Custom AI: When to Build Your Own Solutions

Artificial intelligence solves user tasks using neural networks - mathematical models that mimic the workings of the human brain. A neural network, as a set of algorithms, is modelled after the human nervous system. It can interpret data, recognise patterns, learn and self-improve. In addition to neural networks, AI uses other tools: Machine Learning, Deep Learning.

Does your business need customised AI-based solutions? We've written about AI agents before. In this article, Emat ltd managers discussed standard and non-standard AI.

Ready-to-use (standard) AI
AI makes many tasks faster and cheaper, automates processes, analyses large amounts of data and, of course, generates revenue. Today, companies most often use off-the-shelf AI systems or tools: chatbots, AI personas, or customer service solutions that can respond to users or customers without extensive development. These solutions help test ideas. They can be used right here and now.

But businesses in complex or highly specialised fields often can't adapt standard AI solutions to their workflows. Or the neural network can't use their own data. In this case, non-standard solutions are needed.

Custom AI
Automating customer queries, handling research questions or analysing complex data sets requires a special approach and more often than not, custom development solutions for specific business objectives, goals and data. Like any customised solution, custom AI takes longer (to develop and implement) and is usually more expensive at the start.

Custom AI solutions are customised for specific tasks and connected to company workflows. When training an AI model, customer data is used to make it better able to cope with specific tasks.

For example, the application for technical tyre inspection, a native mobile development of Emat EOOD it company for Android, allowed the customer to restructure the entire service and introduce AI moderation logic for tyre inspection.

The online telephony platform created by Emat programmers solved the problem of handling telephone calls to the sales, support and service departments of a large trading company. We implemented serial number entry using AI for voice recognition. The platform allows us to track waiting times, connect new phones and third-party software.

Customising off-the-shelf AI tools
Most often, companies use already existing open source AI systems. Such solutions are easier and cheaper than developing from scratch. They are customised for specific tasks - through configurations, pre-training on internal data or integration with other systems.

In industry and business, such solutions are used especially often. Off-the-shelf AI tools are customised and implemented not only in individual processes, but also in key business processes, such as logistics, delivery, warehousing, stock allocation, resource planning, investment strategies, and supply chains.

Building AI from the ground up
If an off-the-shelf solution cannot be found, companies may decide to develop their own AI solution from scratch. This approach is most often chosen by businesses with special operational needs, highly specialised domains or large-scale requirements that off-the-shelf tools can't handle.

In this case, the IT company creates its own model, and the client company can control all processes - from AI training to working in specific industry scenarios.

For example, off-the-shelf computer vision models require deep customisation, and the visual inspection or classification processes themselves can vary greatly from industry to industry. Therefore, customised AI solutions in this area are particularly in demand today, according to Emat development specialists. Such systems are trained to recognise, interpret and understand images or video content tailored to specific business objectives.

Companies often develop their own conversational AI platforms that can maintain a dialogue and provide accurate and consistent responses. Such AI handles customer queries, chat and communication within the company.

In addition, custom AI systems are being developed for tasks such as predictive analytics, anomaly detection, and fraud prevention.

When do you really need a custom AI solution?
Sometimes there is simply no off-the-shelf solution on the market for a particular problem, or the problem is so unique that it needs to be solved despite the costs. In other cases, if the company's data is important, custom AI development becomes part of the strategy and a key element of the business.

A company may benefit from custom AI development if:

• off-the-shelf solutions limit efficiency and don't cope
• do not cope with the required performance or quality.
• available tools require too much tweaking and customisation
• off-the-shelf solutions do not exist
• company data is a strategic asset and requires special protection
• it is necessary to avoid dependence on a single vendor and ensure exclusivity
• it is important to have full control over the customisation and operation of the AI system
• require unique features or integrations that are not available in standard products.
• need special features or integrations with other systems that standard products do not offer