Tuesday, midday. I am once again swamped with tasks and again notice that my stomach is empty, only when it starts to remind me of itself. I notice, but understand that now I need to come up with what to eat, and this starts to give me a headache. I keep falling for this hook every time, but I can't do anything about it: I don't have the time or desire to plan my meals in advance. And, to be honest, I'm not alone in this
And I am also a potential target audience for an application that solves this problem. I just don't know or think about it (and probably won't).
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Instead of me, on this very Tuesday or another, someone on the other side of the world will wake up with the idea for this application. Maybe their own experience will prompt them to think about it, or maybe it's innate genius. Who knows how ideas work: there are countless variations. Sometimes the idea may be very raw - the main thing is to find someone who will develop it into something meaningful.
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This is how our client described his vision of the future product. He had long contemplated this idea, mentioning it each time he tried to eat properly, and each time realizing that this description lacked details.
The irony is that he didn't need the details in the end.
Working with ideas in the form of examples of how the product will potentially be used is not a problem for us," comments Konstantin Semenenko, founder and CTO of ManagedCode. "We don't force clients to write long specifications: working on the technical task (TT) is on our side. The main thing is the idea, and we make it happen.
One could say that the collaboration takes place in the format of "the client gave the idea - received the product." Of course, everything is agreed upon, but there is no need to work out all the details.
Usually, the work starts with gathering requirements from the client, followed by creating an approximate UI/UX to bring the product vision closer to reality. After the design is approved, the development task is broken down into separate tasks: priorities are determined for them, and an estimate is made.
Our goal is primarily to develop features that can be monetized so that the client can start earning as soon as possible.
1. Discussion;
2. UI/UX design;
3. Architectural design;
4. Task definition and prioritization;
5. Development.
Moreover, within each stage, its own iteration cycle may occur.
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For the AI-Powered Food Delivery Copilot, we didn't immediately settle on the application interface, only stopping when the client was completely satisfied. This required several brainstorming sessions, accompanied by endless cups of coffee, but it was worth it.
On the other hand, there were no problems with architectural design. Regarding technologies, ManagedCode typically uses the Microsoft stack, Azure, C#, and Semantic Kernel for all projects - their main advantage is security, which is especially important for applications with AI integration.
In the development of our AI-Powered Food Delivery Copilot, we faced a challenge: how to understand vague user requests, as they often don't know exactly what they want? The answer we found lies in the use of Semantic Kernel, an innovative tool that allows our AI agents to interpret human desires and translate them into specific actions.
Semantic Kernel helped us create a system that utilizes plugins and connectors to gather data from various APIs, such as restaurant databases, menus, and review systems. This enabled us to integrate diverse sources of information and use them to make informed decisions about what to offer the user.
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Thanks to Semantic Kernel, our agents can analyze user requests, such as "hey, order me something healthy and ready in no more than half an hour," and transform them into specific orders, taking into account individual preferences and time constraints.
Moreover, Semantic Kernel provided us with the ability to easily adapt our AI models to changes in market demands and technologies, ensuring high flexibility and scalability of our solution. This means that we can quickly implement new features and enhancements, which is critically important in the fast-paced world of technology.
As a result, our AI-Powered Food Delivery Copilot not only fulfills orders but also provides personalized recommendations, significantly enhancing customer satisfaction and service efficiency. Semantic Kernel allowed us to transform vague user desires into concrete actions, making our application not only intuitive but also extremely convenient for everyday use.
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The development of the MVP project took about three months, which is an average time for such an application. However, we also offer a package of 20 hours of any of our specialists' time for prototypes and idea exploration. This is useful if you need to test whether the model can achieve the desired results at all.
In this project, research took a little time, as we immediately understood which model would be best suited for such a task.
As a result, we have a fast and functional AI-Powered Food Delivery Copilot, which allows restaurants and delivery services to continuously monitor and improve their operations, solving potential issues before they affect customer satisfaction.
By the way, we are currently seeing significant demand for AI integration in applications. So it's time to hop on this automation train. And as for whom to turn to for development, you already know 🙂
