According to data from the analytical platform Statista, large insurance companies in Europe and America always keep an eye on trends in development and technology.
That is why Diceus makes every effort to help the insurance sector meet its needs for high-quality development.
Here are a few tips on what to look for:
How to find a team of app developers
The purpose of a mobile app is to be convenient and useful for users, as well as to help you stand out from your competitors in the market. This requires competent service development.
How do you choose a contractor who will create a product that is profitable for your business, fast, and, most importantly, high-quality? We have compiled a short checklist of things to look for when choosing a contractor:
1. Developer competence
Don’t forget to find out who exactly will be working on the app. It’s good if the company can provide a team working on different technology stacks and programming languages. It’s also good if they have relevant development experience in their portfolio. This allows you to correctly select the basis for creating the app and form a project budget.
2. Company standards, philosophy, and mission
Effective work is facilitated by the contractor and client company sharing common values and approaches to problem solving.
3. Cost of services
It is important to understand that professional services cannot be cheap. IT product development requires resources — time and money.
4. Company reputation
When choosing a contractor, it is important to consider not only relevant experience and guarantees of results, but also a good reputation in the IT market. This also guarantees data confidentiality when working with a contractor.
As we can see, each stage is important for achieving results.
It is important to note that all processes are up to date and prepared in accordance with AI technologies.
Stages of AI implementation
To implement artificial intelligence as smoothly as possible and identify key issues at an early stage, you can follow this algorithm:
1. Pilot project
To start with, choose a single task that requires a small investment, such as automating responses to frequently asked customer questions, and develop a strategy for solving it using AI. You can then evaluate the results and scale up the application.
2. Data collection and preparation
The quality of training data is the foundation of AI effectiveness. Information must be structured, formatted correctly, and cleaned of noise.
3. Platform selection
This can be popular existing paid and free services or a model developed and trained from scratch.
4. Team training
Employees need to be trained so that they can work effectively with the new tools and do not choose the more familiar way of performing tasks manually.
5. Monitoring and optimization
After implementation, it is important to monitor and adjust models to changing conditions.