Article Series: “100 LLM Applications that have earned over $500,000”
Handling customer support tickets can be a complicated and time-consuming process. Support agents often find themselves faced with the task of dealing with partial information, having to navigate through multiple windows to gain context, guidelines, and craft responses.
Note.The series of articles “100 LLM Applications that have earned over $500,000” is based on the contents of the book “100 IA Startups“. The book includes the identifying data of each of these real cases.
The Artificial Intelligence Agent of ACME is promising to revolutionize the customer service sector by enabling employees to resolve complex issues in half the time.
Only six weeks since its launch, it has already been adopted by a dozen mid-sized companies, making a noticeable difference in thousands of support tickets each week.
Problem.
Handling customer service tickets can be a complicated, time-consuming process.
Support agents often find themselves faced with the task of dealing with partial information, having to navigate multiple windows to gain context, guidelines, and craft responses.
This not only affects the efficiency and accuracy of support operations but also the job satisfaction of the team.
Solution.
The Artificial Intelligence Agent of ACME’s proposal focuses on enabling support teams to resolve complex requests in record times.
Through deep integration with Zendesk, their platform has won the appreciation of both agents and support leaders.
Besides offering high-quality pre-written answers and actionable recommendations, the platform syncs with a wide variety of data sources like Zendesk Guide, Confluence, Notion, and Slack, ensuring up-to-date solutions and answers without the usual cumbersome maintenance.
Their commitment to security and adaptability is also evident, offering features such as redacting personally identifiable information and adhering to SOC II regulations.
Key functionalities of the Artificial Intelligence Agent of ACME include:
– Empowers customer service agents by providing context, cross-references from content sources, and preliminary answers. Agents receive instant summaries of the current request, recent interactions with the requester, and suggested macros, tags, and reference materials.
– Provides instant answers to internal teams. Enables internal teams across your company to get instant answers and references to their product support questions.
– Chat Assistance. Delight your customers with access to accurate and instant answers to their questions. Escalate issues to your human agents when necessary.
– Seamlessly supports multi-brand, multi-product, and multi-language teams.
– Recommends and uses macro answers as a starting point to write preliminary personalized responses to customers.
The identifying data of this real case are included in the book “100 IA Startups“. For each of the 100 startups analyzed, it details:
- Problem solved.
- Proposed solution.
- Technical analysis.
- Identifying data of the company, its website, and its founders.
The startup presented in the article uses an LLM Application to solve the described problem. If you are interested in learning more about LLM Applications or Artificial Intelligence applied to the company, here are some interesting links:
- Courses and Bootcamps to learn to create LLM Applications.
- Development of LLM Applications for your company.
- Development of LLM Applications for a new startup.
- Artificial Intelligence consulting for businesses.
- In-Company custom Artificial Intelligence training.
- Selection of Artificial Intelligence professionals, LLM Applications, Machine Learning, and Data Science for businesses.
- Experimental projects of LLM Applications: contact between entrepreneurs and junior developers.
- We are looking for entrepreneurs interested in creating startups based on LLM Applications. We provide technical assistance and monitoring.
- We are looking for collaborators: Do you work in a training center, a consultancy/advisory service, or in a support center for entrepreneurs? We are interested in talking to you.