Case Study
Automated Appointment Scheduler
Automated Appointment Scheduler
Services
Tools
Langchain - Python - Google Cloud - Docker - Langserve - NextJS
Highlights
Reduce/remove need for man hours spent on customer service
The Opportunity
Countless companies in various industries across the world deal with high volumes of customer interactions. Whether these are direct requests for customer support, appointment scheduling, sales, general Q&A, and much more, these customer interactions take time and cost money. The typical customer support spend for a business is between 10 and 15% of total salary spend. What if you could erase that expenditure?
The Opportunity
Countless companies in various industries across the world deal with high volumes of customer interactions. Whether these are direct requests for customer support, appointment scheduling, sales, general Q&A, and much more, these customer interactions take time and cost money. The typical customer support spend for a business is between 10 and 15% of total salary spend. What if you could erase that expenditure?
The Opportunity
Countless companies in various industries across the world deal with high volumes of customer interactions. Whether these are direct requests for customer support, appointment scheduling, sales, general Q&A, and much more, these customer interactions take time and cost money. The typical customer support spend for a business is between 10 and 15% of total salary spend. What if you could erase that expenditure?
The Solution
We developed an automated customer support agent that is capable of natural and fluid conversation. It has an arbitrarily long memory and is capable of recalling details from any point in the conversation. The agent can also do things like reference your company's documentation to answer questions accurately, hook into any existing APIs to take actions like appointment booking/cancellation, and even recognize user frustration and forward the call to a human. This is all possible thanks to the recent cutting edge advancements in Large Language Model tech!
The Solution
We developed an automated customer support agent that is capable of natural and fluid conversation. It has an arbitrarily long memory and is capable of recalling details from any point in the conversation. The agent can also do things like reference your company's documentation to answer questions accurately, hook into any existing APIs to take actions like appointment booking/cancellation, and even recognize user frustration and forward the call to a human. This is all possible thanks to the recent cutting edge advancements in Large Language Model tech!
The Solution
We developed an automated customer support agent that is capable of natural and fluid conversation. It has an arbitrarily long memory and is capable of recalling details from any point in the conversation. The agent can also do things like reference your company's documentation to answer questions accurately, hook into any existing APIs to take actions like appointment booking/cancellation, and even recognize user frustration and forward the call to a human. This is all possible thanks to the recent cutting edge advancements in Large Language Model tech!
5000 hours of AI knowledge below:
Copyright 2024, PressW, LLC
5000 hours of AI knowledge below:
Copyright 2024, PressW, LLC