Conversational AI company
Conversational AI developing services
Create personalised interactions, automate and scale your business without sacrificing quality.
What is
Conversational AI?
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, сonversational AI can assist customers through chatbots, voice assistants, and other interactive interfaces, providing a seamless and efficient user experience.
Why choose DevicoAI for
conversational AI
development services?
High retention rate
96%
Our dedicated team ensures consistent support and expertise, significantly above the industry average of 80%.
Wide expert network
3000
Access to over 3000 engineers and AI experts.
Proven track record
500,000
Over 500,000 man-days successfully delivered.
Support
24/7
Highly experienced management team available around the clock.
Scale up with AI
Transform your business with conversational AI-powered solutions
Conversational AI development process
01
Data collection
Gathering relevant data from various sources.
02
Data preparation
Cleaning and organizing data to make it suitable for analysis.
03
Model training
Using algorithms to train a model on the prepared data.
04
Model evaluation
Assessing the model's performance using metrics like accuracy, precision, and recall to ensure it meets the desired criteria.
05
Model deployment
Implementing the model in a real-world environment to generate insights or automate decisions.
06
Monitoring and maintenance
Continuously tracking the model's performance and updating it as necessary to maintain its accuracy and relevance.
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.
Use cases:
- Patient triage and symptom checking.
- Appointment scheduling and reminders.
- Mental health support through chatbots.
- Providing medication information and adherence reminders.
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.
Use cases:
- Patient triage and symptom checking.
- Appointment scheduling and reminders.
- Mental health support through chatbots.
- Providing medication information and adherence reminders.
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.
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.
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.
Use cases:
- Virtual shopping assistants for product recommendations.
- Handling customer inquiries and support requests.
- Order tracking and status updates.
- Personalised marketing and promotions.
In manufacturing, Conversational AI assists with internal communication, troubleshooting equipment issues, and providing training resources. It ensures efficient operations and improved worker productivity.
Use cases:
- Real-time support for equipment troubleshooting.
- Automated internal helpdesk for employee queries.
- Providing training and safety information.
- Streamlining communication across teams.
No need to explain twice
97% of executives acknowledged that Conversational AI positively influenced user contentment
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.
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.
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.
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.
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.
tilising 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.
Get in touch
Drop us a line about your project and we will contact you within a business day
Our locations
New York
521 Fifth Ave, NY 10175
+1 805 491 9331London
9 Brighton Terrace, SW9 8DJ
+44 1922 214429Warsaw
Towarowa 28, 00-847
info@devico.ioLviv
Uhorska str. 14, 79034
info@devico.ioQuestions & answers
Why should we choose DevicoAI as a vendor for conversational AI development services over competitors?
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.
How much do conversational AI development services cost?
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.
Do I need a lot of data to use conversational AI?
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.
How is conversational AI automation different from traditional chatbots?
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.