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What is and why does it matter to your Business?

Updated: Apr 8

Introduction to Yog GPT

Before diving into the depth of what is and how it benefits your business, let's discuss small language models (SLMs).

What are Small Language Models?

Small Language Models (SLMs) are compact generative AI models having small versions of their Large Language Models counterparts. They have fewer parameters than LLMs ranging from millions to a few billion. This difference in size leads to several advantages of using SLMs over LLMs.

SLMs require lesser computational power and memory, making them suitable for deployment on any small device or edge computing processing. This opens various real-world opportunities such as personalized mobile assistants and on-device chatbots.

Large Language Models (LLMs) vs Small Language Models (SLMs)

The capabilities of SLMs and LLMs aren't constrained by size. LLMs brag an extensive understanding of NLP because of their training on massive datasets, while SLMs excel in domain-specific tasks. SLMs are foundation models trained for specific functions, thus they can run with less computational power. On the other hand, LLMs with diverse architecture allow for complex inference, making them applicable for various applications, from conversational AI systems to content generation.

Comparison between SLM and LLM

With the above comparisons, choosing small or large language models is based on the need, preference, or budget. With the development of AI tools, the demand for more computing power and the need to create sustainable technologies may redirect the market into a small language model direction.

Popular comment: Mike Lorton, a well-known data scientist says choosing between SLM and LLM is like choosing between a scalpel and a Swiss Army knife - each is best suited for a specific task.

Why do SLMs matter?

  • Accessible and Affordable: SLMs are easy to deploy and train by anyone who has a mobile or laptop, without requiring cloud services or hardware. This lowers the barriers for developers and researchers who want to learn and deploy language models for specific tasks.

  • More Explainable and Trustworthy: SLMs have fewer parameters and simpler architecture than LLMs, which makes them suitable for interpretation and debugging. They have more transparent and controllable training data resources, that reduce the chance of biasing in the output.

  • Efficient and Scalable: SLMs use less power and memory than LLMs, which makes them sustainable. They have faster interference time and smaller footprints, suitable for real-time applications and edge computing.

Use Case of SLM in AI Application

Now let's explore how small language models are impacting various AI applications:

  1. Conversational AI and Chatbots: SLMs are capable of developing effective live chatbots. Their adaptability and responsiveness make them suitable for designing conversational interfaces for customer service and virtual assistance.

  2. Customer Retention: SLMs can be trained to retain customers and reduce the nurturing efforts. It would integrate to analyze customer feedback, and sales records, and create personalized recommendations, offers, and customer incentives.

  3. Email and Messaging Assistance: Small language models can help compose emails, text messages, or other forms of communication by suggesting phrases or providing template responses. They can save time and improve communication efficiency.

Case Studies Where Small Language Model Shine

Many companies have already found utility by deploying SLMs customized to their business needs. We will discuss some applications of small language models in various domains, where learned models create business value.

Customer Service and Support

Fetching and integrating detailed product information is important to give accurate and personalized responses to customer queries. Use of fine-tuning to adapt customer queries or company-specific terminology for more effective communication. Customizing the query responses leaves an impression on customers and they tend to learn more about the product.

Automated Email Responses

SLMs can be used to generate personalized email responses for customer inquiries, follow-ups, or outreach efforts. By analyzing the content of incoming emails and generating customized responses, these models can save time for sales and customer service teams while maintaining a high level of responsiveness.

FAQs Assistance

Small language models can be integrated into self-service portals or chatbots on the website to provide instant responses to frequently asked questions, reducing the need for customer-executive effort.

Prospect Qualification

By analyzing customer interactions and inquiries, small language models can help sales teams qualify prospects more effectively. They can identify key phrases or patterns in customer communications that indicate a potential sale, allowing sales reps to prioritize follow-ups.

Sales Script Assistance

Small language models can assist sales representatives by providing real-time guidance or suggestions during customer calls or meetings. For example, they can offer recommended talking points, objection-handling strategies, or product information based on the context of the conversation.

What is is a personalized GPT trained on your organization's knowledge resources that empower your customer-facing team to handle sales and servicing queries. is an on-the-go Gen-AI model that can easily deployed on the platform interface of your choice like WhatsApp, website, Chatbox, CRM, etc. helps you empower your customer executive team to be more efficient and improve customer experience.

Let's understand what is in depth.

Organizations have information stored in many resources like PDFs, videos, memos, brochures, manuals, etc containing vast information about products or services. Thus, it is time-consuming for your sales executive to go through the entire information and gather what they want. As a customer-executive, they have to answer the query of a customer in depth which can be possible only if they are aware of every detail of the product. Thus training a customer executive to be responsive to every customer query requires resources and time.

Here, came to help you out and trained your customer executive with ease. But how can we help train your customer executive like e a pro?

Introduction to Yog GPT

All you need to do is upload the manual, brochure, or any PDF having detailed information about your product on The Gen-AI model will be trained automatically and now have detailed knowledge. Now, you can raise any product-oriented query and will respond immediately.

Now, imagine a scenario, where your customer executive is on a customer call and gets a query not from their expertise. Then, they can ask trained the same query, get a quick response, and maintain customer servicing.

What does offer? is a one-stop solution to improve the way you interact with customers in real-time. On-ground sales and servicing teams can use to maintain the pace with the changing product.

Here is how helps you grow:

Yog GPT Offering

Efficient Knowledge Dispersal

In an organization, every sales executive should be well-informed about the product. So they can respond quickly to any customer query instantly.

Now, the fact is that product or service descriptions are mentioned in various knowledge resources including:

  • Brochures

  • Intranet (Product blogs & FAQs)

  • Emails

  • YouTube videos

  • Manuals

Going through all these resources to learn about a product is time-consuming. Thus, can be trained with all available resources and now sales executives can use it as their personal assistance to get all the answers to customer queries in real-time.

On-the-go Deployment can be deployed on any platform of your choice including WhatsApp, website, and CRM to ensure faster adoption. It is also possible to integrate APIs with any other platform.

For example, if you are using a live chatbox deployed on your website, then you can also integrate to improve the customer experience. Or you can create an extension of this model so native users can use it from their web browser.

Faster On-Boarding

Once the model is trained and deployed, sales teams and channel partners can begin using it as your personal assistant from the first week. helps them understand the product in-depth and prepare to respond to customer queries with confidence.

Reduce your training effort and resources to educate customer executives, simply deploy to make your customer executive more intelligent.

Update with Time

Basic AI models require continuous training with changes in any information resources. However, our training feature lets you update the AI model by sending an email or uploading a document via WhatsApp.

Let's understand this with an example:

Suppose you add a new feature to your offered services, then you do not need to re-train the model. Simply share the updated document with the model on WhatsApp, and it will fetch the added details automatically. The next time you query anything from a model, it will respond as per the updated document. Likewise, you can update the model by emailing the given email ID. So, updating the model is as easy as sending messages on WhatsApp or emailing.

Adaptive to Vernacular

The model also offers vernacular capability to ease the adoption by regional teams. This reduces the communication barrier between executives and customers. However, to adopt the language used by the team members, the vernacular model might undergo separate training.

Some Use Cases of

To understand, how businesses of various industries can use to empower their sales and servicing.

SaaS Applications

Many giant SaaS software solution companies have a pool of 30k+ people working as members of their distributed teams across the globe. However, team members face some major problems in their jobs which include:

  • Onboarding new sales executives and training them to align with the understanding of the services. Especially, if the fresh member is not from a tech background it becomes difficult to understand services and technologies from the available knowledge resources. Here, can help you out. A fresh sales member can learn in-depth about the services by regularly asking service-oriented questions and be able to handle customer queries with disseminated service understanding.

  • Keep teams updated on the few features added to the existing product or services. Re-training of sales members requires significant time and resources. Instead, takes a minute to be re-trained. You can update the model simply by messaging it on WhatsApp or by sending an email.

  • A customer executive gets a bulk of customer query calls and sometimes it is difficult for him to respond. Finding a query solution in any resources like manuals, brochures, and videos takes time and hurts the customer's experience. But with, an executive can get a quick answer and now they can respond confidently to the customer.

Real Estate

A real estate agent handles 25+ projects on average per day. Every listed property has a brochure containing all the information needed, including the plot size, carpet size, location, price per sqft, and more details. A calling agent has to go through the entire brochure and it is very difficult to remember every detail and respond quickly to the customers.

So every time your sales executives are on a customer call, they can use to get answers to every customer query and respond the same quickly. If a customer asks for a carpet size or per sq ft price for any listed property, then the sales executive just needs to write the property name, and every detailed information will be shown.


Automobile manufacturers get component fault queries in bulk and your customer-executive might not be aware of the solution. However, the detailed procedure to repair the fault is given in the manual. Then all you need to do is upload the manual in the model and ask for the solution to prepare the fault. You will get a detailed step-wise solution that you can respond to the customer.

Similarly, if your mechanic is on a visit to repair a fault but finds it difficult to repair. Then he can ask a model on his phone to give a step solutions to repair a fault that too in his regional language. Then following the steps, he will be able to repair the fault.

Automotive Components Distributors

A leading bearing distributor deployed to power their sales and distribution channel. A bearing distributor has different-sized SKUs and gets a demand for the supply of every part. Now imagine a scenario, where you get an order to deliver the bearing with given details of inner or outer diameter. Then you need to give a value of diameter to the and it will suggest the exact part you are looking for.

Thus, instead of groping the entire brochure, you simply need to give a few parameters and get the right SKU/part number.

Frequently Asked Questions (FAQs)

Ques. How does a model work? is firstly trained on the organization's knowledge resources. Once trained, the customer support team can access this knowledge, insights, and data on the go and answer customer queries.

Ques. How much data is needed to train a model?

You can use all of your product or service knowledge resources to train a model. Upload brochures, and manuals, and paste a Youtube or blog link, or any other resource to train the model.

Ques. What is fine-tuning in AI? 

Fine-tuning is the process of training a pre-trained machine learning model on a small, targeted data set. Fine-tuning keeps the original capabilities of a pre-trained ML model while modifying it to be used in more personalized use cases.

Ques. Can I deploy on my mobile WhatsApp?

Yes, you can deploy on your mobile WhatsApp to ask questions related to your customer inquiries.

Ques. How is the model updated when the specifications for a product or service change?

Updating the model is as simple as sending a WhatsApp message. You can update the model just by sending that update on WhatsApp as a message, uploading the updated document, or emailing the given ID. It does not require re-training from the scratch.

Ques. How my customers can use to get answers to their queries?

You can deploy on your website as a live chatbox, marketplace platform, or emailing system, or create an extension so customers get an instant solution for their queries.

Ques. Is it possible to deploy the model on every customer executive device?

Yes, it is possible to deploy the model on every customer executive device so they can be responsive on every customer query call.

Ques. Can I train the model specifically for the regional teams?

Yes, training in the model for regional teams is possible. The model will trained separately to adopt the language and jargon used by the team members.

Ques. What is the best replacement for the live chatbox on the website?

You can deploy instead on your website which empowers the customer servicing support and lets all queries resolved.


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