Designing an intelligent order system and customer experience for Kamand sweets shop

Brand Type: Sweet shop and confectionery
Brand name: Kamand
Netanik services: Artificial intelligence, order automation, customer experience, intelligent systems, etc SEO local

Infrastructure and site technical manager: Navid Zamani

Project manager and graphic designer: Sohail Motaghinia

Smart ordering system for Kamand sweets shop

"Kamand" was a sweet shop focusing on fresh products, special occasion cakes, handmade sweets and special orders. As the number of customers and the variety of orders increased, it needed a more regular process to manage customer relations. A significant part of the orders were received through calls, messengers and social networks, and this caused the customer information, delivery time, product type and order details to be scattered in different channels. The issue was not just placing an order; The customer expected to be able to quickly ask about products, get a suitable offer and finalize his order without long back and forth. Netanik for a lasso Intelligent order system and customer experience designed that by combining artificial intelligence, automation and customer data, the communication path from the first question to placing the order was more coherent.

The main problem of the lasso

In businesses like confectionery, many customers need guidance before buying, and questions such as product selection, required quantity, taste, weight, occasion and delivery time are considered a natural part of the sales process. If answering all these questions is done manually, with the increase in the volume of messages, more pressure will be put on the sales team or collection staff. On the other hand, short and impersonal answers may kill the opportunity to build a better relationship with the customer. Therefore, Kamand needed a solution that would increase the response speed, but also maintain the human and intimate feeling of a candy brand.

The goal of system design

The goal of the project was not to build a generic chatbot to answer a few common questions, but to design a conversational experience that could simplify part of the buying process for the customer. The system should know product information, order conditions, preparation times, and frequently asked questions, and provide relevant answers based on customer needs. In more complex orders, the system must be able to collect information and prepare the request for human review. In this way, instead of eliminating human communication, artificial intelligence entered the process as a layer to simplify this communication.

Suggested internal link: "Artificial Intelligence Solutions for Businesses" → Netanik AI Services page.

Intelligent customer experience architecture

The architecture of this Smart ordering system It was designed in such a way that the customer can experience a relatively seamless path from the first interaction to the request registration stage. The basic information of Kamand products and services was placed in a structured knowledge base and the artificial intelligence model used this information to answer. In the order section, necessary information such as the product, quantity, desired date and type of request was collected so that the Kamand team could have more complete information without searching the messages again. This structure also provided the necessary background for the development of more capabilities in the future.

Product information layer

The first part of the system was the product database, which included each product's specifications, categories, descriptions, order conditions, and related information. This data must remain structured and up-to-date in order to generate AI responses based on real brand information. In case of product or sales conditions change, knowledge base information should also be updated so that the system does not use old information. Such a structure makes AI respond based on the data provided by the business instead of guessing.

Customer interaction layer

The second layer was related to the conversational interaction and was responsible for receiving the customer's request in the form of natural language. Instead of choosing several complicated options, the customer could explain his need in a simple way and the system would suggest the right path according to the available information. If the request was simple, the answer could be given directly, and if it required further investigation, the conversation would move to the next stage. This model puts technology in the background and the customer experience remains natural.

Artificial intelligence in the ordering process

An important part of the project was using AI to understand the customer's intent rather than just recognizing the words in the message. For example, a customer may say "I want a style option for a birthday party of about twenty people" and expect the system to first understand his need and then suggest suitable options. Such interaction requires combining product information with conversational context and can make the shopping experience more personalized than a fixed menu. Of course, the smart offer should be based on real products and verified brand information so that the customer experience is not dependent on false information.

Appropriate product offer

The system could suggest options among the available products according to the occasion, the number of guests, the type of product and the customer's preferences. These offers were not intended to replace customer choice, but to act as a digital assistant that makes the choice easier. In such a model, product information and brand rules determine what offers can be displayed, and AI takes over the task of understanding the request and making a connection between the customer's need and the available options. This approach enables personalization without turning the shopping experience into a complex process.

Answering frequently asked questions

Another part of the system was designed to answer questions that are frequently asked by customers. Things like preparation time, how to register an order, conditions of special orders and general information of products can be placed in the knowledge base and the system can respond based on them. This issue can reduce the amount of repeated questions from the shoulders of employees and at the same time make the initial answer available to the customer faster. Where there is insufficient information, the system should forward the request for human review rather than guessing.

Personalization of customer experience in Kamand

One of the important opportunities of artificial intelligence for brands like Kamand is to create an experience that is not exactly the same for all customers. A customer shopping for a birthday has different needs than someone ordering for a business meeting, corporate gift, or family event. By receiving some simple information, the system can recognize the context of the request and adjust the path of offers according to the same conditions. Such personalization should be done while maintaining simplicity so that the customer does not feel forced to fill out a long form to buy a simple product.

Knowing the occasion of purchase

Occasion is one of the most important pieces of information in the pastry and cake industry and can affect product type, quantity, packaging and preparation time. The intelligent system could determine the appropriateness of the order with short questions and then provide relevant information to the customer. For example, ordering a birthday cake can be different from ordering a gift or buying a large number of sweets. This simple separation makes the user experience more personalized and the ordering process more manageable for the internal team.

Understanding customer preferences

If you have the right infrastructure, information from previous orders can also help personalize future communications. For example, if the customer has selected a certain product or type in previous interactions, the system can provide more relevant offers in the right situation. Of course, the use of such data must be done in compliance with privacy and customer data laws. The purpose of personalization is to create a better experience, not to collect information aimlessly.

Order registration and management automation

After the conversation phase, the most important part of the system was converting the conversation into usable information for the Kamand team. In a manual process, employees may have to extract order information from multiple messages and re-enter it in another office, file, or system. Automation can capture this information in a specific format and provide it to the team for review or approval. In this way, the gap between "customer conversation" and "operational order information" is reduced.

Order information structure

For each order, fields such as customer name, product type, quantity, desired date, occasion, specific description and order status can be considered. After receiving the information, the system can identify incomplete items and ask the customer the necessary questions before transferring the request. This avoids unnecessary back-and-forth and allows the Lasso team to receive a more complete request. Finally, the final confirmation of the order can still be done by the collection staff.

Connection to internal process

The value of automation increases when the information received does not remain only in a chat and is transferred to the internal business process. The order can be entered into a dashboard, CRM or order management system after completing the information, and its status can be tracked from "new request" to "confirmation", "preparation" and "delivery". This structure allows for a more integrated view of orders and makes it easier to manage a higher volume of orders as the business expands.

Lasso digital and local SEO experience

For a sweet shop, visibility in local searches is very important, and the website should be able to clearly provide users with information such as brand name, address, working hours, contact number and services. Google uses structured data for local businesses, and for food businesses, information such as address, opening hours, menu, and contact information can be defined in the page structure. This helps the search engine to better understand the nature and information of the business, although it is not guaranteed that a particular result will appear in the search results.

Site content structure

Along with the smart system, the content structure of Kamand's website should also be designed based on the actual needs of the customer. Product pages, categories of sweets, special occasion cakes, special order, about brand and contact information should each have a specific purpose. The content of each page should also be written for humans and not just filled with repeating keywords. This combination of user experience, local information and technical infrastructure can create a more suitable foundation for a local business's digital presence.

Local structured data

For the home page or the branch introduction page, the actual business information can be put in the appropriate Structured Data format so that search engines have a more accurate understanding of the type of activity, location and contact information. For local businesses, Google recommends using the most specific subtype that matches the business and correctly defining information such as name, address, and other relevant attributes. Also, the structured data must match the visible content of the page.

Designing the conversational experience of the Kamand brand

One of the important differences between a successful intelligent system and a dry chatbot is the way it talks. Kamand is a food and emotional brand, and therefore the system's responses should not resemble the messages of an enterprise software. The tone should be warm, respectful, and inviting, but still provide accurate and reliable information. In designing the conversational experience, Netanik considered the brand personality as part of the system architecture so that the technology remains aligned with Kamand's identity.

human tone

The conversation with the customer should be short, clear and natural, and at each stage only request information that is really necessary to proceed with the order. Excessive use of technical jargon, long messages, or repetitive responses can detract from the user experience. The system should be able to ask the next question if needed and at the same time leave the choice to the customer. In fact, AI should act like a digital sales assistant, not an online form that speaks artificial sentences.

Transfer to human expert

No intelligent system should assume that it can handle all possible situations without human intervention. Complex orders, specific requests or cases where there is insufficient information to answer should be referred to an expert. The system can provide the expert with a summary of the conversation and information received before the transfer, so that the customer does not have to explain everything again. This point of connection between AI and humans was one of the most important parts of the experience designed for Kamand.

data analysis and improving the sales process

Launching an intelligent system is not the end of the project, and data from customer interactions can be used to incrementally improve the process. Examining frequently asked questions, the most frequently asked products, the points at which customers abandon the conversation, and the requests that are most often transferred to the operator will generate valuable information. By analyzing this data, the Kamand team can modify the site content, product information, and even the order process. Therefore, over time, the system does not remain just a response tool and becomes a source for better understanding of customer behavior.

Measurable indicators

To check the performance of the system, indicators such as the number of interactions, the request completion rate, the number of transfers to an expert, and frequent topics can be checked. In the next step, the relationship of this data with actual orders and customer behavior can also be evaluated. The purpose of this measurement is not to produce representative numbers; Rather, it is finding points that improve the customer experience. This approach allows system development to continue based on real data and business needs.

Continuous improvement of the system

If a new product is added to the lasso menu, its information must be placed in the knowledge base of the system, and if the order conditions change, Workflow It should also be updated. This simple cycle shows that a successful AI system is not a one-time project and requires maintenance and optimization. In such a structure, the Netanik team can work on improving the prompts, knowledge base, automation flows, and user experience. The desired result is formed when the system grows along with the business itself.

The result of the Kamand project

The Lasso project is an example of this approach that AI is not necessarily only applicable to large technology companies or complex organizations. A pastry shop can also simplify the customer experience and manage some of its repetitive processes in a more structured way with the right use of AI. In this project, the focus was on building a usable system that would start from answering and recommending a product, and then be able to connect to order registration and internal processes. In this way, technology served the brand experience and not the other way around.

Value created for the brand

For Kamand, the main value of this system was defined in creating a more coherent communication path between the customer and the business. The customer can get the information they need faster and the internal team can have more structured information about the requests. On the other hand, the system architecture has the ability to develop online ordering, customer clubs, personalized offers and customer behavior analysis. This expandability makes investment on AI infrastructure not limited to a small capability.

Nathaniel's role

In this project, Netanic did not see artificial intelligence as a separate tool from brand design or digital experience and tried to bring all the components into a coherent path. From conversational and knowledge base logic design to order automation, user experience and local SEO considerations, each section was defined with a specific purpose. This approach allows Kamand to gradually incorporate the technology into its actual processes and add new capabilities in the future. The result is an intelligent and expandable infrastructure that can play a role in both customer communication and internal business management.

The smart future of Kamand

The development path of this project can continue far beyond the initial response to the customer. Connecting the system to product inventory, online ordering, CRM, customer club and behavior analysis can manage part of the sales process and customer relationship in a more integrated way. At a more advanced stage, an AI Agent can execute part of certain processes with controlled access to business tools and, in critical cases, leave decision-making to human resources. Such an architecture helps Kamand to have a more coherent digital ecosystem instead of using a few tools sporadically.

From order to relationship

The ultimate goal of this system is not just to order faster; The more important thing is to build a better relationship between the customer and the brand. When the right information is provided to the customer at the right time and the shopping experience is simple and personalized, the relationship with the brand can be formed beyond a simple transaction. The data from this interaction can also be used to better understand the customer and design future experiences. In such a model, AI instead of just behind a chatbot It becomes part of the brand's digital experience.

summary

The Kamand sweet shop project shows that Artificial intelligence in customer experience It can also create a functional infrastructure for local businesses. The combination of knowledge base, intelligent conversation, product suggestion, order automation and data analysis can make the path of customer communication with the brand simpler and more consistent. In this project, Netanik tried to align technology with the human identity of the Lasso brand so that automation does not chill the customer experience. The result is an architecture that can start from a simple responsive system and evolve into an intelligent system in the future Order management And the relationship with the customer becomes.