In today’s competitive landscape, software enterprises are constantly seeking ways to streamline operations and reduce support costs. Conversational AI offers a compelling approach to achieve these goals.
Further, with the advent of LLMs and RAG framework, it seems very easy to build a virtual assistant. However, the task isn’t always easy. Before you even venture toward looking up the build aspect, you should gauge whether third party solution providers are potential options.
Before embarking on the development of a custom GPT chatbot, it’s crucial to carefully evaluate the advantages and disadvantages, as well as the potential challenges and time required.
This blog post will delve into the key differentiators between building your In house Custom GPT chatbot vs third party solution providers like SearchUnify Virtual Assistant (SUVA). This will empower you to make an informed decision for your enterprise, based on your specific needs and requirements.
Understanding Custom GPTs
First let us understand what an Inhouse custom GPT is.
For instance, if you are a paid subscriber of ChatGPT, then you must be a bit aware of this. Custom GPT is a specialized version of the GPT (Generative Pre-trained Transformer) model that has been fine-tuned for a specific task or domain. ChatGPT allows you to quickly create custom GPTs by simply writing the prompt (instructions), providing some examples and your GPT is ready to serve.
How does GPT Get You the Answers?
Based on the instructions provided, whenever you enter a prompt, it looks into all the data trained with chatGPT, combines them with the prompt and generates text based answers. This framework is called RAG. RAG stands for Retrieval Augmented Generation. It’s a technique used in natural language processing (NLP) where a language model is combined with a retrieval system to enhance its ability to generate text.
Technically this is how RAG Works:
- Retrieval
Relevant information is fetched from a knowledge base or external data source based on the user’s query.
- Augmentation
The retrieved information is combined with the original prompt and fed into the language model.
- Generation
The language model generates text based on the combined information, providing more accurate, relevant, and informative responses.
You must be thinking by now that it is pretty easy. But wait all these seems to be easy because ChatGPT in this case creates a RAG pipeline for you in the Custom GPT and the data it is leveraging is all present in the public domain. So, this approach is good when your enterprise data is publicly available and is ready to serve. Delve deeper into RAG and its best practices.
Now imagine that your enterprise data is not public and residing in silos in multiple platforms like Salesforce, JIRA, Zendesk, etc.
Therefore, to build your own inhouse virtual assistant, you will need to build:
- RAG pipeline to retrieve knowledge
- Connectors with these platforms to train the content
- Permission modules to show information as per user permissions.
Now, these three are not just one liner tasks but individual products in themselves and a dedicated inhouse team of Software developers, data scientists would be required to achieve such a feat.
Further, this is just a use case of questions and answers. There could be use cases like creating cases from the chatbot, or warmly transferring the conversation to the live agent, integrating RPA to perform monotonous tasks, and many more.
By now you must be thinking that there is no harm to evaluate other options, the third party vendor with a pre-built solution meeting these capabilities.
Presenting you SearchUnify Virtual Assistant (SUVA), a conversational AI chatbot from the product suite of SearchUnify which has just the right modules to meet your requirement of optimizing customer support costs.
By Installing SearchUnify Virtual Assistant (SUVA) on your customer support domain you will be able to gain the following benefits.
- Focus on Core Business Activities
Developing and maintaining a custom AI model like Custom GPT solution requires a dedicated in-house team. On top of that, maintaining the data privacy of the enterprise content in the RAG pipeline is an added effort. This can divert valuable resources away from your core business activities. Conversely, solutions like SearchUnify Virtual Assistant (SUVA), a pre-trained solution with its FRAG framework, eliminates this burden. You can leverage its capabilities without getting bogged down in model management and updates.
Additionally, SearchUnify Virtual Assistant (SUVA) stays current with the latest advancements in the field, ensuring you benefit from ongoing innovations.
- Seamless Integration and Deployment
While custom GPT models can be deployed, integrating them with various CRM software solutions like Salesforce and Zendesk can be a challenge. SUVA seamlessly integrates with these platforms, streamlining data flow and enhancing overall efficiency.
- Advanced Capabilities for Enhanced Relevancy and Insights
SUVA goes beyond basic question answering by incorporating hyper-personalization giving it a personal touch to the end user. Additionally, SearchUnify Virtual Assistant (SUVA) allows you to choose from multiple large language models (LLMs) based on your specific use cases, ensuring optimal performance. To learn more about these functionalities, watch episode 2 of SUVA Chronicles.
- Continuous Learning and Improvement
SearchUnify Virtual Assistant (SUVA) incorporates feedback mechanisms and reinforcement learning to continuously adapt and improve. It also identifies the use cases where it should try providing additional self-service by auto-suggesting questions to the users, before they warmly transfer the conversation to a human agent. This feedback loop helps SearchUnify Virtual Assistant (SUVA) identify areas for improvement in content and user experience.
- Integrated AI and RPA for End-to-End Automation
SUVA integrates AI with Robotic Process Automation (RPA) to automate tasks like case creation, status updates, and live agent handovers. This comprehensive approach streamlines service management and reduces manual workload.
- Advanced Analytics and Performance Measurement
Beyond the basic LLM usage dashboards offered by custom GPT solutions, SearchUnify Virtual Assistant (SUVA) provides comprehensive analytics and dashboards within the admin panel. These insights, including deflection rates and abandonment rates, empower you to measure SUVA’s performance and identify areas for optimization.
Learn How Accela Scaled Customer Support & Self‑Service Outcomes with SUVA
Conclusion
While custom GPT solutions offer a certain degree of control, SUVA’s pre-trained, comprehensive approach delivers significant advantages for enterprises. By focusing on core business activities, seamless integration, advanced capabilities, continuous learning, sentiment analysis, and integrated AI-RPA, SearchUnify Virtual Assistant (SUVA) empowers you to reduce support costs and deliver exceptional customer experiences.
LLMs are the easiest part of AI/ML projects. The complete stack of building intelligent conversational AI solutions is the major chunk. Managing access controls, integrating with CRM softwares, etc. are still some of the stack which customGPT will not provide.
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