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Breaking Down the Key Differences Between ChatGPT and Replicant

You’ve probably heard of ChatGPT by now. OpenAI’s automated chatbot – which crossed 1M users in just a few days following its December release – has made it clear to the world that human-to-machine communication has entered a new era.

But for businesses, the AI landscape may still look a bit blurry. 

That’s because AI is a broad term. The current wave of new tools, apps, and software updates that are centered around AI can make it difficult to keep track of the specific impact each is intended to have. For customer service leaders, the difference between Contact Center Automation and ChatGPT is of particular interest. 

On the surface, both are AI-powered solutions designed to improve and expedite communication with users. Both leverage natural language processing (NLP) and machine learning to process and respond to questions. And both are constantly improving and evolving through continuous learning and optimization.

But ChatGPT and Contact Center Automation solutions like Replicant differ in several ways. From how they can help customers to how they’re managed by administrators, both ChatGPT and Contact Center Automation offer distinct benefits in each a general business setting and a specific customer service environment.

Here are five key differences:

Scope of Conversations

ChatGPT is a chat application that leverages OpenAI’s GPT3.5 Large Learning Model (LLM). It is trained on a large corpus of text data from the internet, and is designed to provide natural, informative answers to any question, based on public knowledge. The large training dataset used by ChatGPT’s LLM allows it to accurately predict what each word it types in a response should be. 

Contact Center Automation shares the goal of natural automated conversations, but is designed to resolve specific customer service issues for businesses based on their unique policies, know-how and systems. In practice, this means that Contact Center Automation is deployable in customer service, where questions don’t just require general answers but full resolutions. Contact Center Automation is centered around the idea that customer service leaders need a way to automate their most common customer service calls. In this way, it focuses on predictability and responses that lead to specific resolutions – not just natural conversations. 

Communication Channels 

ChatGPT is designed for use in text-based communication channels, such as messaging apps and chatbots. Using ChatGPT over voice can present several challenges, as voice-based interactions are typically more conversational and fluid than text-based interactions. Contact Center Automation, on the other hand, is designed for voice, chat, and SMS conversations. For businesses, the phone channel remains a crucial and popular method of customer service.

Customer service leaders choose Contact Center Automation because phone conversations present many different accents, background noises, and unique patterns of speech. It’s imperative their automation solutions are voice-first to be truly impactful. In addition, Contact Center Automation solutions that are powered by a single conversation engine across each channel enable customers to have consistent experiences. 

ChatGPT is limited to answering simple customer service FAQs.

Customer Interactions

According to ChatGPT itself, the customer service opportunities for the application are typically those “that can be resolved through self-service, such as answering frequently asked questions and handling simple inquiries.” That means it can give you some of the information you could find on a website (e.g., what is the return policy for a product) but not help you if the replacement product was not shipped. For this, a solution requires access to internal systems and policies that are specific to that business. Contact Center Automation, on the other hand, excels at resolving more complex customer interactions that traditionally require human intervention.

Take an appointment scheduling call, for example. In this scenario, a customer may ask “can I come into the Brooklyn location in two weeks?” Thanks to domain-specific conversation design, Contact Center Automation can understand this to mean a customer desires an appointment at a certain location, in a certain timeframe. It can respond with, “Sure, does 9AM on Tuesday, March 14th at our Brooklyn location work?” In this scenario, an LLM would lack the context to understand the customer’s intent and the ability to to take action. It may simply respond with something along the lines of “yes, the Brooklyn location is open on Tuesdays,” and wouldn’t have the functionality to schedule on your behalf or estimate a cost based on your customer profile.

Contact Center Integrations

GPT3.5’s capabilities can be leveraged by businesses using the OpenAI API. This can help make several processes more efficient, including data sorting, brainstorming, and information summarizing. However, it can’t be integrated directly into a contact center’s tech stack. Contact Center Automation integrates into every existing CCaaS and UCaaS platform, allowing agents to keep using their everyday tools while automation takes care of tier 1 requests.

Contact Center Automation can be placed in front of, within, or behind an IVR, or replace it entirely. For businesses, it is crucial that customer service automation solutions can connect with a CMS, telephony, and industry-specific software in order to add meaningful value for use cases like customer authentication, booking & reservations, account management, and many more. 

Analytics and Observability

There are many ways customers can ask the same question, and contact centers need immediate visibility into how their automation solutions react to new utterances. ChatGPT does not offer the observability to track unknown intent detections, nor does it offer the kinds of analytics contact centers require to constantly improve their experiences. Conversely, Contact Center Automation provides visibility into all customer support conversations in an end-to-end dashboard. From there, managers can monitor realtime data for KPIs like resolution rates, call volumes, and call drivers.

Automation also provides transcripts for every conversation that redact PII and allow businesses to dig deeper into their customer experience. Additionally, contact centers can immediately update conversation scripts to better understand the unsupported flows in their automation flows that can help improve their website, IVRs, or further automation opportunities. 


On the whole, both ChatGPT and Contact Center Automation present massive opportunities for businesses to drive efficiency. ChatGPT has many applications as a productivity tool, a powerful API call, and a brainstorming and research resource. Long-term, LLMs will become more ubiquitous and add productivity gains for many existing applications. But using LLMs in isolation with customers is risky at best. Currently, it lacks important security and compliance features like SOC2, HIPAA, and PCI for external, out-of-the-box use by enterprises. 

Conversely, Contact Center Automation represents the most advanced external-facing application of conversational AI. Designed specifically for customer service, Contact Center Automation is built on the key tenets that enterprise businesses require: reliability, trust, and end-to-end resolutions. Replicant is already automating millions of conversations every month to help businesses meet objectives specific to their operations, including lowering cost per contact, increasing service capacity, improving CSAT, and most importantly freeing agents to focus on more complex tasks.

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