Today’s Challenges

In an increasingly digital world, organizations find it difficult to effectively manage the explosion of interactions across voice and digital channels. The biggest challenge is to ensure an optimal experience while preserving the current tech stack and minimizing cost.

Most prevailing technologies cannot understand context nor respond conversationally. Technology solutions with limitations such as these force customers and employees to converse in a robotic or machine-like language. They then try to skip these options and speak to humans directly for service or support requests, increasing operational costs.

  • Long wait time
  • Human dependencies
  • Lack of scalability
  • Growing needs of customer and employee experience
  • Agent train, strain and drain
  • Inefficiencies in existing automation

Benefits of Experience Optimization (XO)

Advanced AI-first technology, when deployed correctly, enables businesses to harness conversational interactions with greater efficiency and at reduced costs while ensuring the highest customer, employee, agent and partner experiences. Ultimately, companies can have complete control over the user experience with real-time information, nudges and alerts. As one of the pioneers in the field of Experience Optimization and Conversational AI, here are a few things that offers:

  • Human-like interactions across voice & text channels
  • Choice of self-service options
  • “Customer First” image with proactive and personalized service
  • Omnichannel presence
  • Reduction in response time and average handling time
  • Continuous innovation at lesser cost
  • Deliver proactive and Personalized service
  • Achievement of scale and at speed

“By 2023, more than 60% of all customer service engagements will be delivered via digital and self-serve channels, up from 23% in 2019”

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“By 2025, customer service organizations that embed AI in their multichannel customer engagement platforms will elevate operational efficiency by 25%.”

Conversational AI: Enabling Experience Optimization (XO)

Devices are getting smaller while menus, systems and apps are growing increasingly complex, which people often don’t know how to navigate. However, they know what they want to do and they know how to chat and text. By replacing traditional UIs with human-like conversations, companies can make customer and employee experiences more straightforward and intuitive.

Recent advances in language technologies have also made complex linguistic decision-making methods beyond linear scripts and crude yes/no trees possible. Such advances have made virtual assistants a mature solution that enterprises across many industries are taking seriously.


Provides timely, accurate and tailored experiences on your customer’s terms


Reduces the need for tickets, callbacks and queues. Available 24/7

Cross Channel

Provides self-service across popular channels, endpoints and IVRs

Data Driven

Generates new sources of data on customer behavior, language and engagement


Highly scalable, available in a variety of languages and integrates seamlessly

Cost Effective

Requires minimal upfront investment, deploys rapidly and quickly reduces support costs


Delivers responses in seconds and eliminates wait times

Behind the Scenes

Experience Optimization (XO) uses a combination of conversational AI, natural language processing (NLP), machine learning (ML), speech recognition, natural language understanding (NLU) and other language technologies to process and contextualize the spoken or written word, as well as figure out the best way to handle and respond to user input. Overall, it helps optimize the interaction experiences on various channels, languages, and contexts.

Natural Language Processing

Conversational AI works by breaking sentences down to their root level, handling the many quirks of human language and acknowledging that there is information or a command to be parsed. The process by which a computer can understand human language is known as NLP. It does so by pulling out intents and entities, looking for statistically significant patterns that it has been trained to identify and considering factors such as synonyms, canonical word forms, grammar, slang and more.

Natural Language Processing Conversational AI
Natural Language Processing Conversational AI

Training Models

Machine learning and other forms of training models allow computers to recognize the combinations of words that typically indicate intent and learn from experience without being explicitly programmed by a human. Most platforms and frameworks provide only one of these types of training engines.

Platform Natutal Language Processing

Within the world of machine learning, there are two main types of learning methods. Supervised ML refers to analyzing a training dataset and using some form of a learning algorithm to make predictions, compare its output with the correct, intended outcomes and identify errors. This output is then used to modify the model accordingly – making it more accurate over time. On the other hand, unsupervised ML refers to analyzing a set of data that isn’t explicitly classified or labeled and it is typically used after the bot has been deployed for internal testing or to the field. Unsupervised ML usually involves automatically expanding a virtual assistant’s language model for chatbots by adding all successfully identified utterances to its model

Fundamental Meaning is a deterministic process. Here, a piece of given input information will always produce the same output information. The method uses semantic rules, such as grammar, word match, word coverage, word position, sentence structure and language context to match a user utterance to an intent.

Virtual Assistant Natutal Language Processing
Knowledge Graph Engine

Knowledge Graph is another training model that enables developers to create an ontological structure, a method of grouping according to similarities and differences, of key domain terms. The model then associates them with context-specific questions and their alternatives, synonyms and ML-enabled classes.

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