DevicoAI

TECHNOLOGY

Intelligent Automation

Be the cool kid in class and use Conversational AI to create personalised interactions, automate and standardise customer service, and scale your business without sacrificing quality.

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What is Conversational AI?

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Conversational AI is a field within artificial intelligence (AI) that enables machines to understand, process, and respond to human language in a natural and engaging manner. By mimicking human conversation, Conversational AI can assist customers through chatbots, voice assistants, and other interactive interfaces, providing a seamless and efficient user experience.

Proof of the pudding: According to Gartner, by 2025, customer service organisations that embed AI in their multichannel customer engagement platforms will elevate operational efficiency by 25%.

Computer vision development process

01

Data collection

Gathering relevant data from various sources.

Data collection

02

Data preparation

Cleaning and organizing data to make it suitable for analysis.

Data preparation

03

Model training

Using algorithms to train a model on the prepared data.

Model training

04

Model evaluation

Assessing the model's performance to ensure it meets the desired criteria.

Model evaluation

05

Model deployment

Implementing the model in a real-world environment.

Model deployment

06

Monitoring and maintenance

Continuously tracking the model's performance and updating it as necessary.

Monitoring and maintenance

How businesses are using
Conversational AI

From enhancing customer engagement to automating support tasks, Conversational AI is transforming various industries and allowing them to scale without adding cost or compromising on service promises.

Healthcare

Conversational AI can assist patients with scheduling appointments, providing medical information, and offering mental health support. It significantly improves patient engagement and accessibility to healthcare services.

Case Study: Nuance uses Conversational AI to offer 24/7 medical advice through a chatbot, helping their customers receive timely information and support.

Use cases:

  • Patient triage and symptom checking.
  • Appointment scheduling and reminders.
  • Mental health support through chatbots.
  • Providing medication information and adherence reminders.
Finance

Financial institutions use Conversational AI to provide customer support, assist with transactions, and offer personalised financial advice. This technology helps in improving customer experience and operational efficiency.

Case Study: Bank of America’s Erica is a virtual assistant that helps customers with their banking needs, from checking account balances to making transactions and providing financial tips.

Use cases:

  • Customer service through virtual assistants.
  • Automated handling of common banking transactions.
  • Personalised financial advice and planning.
  • Fraud detection and alerts through conversational interfaces.
Retail

Retailers leverage Conversational AI for customer support, personalised shopping experiences, and handling inquiries about products and services. This technology enhances customer engagement and drives sales.

Case Study: H&M uses a Conversational AI chatbot on their website and mobile app to assist customers with product searches, order tracking, and personalised fashion recommendations.

Use cases:

  • Virtual shopping assistants for product recommendations.
  • Handling customer inquiries and support requests.
  • Order tracking and status updates.
  • Personalised marketing and promotions.
Manufacturing

In manufacturing, Conversational AI assists with internal communication, troubleshooting equipment issues, and providing training resources. It ensures efficient operations and improved worker productivity.

Case Study: Siemens uses Conversational AI to provide real-time support to technicians on the factory floor, offering troubleshooting tips and maintenance advice.

Use cases:

  • Real-time support for equipment troubleshooting.
  • Automated internal helpdesk for employee queries.
  • Providing training and safety information.
  • Streamlining communication across teams.

The Core Capabilities of
Conversational AI

Natural Language Processing

Understanding and processing human language to provide meaningful responses. It is crucial for interpreting user inputs accurately.

Practical Use Cases:

01

Parsing customer queries to provide relevant answers.

02

Translating languages in real-time for global support.

03

Extracting key information from conversations for analysis.

04

Enabling multi-turn conversations for complex interactions.

Natural Language Processing

Speech Recognition

Converting spoken language into text. It is essential for voice-activated systems and virtual assistants.

Practical Use Cases:

01

Transcribing customer calls for analysis.

02

Enabling voice commands in mobile applications.

03

Assisting with dictation and note-taking.

04

Enhancing accessibility for visually impaired users.

Speech Recognition

Text-to-Speech

Converting text into spoken language. It provides a voice to AI systems, making interactions more natural.

Practical Use Cases:

01

Reading out information to users in a conversational manner.

02

Providing verbal assistance in navigation systems.

03

Offering auditory alerts and notifications.

04

Enhancing customer service with voice responses.

Text-to-Speech

Dialog Management

Managing the flow of conversation to ensure coherent and contextually relevant interactions.

Practical Use Cases:

01

Handling multi-turn conversations smoothly.

02

Maintaining context across different interactions.

03

Escalating to human agents when necessary.

04

Personalising conversations based on user history.

Dialog Management

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Advanced Conversational AI Techniques

The table below dives deeper into advanced Conversational AI techniques. These techniques require significant computational resources and expertise for implementation.

Criteria

Transformer Models

Generative Models

Transfer Learning

Definition

Models that use attention mechanisms to understand context and improve language understanding.

Models that generate human-like text based on input data.

Utilising a pre-trained language model on a new, related problem.

Goal

Improve understanding and response accuracy by focusing on context.

Create coherent and contextually appropriate responses.

Leverage existing models to reduce training time and improve performance on new tasks.

Algorithms

Attention layers, encoder-decoder architectures.

GPT-3, BERT.

Fine-tuning pre-trained models, domain adaptation.

Data Requirement

Requires large amounts of conversational data.

Requires substantial data to generate meaningful text.

Requires less data than training a model from scratch, using pre-trained models.

Advantages

High accuracy in understanding context, ability to manage long conversations.

Capable of generating high-quality, human-like text.

Significantly reduces training time and resources, improves performance with less data.

Applications

Customer support chatbots, virtual assistants, automated transcription services.

Content creation, automated report generation, conversational agents.

Custom chatbots, virtual assistants, sentiment analysis.

Techniques

Self-attention, transformer networks, BERT.

Generative pre-trained transformers (GPT-3), fine-tuning on specific tasks.

Model fine-tuning, transfer learning architectures like GPT, BERT.

How to choose the right AI platform for
Conversational AI

When selecting an AI platform for Conversational AI, consider the following aspects to ensure it meets your specific needs:

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Data Storage and Management: Ensure the platform can handle large volumes of conversational data efficiently. Examples: Google Cloud Storage, AWS S3.

Data Annotation Tools: Look for integrated tools for labelling and annotating conversational data. Examples: Labelbox, Amazon SageMaker Ground Truth.

Pre-trained Models: Check if the platform provides access to pre-trained language models to accelerate development. Examples: TensorFlow Hub, PyTorch Hub.

Custom Model Training: Ensure the platform supports custom model training for specific use cases. Examples: Google AI Platform, Azure machine learning.

Scalability: The platform should scale computational resources dynamically to handle intensive training tasks. Examples: Google Kubernetes Engine (GKE), Amazon EC2.

Model Deployment: Look for seamless deployment capabilities to integrate models into your existing systems. Examples: AWS SageMaker, TensorFlow Serving.

Edge Deployment: If your application requires edge computing, ensure the platform supports deploying models on edge devices. Examples: AWS IoT Greengrass, Google Cloud IoT.

Real-time Monitoring: The platform should offer tools to monitor model performance and accuracy in real-time. Examples: Azure Monitor, Google Stackdriver.

Model Updating and Retraining: Ensure the platform supports continuous integration and deployment (CI/CD) pipelines for updating and retraining models. Examples: Jenkins, GitLab CI/CD.

Data Security: The platform should comply with industry standards for data security and privacy. Examples: AWS Shield, Google Cloud Security.

Compliance: Ensure the platform adheres to regulatory requirements relevant to your industry. Examples: GDPR compliance, HIPAA compliance.

Technical Support: Look for platforms that offer robust technical support and documentation. Examples: AWS Support, Google Cloud Support.

Community and Ecosystem: A strong user community and ecosystem can provide valuable resources and third-party integrations. Examples: TensorFlow Community, PyTorch Community.

Top Trends Shaping the Future
of Conversational AI

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Advanced Natural Language Understanding

Enhanced NLU capabilities allow AI to understand context, sentiment, and intent more accurately, leading to more meaningful and engaging interactions.

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OpenAI's GPT-4 model showcases advanced NLU by generating human-like text and understanding context, making it useful for a wide range of applications from customer support to content creation.

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Multilingual and Cross-Lingual Models

Conversational AI systems are becoming more adept at handling multiple languages and even translating between them in real-time.

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Google Translate’s integration with Google Assistant allows for real-time translation and conversation across different languages, breaking down language barriers.

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Emotionally Intelligent AI

AI systems are being developed to detect and respond to human emotions, enhancing the user experience by providing empathetic and contextually appropriate responses.

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Affectiva uses Conversational AI to analyse facial expressions and vocal tones, allowing brands to measure and improve customer satisfaction in real-time.

Questions & answers

With a market-tested approach DevicoAI combines AI expertise with a practical approach to develop custom solutions that help your company drive competitive advantage by delivering seamless and personalized customer experience.

The cost depends on the complexity of the project, the technologies involved, and the scope of the solution. We offer tailored pricing based on your needs and goals to ensure you get the best value for your investment.

Conversational AI requires accurate data to work correctly and give expected results. This data comes from texts, numbers, images, and videos.The amount of data required depends on the specific project and chosen algorithms. We can help you assess your data readiness and explore strategies for maximising its value.

Chatbots use keywords and other language identifiers to trigger pre-written responses, while conversational AI uses machine learning to mimic human interactions and conversational flow.

Yes, we work closely with your internal teams to integrate conversational AI into your existing infrastructure and ensure that the transition is smooth and efficient.

To begin, we need an understanding of your business objectives, access to relevant data, and any system specifications necessary for integration.

Yes, we specialise in integrating conversational AI solutions with existing systems to enhance their functionality and performance.

The timeline depends on the complexity and scope of the project. On average, custom conversational AI can take anywhere from a few weeks to several months.

Yes, we provide ongoing support and maintenance to ensure that the conversational AI solution continues to meet your business needs and adapts to any new challenges.

We implement industry-standard data security protocols, including encryption, secure access controls, and compliance with relevant data privacy regulations such as GDPR.

We use a variety of ML algorithms like linear regression, logistic regression, clustering, decision trees, random forest.

We follow strict compliance protocols throughout the ML process, including data anonymisation, secure storage, and regular audits to maintain compliance with industry standards and regulatory requirements.

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