Leveraging artificial intelligence
AI chatbots in use
Analyze customer feedback, recommend products, improve service
AI chatbots: Give your data a voice
Our cloud-based AI chatbot helps companies reply to customer inquiries quickly and efficiently. Using the documents you provide, the chatbot establishes a comprehensive knowledge database. This allows it to support your customers in many areas anonymously or after they have logged in:
Analyze customer evaluations
The chatbot provides insights into customer evaluations and feedback that helps companies to improve their products and services on an ongoing basis.
Optimize customer support
Through interaction with product databases and technical documentation, the chatbot supports customers in finding the right products and solving problems quickly.
Reduce the burden on your customer service team
The chatbot replies to many customer inquiries on its own and frees employees up from time-consuming support activities. They can focus on their core tasks.
Just 7 steps to an AI chatbot: Let your data speak!
Step 1 – Preliminary meeting
Define the data that will be used for the knowledge database and the underlying technology (AWS or Azure)
Step 2 – Data provision
The customer must provide the data to be used as the knowledge base for the chatbot. The data must be anonymized by the customer to meet data protection requirements.
Step 3 – Anonymization
In addition to anonymization by the customer, Skaylink performs an anonymization step to remove personal information such as names and addresses.
Step 4 – Demo
Within 5 working days, Skaylink provides a demo version of the chatbot based on publicly available data. This demo chatbot has its own user interface.
Step 5 – Implementation
The standard implementation of the chatbot can be completed within 3–6 weeks. The most time-consuming part involves building the knowledge base, avoiding hallucinations (false statements), and ensuring ethics and contextual awareness.
Step 6 – Operation
The customer can choose whether they want to operate the chatbot or whether Skaylink should take over the operation.
Step 7 – Adjustments
Custom adjustments, like a customized user interface or integration into the website, are invoiced based on time and effort.
Data security & compliance
Your data is safe with us. Our chatbot works in compliance with the GDPR and ensures that your information is processed securely. Customer data stays in your possession, and we support you in anonymizing the data and using it securely. Through encryption, authentication, and regular security updates, we ensure the highest degree of protection against unauthorized access.
Data anonymization
The customer must anonymize the data that are used to build the knowledge base. Skaylink also performs an anonymization step to remove personal information such as names, addresses, etc.
Data security
The security standards typical of cloud computing apply to the Skaylink chatbot solution: encryption, authentication, backups, audit capability, etc.
Data ownership and use
The customer data remains the property of the customer. Skaylink does not use this data in any way. The customer can make their own decision about whether and how chats are saved and used.
Protection against incorrect information
The chatbot avoids replying if the underlying information is not contained in the knowledge base.
Compliance
The solution is designed to meet GDPR requirements.
Benefit title
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Case study: Using generative AI to improve customer loyalty
To strengthen customer loyalty, an e-commerce company used generative AI to analyze customer reviews. The company was able to respond better to the needs of its customers by gaining important insights and identifying trends. This approach increased customer satisfaction and customer loyalty, which in turn gave the company a competitive advantage.
- Automatically recognize key topics: AI quickly processes large volumes of evaluations and extracts important findings and trends.
- Structured analysis: The feedback is categorized into clear topics and helps to identify areas for improvement.
- Time-saving automation: Automating the analysis frees up resources for other strategic initiatives.
- Practicable findings: Clear recommendations enable data-supported decisions.
- Measurable effects: Retailers can track the effectiveness of measures and continuously improve what they have on offer.
Technologies used:
AWS Bedrock, AWS Aurora, AWS Quick Sight, AWS Lambda, AWS S3, AWS Step Functions, and AWS Lightsail
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