AI-driven chatbot solutions’ strategy and implementation for your business

Empower communication with your customers using AI-driven chatbots with continuous learning
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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.)
AI chatbots are worth considering for your business if:

Abto Software chatbot 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 Abto’s AI-powered chatbot solutions:

1
Continuous (self) learning by analysis of data collected from recent live conversations
2
Setup/amendment of conversation flows via chatbot admin page without developer involvement
3
Centralized AI/NLP engine accessible via API for intent recognition, NLP & chatbot interaction
4
Unlimited intent recognition for human-like chatbots
5
Flexible setup (cloud/on-premise), integrates with voicebot/ERP/CRM, highly extensible and scalable

Approach to delivering conversational AI chatbots

Discovery

Discovery

Meet stakeholders, confirm business priorities
Evaluate available resources, policies, user audiences
Define target KPIs

Solution design

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

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

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

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Conversational design practices. Abto Software are experts in chatbot design for seamless customer interaction through messaging. Elevate your chatbot experience with our technology.
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Smart chatbots enhanced by LP. We integrate AI and machine learning for advanced chatbot capabilities, enabling understanding of concepts, sentiment, and optimal customer service.
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Seamless integration across systems. Our chatbots seamlessly integrate with key digital channels, customer service desks, CRM systems like Salesforce, and other vital applications.

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
Read more
Conversational AI-driven chatbot for multi billion European fintech company

Have a use case in mind?

Let’s talk to one of our chatbot experts.

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AI 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
Read more

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 Science
  • Data mining
  • Predictive analytics
  • Recommender systems
  • Time series analysis
  • Digital signal processing
  • Probabilistic programming
  • Bayesian models
  • Advanced statistics
  • Data visualization
Machine Learning
  • 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
Natural Language Processing
  • TFIDF
  • Word embeddings
  • RASA
Tools and frameworks
  • 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