Dialogflow, Rasa, Llama 2, or a custom transformer setup—so long as the final solution is scalable and maintainable. Let me know how you would approach the project, what past work shows you can hit these benchmarks, and an estimated timeline to a working pilot.
Here’s what I need from you:
• End-to-end design and build of the automation workflow, including model selection, training, and integration with our existing help-desk or live-chat platform
• A user-friendly dashboard or reporting layer that lets my team monitor conversations, review escalations, and fine-tune the model as our knowledge base changes
• Clear documentation covering setup, maintenance, and hand-off processes
Acceptance criteria:
1. At least 80 % of routine support tickets are handled end-to-end by the AI with no human intervention.
2. Average customer response time is cut by 60 % or more compared with our current baseline.
3. All data privacy and security requirements for our industry are met, with logs and transcripts stored in an auditable manner.
I’m open to your recommendations on the exact natural-language tools, frameworks, and hosting stack you prefer—whether that’s Dialogflow, Rasa, Llama 2, or a custom transformer setup—so long as the final solution is scalable and maintainable. Let me know how you would approach the project, what past work shows you can hit these benchmarks, and an estimated timeline to a working pilot.