AI Service Agents - Build vs. Buy

As AI initiatives become higher priorities and technologies become more accessible, many enterprise organizations consider building solutions in-house. A variety of questions typically arise when exploring the idea:

• Will general LLMs (GPT4, BERT, RoBERTa, LLaMA, Megatron) work for our needs? 

• Can we just apply ChatGPT? 

• Is GenAI the answer for us? 

• Can we just use RAG architecture or Vector Embedding Databases? 

• Are there some use cases that make sense to Build vs. Buy? 

• Do we have the right expertise?

AI Service - Build vs. Buy

For service organizations, the build approach is often the best path for simple use cases that require a bit of customization, while complex use cases in service environments with complex products find a faster path to success with the right AI solution partner. 

Planning an AI initiative to improve service performance?  

Choosing the right AI technology is critical. Should you adopt ChatGPT or leverage Ascendo AI’s Service Co-Pilot, purpose-built for service organizations? 

Companies with Technical Support or Complex Services typically require a higher degree of accuracy from AI responses than ChatGPT offers out-of-the-box. Ascendo’s AI has been trained on the service data of the world's largest manufacturers, delivering unmatched accuracy compared to even the best adaptations of generic LLMs.

Before attempting to adapt LLMs to fit your needs, consider the high costs, time, integration, and expertise needed for a solution that may still have limitations. Ascendo AI’s platform gives you unmatched AI performance, so you can quickly begin optimizing your service operations. 

Ascendo AI Service Co-Pilot Vs. Open Source LLMs 

Criteria	Ascendo AI	Open Source LLMs
Service Domain Expertise	Service-focused model, trained on the data of complex and large manufacturers to understand the language of service.	General-purpose models lack specialized knowledge and features tailored for the service domain.  Continuous change in models also makes it harder to continuously pick and tune the right model for the right use case
Personas	Customizes interactions for different user personas (e.g., customers, remote agents, field agents, escalation engineers, managers) to provide relevant responses and actions.	Does not natively support persona-specific customizations, making it less effective in targeting different user needs.
Data Integration	Seamlessly integrates with customers’ CRM or data, assets, service records and knowledge base, ensuring that the most relevant and up-to-date information is used.	Lacks native integration capabilities with specific customer systems, leading to potential data silos and outdated information. Additionally, it requires significant integration efforts to utilize company-specific data and may be prone to errors based on outdated or incorrect data assumptions.
Data Quality	Utilizes a custom evaluation framework specifically designed for service data. To achieve the highest level of accuracy, we implement advanced and tailored data transformation techniques that align with the unique characteristics of the data. Each stage of the pipeline—from data ingestion and cleaning to transformation, storage, and response generation—is carefully architected and optimized to meet the specific demands of the application and user requirements, ensuring enhanced precision and relevance in the outputs.	Is trained on a broad dataset, which might not include specific or detailed data pertinent to your industry and company needs.
AI Guardrails	Private, RAG based application, does not allow for hallucinations control and guardrails.	Prone to hallucinations and inaccuracies.
Expert Knowledge Integration	Incorporates tribal knowledge from your service pros directly into the system, ensuring high-quality, accurate responses.	Does not allow for easy integration of specific expert knowledge, potentially leading to generic or less informed answers.
Use Case Personalization	Makes recommendations based on specific machines and customers.  Also, recommendations cater to the engagement area where the use case is initiated from – e.g., slack, teams, email, phone, forms, etc.	Answers do not take asset and customer history into account.
Implementation Time	Designed for quick deployment and integration with existing systems, Go live in less than 2 weeks.	Longer setup and training period required, increasing time to deploy.
User Expertise	Designed for service professionals, minimizing the need for extensive training and technical knowledge.	Requires a dedicated engineering team to customize and implement effectively.
Support & Maintenance	Offers dedicated support and maintenance services tailored to customer needs.	Does not provide specialized support and maintenance for specific industries, potentially leading to slower issue resolution and will require in-house support and continuous maintenance.
Team Enablement	Offers onboarding or enablement support.	Does not provide specialized support and maintenance for specific industries, potentially leading to slower issue resolution and will require in-house support and continuous maintenance.
Scalability	Scalable architecture is designed to handle the growing needs of service organizations.	Scalability depends on the in-house development team’s capabilities.
Cost	Transparent pricing tailored to the needs of service organizations, Set annual pricing.	Cost of customization, usage, implementation, and scaling will vary and may require additional development resources.
Performance	Optimized for high performance in service-related tasks, insights, and recommendations based on your data, ensuring fast and accurate responses to handle issues faster.	A general-purpose model may lead to slower or less accurate responses for specific service industry tasks, and it varies on how well it's customized and integrated.
Security & Compliance	Built-in compliance with industry standards and regulations.	Custom security measures and compliance need to be implemented by your team.
Research & Development	Dedicated teams continually update and integrate the latest AI and GenAI advancements, ensuring cutting-edge solutions tailored for service organizations.	Lacks focused, ongoing R&D specific to the service industry, leading to slower adaptation of new technologies and less customized solutions.
Interface	End-user interface for model tuning.  Transparent method to share mapping and contexts.  In-depth human augmentation to incorporate expert knowledge and feedback.	OpenAI sandbox experience.  Generally, a black box.
Modules	Offers specific modules tailored for different service use cases (e.g., troubleshooting).	Does not offer specific or customizable modules for different use cases.

In the realm of AI for service organizations, the choice between building an in-house solution using general LLMs and partnering with a specialized AI provider like Ascendo AI ultimately hinges on factors such as the complexity of your service operations, the level of customization required, and the desired speed to market. 

While general LLMs offer a versatile foundation, they often lack the domain-specific expertise, data quality, and tailored features necessary for optimal performance in service environments. Ascendo AI's Service Co-Pilot, on the other hand, is specifically designed to address the unique needs of service organizations, providing unmatched accuracy, seamless integration, and a user-friendly experience.  By choosing Ascendo AI, you can accelerate your AI implementation, reduce costs, and ensure that your service operations are equipped with a powerful AI solution that delivers tangible results. 

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