Are you looking for a conversational AI platform but lost in the crowd? Can’t find the perfect conversational AI for your customer support teams? Your customers or for your sales teams? 

The internet is full of “conversational AI” tools that can help you create great customer experiences with just a “little” investment. But today, we’re giving you a solid framework to judge the conversational AI platforms and find the best platform that fits your needs! 

What is a conversational AI platform? 

Before we dive into the details of conversational AI platform selection, let’s understand what conversational AI platform is. 

Conversational AI platform provides a platform to implement human-like dialogue using virtual assistants, chatbots, voice assistants or virtual agents. Conversational AI platforms enable your organizations to build intelligent AI agents that can interact with your customers or employees effortlessly and help them complete their tasks. With conversational AI platforms, you can create: 

  • AI assistants for your customer support team to solve customer problems 
  • AI assistants for your customers to help them find information they need 
  • AI assistants for your sales team to help them gather insights to sell more products 
  • Improve new employee onboarding 

And more. 

When strategically deployed across the customer lifecycle, it unlocks transformative business outcomes – creating unparalleled experiences, optimizing operations, amplifying revenue streams, and unlocking a goldmine of actionable experience data.

Demystifying Conversational AI Architecture

Let’s look at how conversational AI works along with it’s components. 

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The image lists four basic components of the conversational AI platform or tools. Most leading conversational AI platforms employ an almost same stack synergistically orchestrating technologies: 

  • Natural Language Understanding 
  • Dialogue Statement management 
  • Explanation 
  • Natural Language Generation 

Let's deconstruct this multi-layered architecture:

User Input Processing The first stage involves converting user inputs across channels like text, voice, or visuals into machine-readable data formats. Sophisticated ASR models translate spoken utterances into text, while optical character recognition extracts text from images and documents.

Natural Language Understanding Once inputs are transcribed, natural language processing engines decode their semantics – mapping words and phrases to semantic representations of intent, entities, and context. This grasp of user intent and associated attributes is critical for determining the appropriate conversational path.

Contextual Dialog Management Building on this layer of semantic understanding, a dialog manager maintains conversation state, guides users through relevant dialog flows, accesses knowledge sources, and determines appropriate system responses based on intent classification and predictive intelligence models. This contextual awareness ensures coherent conversations that deftly handle multi-turn queries, apply business logic, triage intentions to human assistance when needed.

Response Generation Finally, naturalistic language must be generated and delivered to the user through their preferred channel. Modern conversational AI platforms employ neural response generators to dynamically create human-like replies rather than relying on rigid pre-defined templates.

Of course, there are nuances in how different conversational AI vendors package and deploy the above AI stack. But this high-level overview demystifies the core workflow, laying the foundation for the conversational AI revolution impacting customer engagement across industries.

Navigating the Conversational AI Landscape : How to select the best conversational AI platform?

The conversational AI market is projected to reach $32 billion by 2025. With such explosive growth, conversational AI vendors are racing to deliver ever-more sophisticated solutions. But the overwhelming number of options for every single use case is a boon as well as curse for  enterprises seeking to assess the most appropriate platform. So, we’ve developed a very strict and comprehensive questionnaire to find your next conversational AI software. 

Parameter 1: Artificial Intelligence Prowess of the team

When you’re working with a conversational AI platform, you’re working very closely with their team. It is imperative that you confirm the credentials of the team and ensure you’re not buying into an AI wrapper but a platform that is built on strong foundations. Because most often than not, you’ll need the platform to help you drive your KPIs. Having a strong team of AI experts working along with your team can help you all the time.  

  • What is the team behind the product? 
  • Do they have AI experts? 
  • Do they provide assistance apart from just the platform? 

Parameter 2: Artificial Intelligence capabilities of platform

Check for features like: 

Natural Language Proficiency: 

  • Does the platform leverage advanced language models and NLP frameworks to comprehend user intent accurately across multiple languages, dialects, and complex contexts like idioms, sarcasm, and code-switched inputs?

Contextual Understanding: 

  • How adept is the solution at carrying context across multi-turn conversations, maintaining coherent dialogs, and addressing follow-up questions without users repeating themselves?

Generative AI Capabilities: 

  • Can the platform dynamically construct conversational responses tailored to each individual rather than relying on rigid pre-defined templates? 
  • Does it leverage large language models (LLMs) to generate engaging content?

Industry & Domain Awareness: 

  • Has the AI engine been pre-trained on industry knowledge bases, taxonomies, and historical conversation data to comprehend domain-specific jargon, products, and processes? 
  • Assess the platform's understanding of your specific business vocabulary.

Composable AI Proficiency: Leading platforms employ modular and composable AI architectures, allowing enterprises to orchestrate complementary models for tone analysis, and other intelligent services tailored to unique use cases.

Parameter 3: Security

Amid rising scrutiny around AI bias and leaks of PII, it is important to check platform’s security. Conversational AI platforms must offer complete transparency into their underlying AI models, datasets, and decision-making processes. Probe vendors on their ethical AI safeguards:

  • Inquire about measures for monitoring model outputs, flagging sensitive data, and upholding compliance with industry regulations like GDPR, HIPAA, and CCPA.
  • Evaluate the vendor's AI ethics framework encompassing responsible development, privacy-preserving technologies, human oversight processes, and AI security protocols.
  • Assess the rigor of real-world testing methodologies to validate accuracy, safety, and robustness before enterprise-wide AI deployments.

Parameter 3: Platform Useability 

You need to make sure that the conversational AI platform you choose is easy to use for everyone in your team. Prioritize platforms empowering key stakeholders like:

  • Conversational Designers: Are there low-code/no-code tools with visual dialog builders to construct chatbot conversations, customize personality and apply guided learning without coding expertise?
  • Subject Matter Experts: Can non-technical authors have granular control to curate knowledge bases, refine training data, and optimize AI responses through inline human-in-the-loop oversight?
  • Business Analysts: Does the platform provide intuitive dashboards providing AI-powered insights into content gaps, response quality, and user behavior patterns based on conversation analysis? 

Parameter 4: Conversational responses

The conversational AI platform needs to converse naturally with any stakeholders involved. Check for the following: 

  • How seamless is the conversation? 
  • Does the conversational AI platform hallucinate frequently? 
  • Does the platform provide the ability to give canned responses for some queries? (might be useful for legal questions) 
  • Does it offer preloaded training templates, iterative models, supervised learning with human-in-the-loop, intent matching confirmation and exception processing? 
  • How adaptive is the platform in learning from user interactions and improving over time by monitoring user feedback over time?
  • How well can conversational AI platforms identify intents and drill down on problem identification? 

Parameter 5: Scalability and Integrations 

While pilot projects and proof-of-concepts are invaluable starting points, truly future-proof conversational AI platforms must scale seamlessly to support enterprise-wide deployments and evolving use cases:

  • Cloud Architecture and Infrastructure: Does the vendor's cloud architecture offer global presence, redundancy, and ensure business continuity, low latency, and data residency compliance for your specific needs?
  • System Interoperability: Can the solution integrate seamlessly with your existing enterprise applications (CRM, knowledge bases), data fabrics, and API ecosystems to provide a unified user experience?
  • Centralized Management: Does the platform allow for centralized control over AI models, conversational content, policies, and privacy controls across all deployments within your organization?
  • Omnichannel Orchestration: Does the platform offer tools and templates to quickly deploy AI assistants across all the messaging channels (text, social media etc.), voice interfaces, and digital channels (websites etc.) that you need?

Parameter 5: Real world performance 

Robust enterprise readiness extends beyond just technical checklists. Enterprises must validate real-world performance, time-to-value, and the vendor's ability to be a strategic business partner via:

Proven Expertise:

  • What industry use cases and customer success stories can the vendor provide for deploying their conversational AI platform at enterprise scale?
  • Can they share references and case studies of companies similar to ours that have successfully implemented their solution?

Performance Benchmarks:

  • What performance data can the vendor share on containment rates, accuracy metrics, and resolution quality from real production environments over time?
  • How do they measure and benchmark the effectiveness of their conversational AI models in real-world usage scenarios?

Support Quality:

  • What kind of professional services, solution architects, and data science teams does the vendor have to support sustainable adoption?
  • Do they offer dedicated resources to help optimize and continuously improve our conversational AI implementation?

Top 10 conversational AI platforms in 2024

Alltius

Alltius is a leading conversational AI platform that is built on decades of research in CMU and Wharton. Alltius transforms customer service by enabling organizations to create AI assistants for their customers and customer support team. Alltius’ self-serve AI assistants can solve customer queries within 10 seconds with almost 0 hallucinations. 

Alltius is a no-code, completely customizable and can be deployed within a day. It reduces wait times, deflects tickets, solves customer queries, provides customer insights to product teams and improves cross-sell & upsell revenues from customer support channels. 

Best Features

Versatility

Alltius’ AI offers a lot of versatility in options to create AI assistants. Alltius’ no-code platform offers lego like structure where you can pick and choose any of these options to create your AI assistants: 

  • Knowledge Sources: Alltius integrates with a wide range of sources including APIs, URLs, documents, tickets, videos, structured databases, images, charts, and more.
  • Skills: It is capable of performing diverse tasks such as answering queries, creating sales pitches, data comparison, writing to databases, ticket creation, labeling, and drafting emails.
  • Channels: AI chatbots are compatible with various channels including APIs, widgets, Slack, and others. 

Time to Value 

Alltius delivers ROI within weeks with ability to 

  1. Create and coach AI assistants in minutes, test and go-live in a day
  2. Get customer insights, product improvement recommendations & more from day 1
  3. Auto-update your FAQs

And more.  

Conversation Accuracy 

Alltius has inbuilt mechanisms to reduce hallucinations and improve the intent mapping. Alltius’ AI assistants are hallucination free with our advanced segmentation and knowledge technologies. This makes it easy to deploy it on customer facing channels. 

Alltius’ AI assistants currently demonstrate 95% precision out-of-the-box and can be pushed up to 99.99% accuracy and precision with additional customization by our team. 

Apart from this, Alltius is trained to understand customer intent with highest accuracy, which it then uses to draft responses in order to placate customers according to the mode. The Alltius’ AI assistants require no “prompt engineering” to get correct response, which makes it easy to use. 

Customization 

Alltius is a completely customizable platform to suit your needs. Alltius is currently used by customers for sales, customer support and customer self support use cases in a variety of industries like insurance, banking, retail, education, consulting and more. Alltius is completely customizable according to your use case. 

Conversation Analytics  

Alltius doesn't just reply, but is on top of all the conversations happening via the AI assistant platforms. Alltius’ AI assistants learn from conversations, it transforms conversations into knowledge articles for further use and it also shares insights about customer sentiments & trends to the product team to further improve the product. 

Such analytics lead to direct impact on customer satisfaction, revenue, loyalty and ultimately, customer experience. Learn more about Alltius: 

Dialog Flow (Google)

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Dialog Flow is a conversational AI platform by Google that allows building voice and text-based conversational interfaces powered by machine learning and natural language processing.

Strengths:

  • Integrates seamlessly with other Google services like Google Cloud
  • Easy to use visual interface for building conversational flows
  • Natural language understanding capabilities in over 20 languages

Weaknesses:

  • Limited customization options for more advanced use cases
  • Complexity increases with scale of implementation
  • Costs can quickly become expensive for higher usage
  • Challenges with handling context over multi-turn conversations

Pricing: Free tier with limited usage, paid pricing based on usage starting at $0.002 per request

IBM Watson Assistant

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IBM Watson Assistant leverages conversational AI and natural language processing to automate conversations across any application, device or channel.

Strengths:

  • Advanced NLP and intent recognition capabilities
  • Voice interaction support in multiple languages
  • Ability to search enterprise data sources for relevant info

Weaknesses:

  • Complex to setup and deploy for non-technical users
  • Limited out-of-the-box integration capabilities
  • Can be expensive for enterprise-scale usage
  • May require significant training data in some domains

Pricing: Lite plan free, plus pay-as-you-go based on usage starting at $0.0025 per message

Amazon Lex

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Amazon Lex is a fully managed AI service that allows building conversational interfaces into any application using voice and text.

Strengths:

  • Tight integration with AWS ecosystem and services
  • Automatic speech recognition in multiple languages
  • Pay per use pricing can be cost effective

Weaknesses:

  • Not well-suited for complex, multi-turn conversations
  • Limited customization options compared to other vendors
  • Primarily focused on building voice experiences
  • Can lack some advanced conversational capabilities

Pricing: Pay-per-use model, voice $0.00075 per speech request, text $0.00004 per request

Microsoft Bot Framework

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Microsoft Bot Framework provides a conversational AI platform to build and deploy conversational AI bots across channels like Teams, Slack, Alexa etc.

Strengths:

  • Integrates natively with Microsoft stack and services
  • Advanced language understanding with LUIS.ai
  • DevOps tools and end-to-end environment to manage lifecycle

Weaknesses:

  • Primarily focused on Microsoft ecosystem
  • Can be complex for non-technical teams to build bots
  • Lacks unified solution compared to some other platforms
  • AI/NLP capabilities may not be as robust

Pricing: Paid tier starts at $0.60 per 1,000 messages + data transfer and compute costs

Pandorabots

Pandorabots is a conversational AI platform to build and deploy intelligent conversational assistants and chatbots.

Strengths:

  • Visual interface for designing conversation flows
  • Can be deployed across multiple channels & devices
  • Integrates with major NLP services like Google, IBM etc.

Weaknesses:

  • Limited advanced NLP and dialogue management capabilities
  • Building complex chatbots can require significant development
  • Lacks some enterprise deployment management features
  • Customer support could be improved

Pricing: Free starter tier, paid business tier at $0.003 per interaction, enterprise pricing available

Kore.ai

Kore.ai is a conversational AI platform providing virtual assistants for enterprise employee & customer experience.

Strengths:

  • Advanced natural language processing across 100+ languages
  • Seamless omnichannel deployment across messaging, voice etc.
  • Comprehensive toolset from building to monitoring & optimization

Weaknesses:

  • More expensive than some other conversational AI platforms
  • Can have a steeper learning curve for business teams
  • Voice interaction capabilities could be improved
  • May require services engagement for complex implementations

Pricing: Not publicly disclosed, quote-based enterprise pricing

Rulai

Rulai specializes in conversational AI solutions using its proprietary low-code platform and NLP / NLU engine.

Strengths:

  • Focused on contact center and customer experience use cases
  • Low-code tools to build and maintain conversational apps
  • Automated training using real-world conversational data

Weaknesses:

  • More niche player compared to larger conversational AI platforms
  • Enterprise deployment and governance capabilities less robust
  • Voice interaction support appears more limited
  • Pricing not very transparent

Pricing: Not publicly disclosed, enterprise pricing model

SAP Conversational AI

SAP provides conversational AI services enabling intelligent chatbots and voice assistants for business applications.

Strengths:

  • Native integration with SAP's enterprise software ecosystem
  • Omnichannel support across text, speech and digital assistants
  • Deploys advanced AI models using transfer learning

Weaknesses:

  • More complex integration for non-SAP business environments
  • Relatively newer player in standalone conversational AI space
  • Advanced NLP/NLU capabilities could be lacking vs best-of-breed
  • Not as much flexibility compared to other independent platforms

Pricing: Based on SAP pricing policies & existing product usage

Haptik

Haptik is a conversational AI platform focused on building virtual assistants and chatbots for customer service.It offers user-friendly visual tools for designing chatbot conversations and pre-built templates for common industry use cases. Haptik combines natural language processing and intent recognition capabilities to facilitate intelligent self-service experiences.

Strengths:

  • User-friendly visual tools for designing chatbot conversations
  • Pre-built chatbot templates for common industry use cases
  • Good NLP and intent recognition capabilities

Weaknesses:

  • More limited in advanced conversational AI features
  • Voice/speech interaction integration is lacking
  • Not focused on broader enterprise virtual assistant use cases
  • Customer support could be lacking for large enterprise clients

Pricing: $200/month basic plan, enterprise pricing available on request

Conclusion 

In conclusion, selecting the right conversational AI platform is pivotal for organizations looking to enhance customer experiences, streamline operations, and drive revenue growth. The framework provided offers a comprehensive approach to evaluate and compare different platforms based on key parameters such as AI capabilities, security, usability, conversational responses, scalability, real-world performance, and more. Among the top 10 conversational AI platforms in 2024, each offers unique strengths and weaknesses, catering to various business needs and use cases. Platforms like Alltius stand out for their versatility, rapid time-to-value, high conversation accuracy, customization options, and robust conversation analytics. 

However, it's essential for organizations to carefully assess their specific requirements, industry nuances, and desired outcomes before making a decision. Whether it's prioritizing ease of use for non-technical stakeholders, ensuring compliance with security regulations, or seeking scalability for enterprise-wide deployments, aligning platform capabilities with business objectives is paramount.

Ultimately, successful adoption and deployment of conversational AI platforms rely not only on technical functionalities but also on vendor support, real-world performance benchmarks, and strategic partnerships. By leveraging the outlined framework and thoroughly evaluating each platform against their unique business needs, organizations can make informed decisions to unlock the full potential of conversational AI technology and drive transformative business outcomes.

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