How Prototypes Save AI Startups Money

The progression of artificial intelligence, despite holding the potential to revolutionize numerous sectors, often comes with significant expenses and resource requirements.

Everything from onboarding first-rate professionals to securing advanced computing resources can make AI development an intimidating task for firms with limited resources.

AI startup is not a regular startup

The resource constraint issue is particularly acute for AI startups: a substantial number of them lack the necessary funding to experiment with a variety of AI models, explore diverse technologies, and repeatedly test these models to reach the requisite project accuracy.

Having collaborated with a multitude of AI startups, we've observed their journey from inception to a functional product. Through each iteration, we've gleaned insights into where AI startups frequently end up losing funds. The culprits aren't as evident as one might initially assume.

Working with AI is as complex as it seems

In the context of AI startups, the unique nature of the product introduces a heightened level of risks and potential financial pitfalls. Despite all the breakthroughs in the field of artificial intelligence and machine learning, forging an AI product is largely a speculative venture: there's never absolute certainty about the product's effectiveness or whether it will justify the invested money and time in the long run.

The underlying reason for this uncertainty is rooted in the characteristics of the technologies employed. Machine learning products hinge on machine learning models, which need to undergo training to process data. There's virtually no assurance that a trained model will exhibit strong performance once deployed in the real-world environment.

The trials of AI development

Many AI startups run into the following issues:

• Over and underfitting
• Issues with data quality and bias
• Lack of feature engineering
• Improper model selection
• Incorrect problem framing

None of these challenges can be readily addressed by simply retraining or substituting one machine learning model for another.

Each trial not only drains financial resources but also consumes more time, chipping away at a project's budget, which is typically constrained for startups.

Strategic planning and formulating a roadmap are often touted as crucial steps for any startup.

Although this idea holds merit, it's not always straightforward to put it into practice, especially for a startup aspiring to incorporate AI technology.

Where AI startups loose money

This is the juncture at which we observe most AI startups encountering difficulties. Planning with multiple unknowns is difficult.

We frequently see startups plunging directly into development without prior testing of the concept or creation of prototypes. This approach could be viewed as an inefficient use of resources that would be more beneficially allocated to actual product development.

Is prototyping the answer?

The step of prototyping is often viewed as optional, yet based on my experience, I would argue that a staggering 90% of AI startups truly benefit from crafting an initial prototype:

• Prototypes serve as a feasibility check for ideas since not every concept can be realized with the existing state of AI technology.
• Prototypes present the sole method to verify the accuracy of a machine learning model. It's critical to evaluate the maximum accuracy attainable with a specific model and dataset before plunging into full-blown product development, thereby saving time and money. Not all projects can reach a high recognition accuracy level due to factors such as model choice, hardware utilized, or the nature of the subjects.
• Prototypes prove to be effective tools to attract investment as they can be easily showcased and demonstrate your product's core functionality.

Once the decision to create a prototype is made, the subsequent step is to select the platform. In our work developing prototypes for AI startups, we typically opt for either a web-based prototype or a chatbot.

If you are interested in creating a prototype for your AI project, we offer AI prototyping services. We do CV, NLP, web and chatbot prototypes

Web Prototype

A web prototype is an excellent choice for more intricate AI projects that aim to display not just the machine learning model, but also its interplay with other components, such as live video feed and user interaction, among others.

Chatbot

Chatbots are ideal for projects where the AI model and its capabilities form the crux of the functionality. The interaction with such a prototype resembles an online chat in which the user submits an image, and the chatbot returns with recognition results, supplemented with additional details of the object detection and recognition process if necessary.

Popular messaging platforms like Telegram support the creation of custom chatbots boasting comprehensive functionality within a brief span. The inherent user experience/user interface (UX/UI) capabilities permit the use of these bots for marketing purposes.

Summing Up

• Prototypes are not an optiol step for the majority of AI startups
• Prototypes serve as important tools for model testing, idea validation, and attracting investors
• Web prototypes are great for more complex AI projects, while chatbots are perfect for AI-focused CV and NLP projects