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Use Cases

How can your Company benefit from LLMs?

LLMs have six key capabilities: Generation, Extraction, Summarization, Rewriting, Classification, and Question Answering. Discover through the following examples how these capabilities can be implemented in your business.

Content Generation

"EcoGifts" sells eco-friendly and sustainable products. With a broad range of items and a diverse customer base, they find it difficult to create enough compelling and personalized content to keep customers engaged. This requires a lot of time and resources.

Data Upload & LLM Selection:

EcoGifts has collected customer data for years, which includes browsing habits, past purchases, product reviews, and customer feedback. They upload this anonymized data to the AI Playground and select an LLM that excels in Generation tasks.

Querying:

The team sets up queries based on their needs. For example, they could ask the AI to generate product descriptions, personalized emails, blog posts on sustainability topics, or social media posts. Here are some examples:

  • "Generate a product description for a bamboo toothbrush."
  • "Write a personalized email to a customer who frequently purchases our recycled paper products."
  • "Create a blog post on the importance of using eco-friendly products."
  • "Compose a Tweet promoting our new range of organic cotton clothing."

Outcome:

The LLM produces engaging and diverse content that the company can use in its customer outreach, saving time and resources. Moreover, since the content is informed by the company's customer data, it will be customized to the specific interests and preferences of their customers, making it more effective in driving engagement and sales.

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Extracting Insights from Customer Reviews

"TechFix" provides technical support and IT solutions. Over the years, it has accumulated a vast amount of customer feedback and reviews. The company recognizes that this data likely contains invaluable insights about their service, but manually processing all of it to extract relevant information would be extremely time-consuming and inefficient.

Data Upload & LLM Selection:

TechFix has thousands of customer reviews and feedback messages saved in their databases. They upload this anonymized data into the AI Playground and select an LLM that is especially skilled in Extraction tasks.

Querying:

The team sets up queries to extract insights about their services. These might involve identifying common complaints, pinpointing popular services, or understanding which aspects customers value most.

Here are some example queries:

  • "Identify the most common issues mentioned in negative reviews."
  • "List the services praised most frequently in positive reviews."
  • "Extract the reasons customers choose our services over competitors."
  • "Summarize the characteristics of reviews that mention our customer service."

Outcome:

The LLM extracts crucial information from the vast pool of customer reviews, providing TechFix with a clearer understanding of their customers' needs and preferences. For example, it might reveal that customers frequently complain about slow response times, praise TechFix's comprehensive solutions, or choose TechFix because of its experienced technicians.

Armed with these insights, TechFix can make more informed decisions to improve its services and better meet its customers' expectations. This may involve adjusting training programs, restructuring services, or launching marketing campaigns that highlight strengths.

Summarizing Market Research Reports

"BloomCosmetics" designs and sells natural beauty and skincare products. The beauty industry is rapidly changing, with new trends, products, and competitors emerging constantly. To stay competitive, BloomCosmetics conducts regular market research, but their team is overwhelmed by the amount of data and has difficulty summarizing and extracting the key points from these extensive reports.

Data Upload & LLM Selection:

BloomCosmetics has a collection of detailed market research reports covering various aspects of the beauty industry, including consumer trends, competitor analysis, product innovations, and more. They upload their market research reports into the AI Playground.
They select an LLM that excels in Summarization tasks.

Querying:

The team sets up queries to summarize the reports, with a focus on extracting key insights that can inform their product development and marketing strategies.
Example Queries:

  • "Summarize the main findings of the Q1 2023 skincare market trends report."
  • "What are the key points in the competitor analysis report for natural beauty brands?"
  • "Summarize the consumer behaviour report regarding preference for organic cosmetics."
  • "What are the main insights from the new product innovations report in the beauty industry?"

Outcome:

The LLM generates concise and clear summaries of the complex reports, allowing the BloomCosmetics team to quickly understand the main findings and implications. This helps them to make timely and informed decisions in response to market trends and changes, such as developing new products, adjusting their marketing strategies, or repositioning their brand.

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Rewriting Technical Manuals into Layman Terms

"DroneFly," designs, manufactures, and sells drones for both hobbyists and professional use. Many of DroneFly's customers are hobbyists with little to no technical background. Although the company provides detailed technical manuals with their products, many customers struggle to understand them, leading to frequent customer service inquiries and potential misuse of the products.

Data Upload & LLM Selection:

DroneFly has an extensive collection of technical manuals and documentation for their range of drones. They upload their technical manuals into the AI Playground.
They select an LLM that excels in Rewriting tasks.

Querying:

The team sets up queries to rewrite the technical manuals into simpler, more accessible language. The goal is to maintain the accuracy and integrity of the information while making it easier for non-technical customers to understand.
Example Queries:

  • "Rewrite the setup instructions for the Falcon X drone in layman's terms."
  • "Simplify the technical specifications section of the Eagle Eye drone manual."
  • "Rewrite the troubleshooting guide for the Hawk Pro drone for a non-technical audience."
  • "Simplify the software update instructions for the drone control app."

Outcome:

The LLM rewrites the technical manuals, turning complex jargon and technical terms into easy-to-understand instructions and explanations. The rewritten manuals are then provided to customers, helping them to set up, use, and troubleshoot their drones without the need for technical expertise.
This significantly reduces the number of customer service inquiries and improves overall customer satisfaction. In turn, this helps to build a stronger brand reputation for DroneFly, as they are seen as a company that prioritizes user-friendliness and customer support.

Classifying Customer Support Tickets

"Safeguard Security" provides home security solutions, including alarms, surveillance cameras, and smart home integration. Safeguard Security receives hundreds of customer support tickets every day, covering a variety of issues. Manually sorting and prioritizing these tickets takes significant time and can delay response times.
Data: Safeguard Security has a vast repository of customer support tickets, including information on the issue reported, the customer's details, the product involved, and any additional comments from the customer.

Data Upload & LLM Selection:

Safeguard Security has a vast repository of customer support tickets, including information on the issue reported, the customer's details, the product involved, and any additional comments from the customer. They upload their anonymized customer support tickets into the AI Playground.
They select an LLM that excels in Classification tasks.

Querying:

The team sets up queries to classify the tickets based on the type of issue reported. For instance, they might classify tickets into categories like "Technical Issues," "Billing Queries," "Product Inquiries," and "General Feedback."
Example Queries:

  • "Classify this customer support ticket into one of our issue categories."
  • "Identify the category of this support ticket based on the customer's problem description."
  • "Sort these customer inquiries by their issue type."

Outcome:

The LLM classifies incoming customer support tickets in real-time, allowing Safeguard Security to rapidly sort and prioritize their response. Critical issues can be addressed promptly, while less urgent inquiries are still directed to the appropriate team members for resolution.

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Automated Customer Support

"TravelEase" an online platform for booking flights, hotels, and tours. TravelEase receives a large volume of customer inquiries daily via email, phone, and their website's chat system. Employing enough customer service agents to promptly answer all these queries is expensive and logistically challenging.

Data Upload & LLM Selection:

TravelEase has a comprehensive FAQ section and a large database of past customer inquiries and their corresponding responses. TravelEase uploads their FAQs and the database of past customer inquiries and responses into the AI Playground.
They select an LLM that excels in Question Answering tasks.

Querying:

The AI is integrated into their customer service system to automatically respond to customer inquiries using the knowledge it has gleaned from the FAQs and past inquiries.
Example Queries:

  • "What is the baggage allowance for this flight?"
  • "How do I change my hotel reservation?"
  • "Can I get a refund for my tour booking if I cancel?"
  • "Are there any special deals for honeymoon trips?"

Outcome:

The LLM provides accurate, instant responses to a wide range of customer inquiries, freeing up human agents to handle more complex or unique cases. This greatly improves the efficiency of TravelEase's customer service, increases customer satisfaction due to reduced response times, and ultimately saves the company significant costs associated with staffing a large customer service team.