AI-driven chatbot solutions’ strategy and implementation for your business
AI chatbots are worth considering for your business if:
- You have been using rule-based chatbots and your analysts are overwhelmed with maintenance of thousands of rules and complicated choices
- You are in the insurance, banking, e-commerce business and would like to automate customer service support
- You have thousands of employees and would like to improve/automate human resource services and communication with your employees
- Your business is focused on younger audiences and you need to provide self-service solution available 24/7 for managing their assets (accounts, orders, etc.)
Our experts will suggest the most efficient implementation strategy for your business based on your:
- Business priorities
- Software/hardware/data available
- Policies & security constrains
- Target users
We can actively assist you in definition and implementation of such strategy after having discovery session(s) with your key stakeholders.
Key benefits of AI-powered chatbot solutions:
Approach to delivering conversational AI chatbots
Discovery
– Meet stakeholders, confirm business priorities
– Evaluate available resources, policies, user audiences
– Define target KPIs
Solution design
– Perform initial data analysis
– Design principal architecture and key AI/NLP technologies
– Define version 1 scope, plan milestones, costs, and duration
– Plan production delivery and maintenance for version 1
– Approve the entire roadmap with the customer
First version implementation
– Develop essential flows/cases to get to market quickly
– Collect feedback and store conversational data
– Implement efficient user interfaces for desired communication channels
Next iterations
– Implement continuous training
– Develop new flows/intents
– Adjust the solution based on user feedback from previous releases
– Begin planning and implementation of a contextual AI-based chatbot (human-like)
Conversational AI solutions
Conversational AI-driven chatbot for multi billion European fintech company
Our AI/NLP developers have delivered a conversational AI chatbot to automate customer service routine. It uses natural language processing (NLP) and deep learning techniques to recognize customers’ intent behind text inquiries. The solution can take a short conversation and do an upsale. German and English languages are supported. The customer service chatbot is able to display the user profile and updated balance within a chat interface.
Business value of implementing conversational AI chatbot:
- Reduced workload of the support staff through streamlined routine conversations
- Swiftly resolves common online banking requests
- 24/7 availability shortens routine inquiry resolution time
- Drives upsells and collects service satisfaction data
AI chatbots – stress off, efficiency on
Contact usAI chatbot boosting customer engagement for JustAnswer
With more than 1 mln users every day seeking for expert’s help on the platform, JustAnswer is always looking for ways to improve and augment their services.Having a chatbot as the first touchpoint on the platform helped our partner provide better, more personalized service to their customers.The virtual assistive technology has been in beta testing for three years being trained on 16 million questions and answers in the company’s database.
Business value of implementing AI-driven assistant:
- Increased customer engagement and automated essential information gathering
- Accelerated lead qualification and processing, improved expert efficiency
- Boosted conversion rates with personalized service, recognizing over 100,000 conversation variables
- Drive premium service adoption, as users engaging with the chatbot are more likely to subscribe
AI expertise
Choosing the right technologies and languages for chatbot implementation is crucial for meeting short- and long-term business goals. Consider factors like exporting training data, configuring rules, and transitioning to a more human-like contextual solution, etc.
- Data mining
- Predictive analytics
- Recommender systems
- Time series analysis
- Digital signal processing
- Probabilistic programming
- Bayesian models
- Advanced statistics
- Data visualization
- Neural networks
- Deep learning
- Autoencoders
- Generative models
- Transfer learning
- Self-supervised/Semi-supervised techniques
- Unsupervised learning
- Clustering
- AutoML
- Gradient boosting
- Decision trees and random forests
- TFIDF
- Word embeddings
- RASA
- Tensorflow/Keras
- NumPy & SciPy
- Scikit-learn
- Apache Spark (MLlib)
- Caffe
- OpenCV
- AWS machine learning
- Matlab
- Data mining
- Predictive analytics
- Recommender systems
- Time series analysis
- Digital signal processing
- Probabilistic programming
- Bayesian models
- Advanced statistics
- Data visualization
- Neural networks
- Deep learning
- Autoencoders
- Generative models
- Transfer learning
- Self-supervised/Semi-supervised techniques
- Unsupervised learning
- Clustering
- AutoML
- Gradient boosting
- Decision trees and random forests
- TFIDF
- Word embeddings
- RASA
- Tensorflow/Keras
- NumPy & SciPy
- Scikit-learn
- Apache Spark (MLlib)
- Caffe
- OpenCV
- AWS machine learning
- Matlab