How to Create an AI App
Not pretending to cover all the nuances of planning, developing and testing AI applications, we think that both experienced developers and beginners will find useful positions in the long-form instructions and examples. The range of application technologies based on the usage of neural networks is huge: from a simplified informer bot to an application equipped with functionality for planning the volume of trade operations, delivery, calculation and forecasting of profits, controlling employees and interaction with customers. Some successful examples of AI implementations that have started with minimal investment include Grammarly and Duolingo, the Waze and Canva services, and the FaceApp photo editor.
Typical ML machine learning replaces the operator at the beginning of communication with the support center, clarifying basic questions. When VCAs are involved in a deep format, requests are personalized, and contact security is increased thanks to the speech recognition and psychological status of customers. The automation of current tasks like searching for tickets, ordering goods, selecting waypoints is a part of virtual operators' functions. For this reason, the choice of ML or VCA depends on the issues to be solved.
AI's NLP capabilities provide initial search for employees and identify their professional skills. In the process, AI HR recommends changes to staff job descriptions if it sees progressive skills assimilation and automaticity, which promotes career growth.
The first two phases for planning organizational and technological operations for the development of an AI application are a fundamentally sound program with several steps. The diagram clearly shows that the first part includes problem formulation, tool selection, required resources, expected costs and profits. The second step is responsible for the formation of validated and accurate databases ready for model training.
This is how AI development company of cross-platform applications start to work. The chain “requirements - goals - vision alignment - unified style” is thought out according to the SMART structure and categorized step-by-step in Scrum or Agile. Objectives and resource availability determine what scope of services and goods can be provided in the planned mode and reduced or expanded in case of scarcity or funds abundance.
Common Crawl, platforms like Kaggle or AWS provide databases that have been checked for accuracy, informativeness, repetition and error-free content in the event of a digital and graphical scarcity of source material. To check your own database, run through the Tibco Clarity utility (launched in 1997) or OpenRefine software.
Chameleon model action type modal data processing brings AI closer to the paradox-exclusive format of human reflection. Autoregression using the 34B protocol has been trained on 10T data tokens, so the multimodality model ensures generation of content and pictures with realistic parameters.
Afterwards, the model by applying the annotated database with 4D masks performs segmentation and elaborates situations in detail within a certain time range. This is similar to storyboarding a movie where the director distributes scenes and events minute by minute. The PSG4DFormer model predicts the creation of masks and subsequent development on a timeline. Such components are the basis for generating future scenes and events.
In Featuretools library, features for ML models are developed automatically: for this purpose, the variables are selected from a database to become the basis for the training matrix. Data in time format and from relational databases become training panels during the generation process.
When physical storage and deployment environments are scarce, SageMaker, Azure, and Google cloud solutions are used. Julia is one of the most popular new languages for generating AI applications: when using commands written in Julia, more than 81% of commands are executed quickly, accurately, and with minimal errors. JavaScript and Python, R also show good results with 75+% accuracy.
In the application stack we add JupyterLab environment, NumPy library for multidimensional arrays or a simpler variant Pandas. Dask library is designed for analysis of large databases with clusters, visualization and parallelization, integration with environments and systems to reduce hardware maintenance costs.
Asynchronous and fast FastAPI operations make the framework preferable to Django, where on servers the WSGI standard needs to be configured to the new asynchronous ASGI. Due to the interface being 6 years old, it has limited data capacity for JWT tokens and S3 storage. We take into account that asynchronous libraries often have problems with unreadable information and sometimes we have to do writes by invoking execute() after passing the SQL query and materials. Note: the root_path attribute is not changed to “/api”, which is inconvenient.
Deployment tools include the proprietary PaaS platform Heroku, the more sophisticated Elastic Beanstalk, and Qovery, which takes the best of both resources. For testing, they use:
| Model Architecture | Assignment | Special Features |
| Convolutional (CNN) | Video and images | Accurate identification, elimination of noise and errors |
| Recurrent (RNN) | Digital data and language | Sequence processing |
| General adversarial (GAN) | Generating new data and pictures | Simulation with generation of new data, as bases for training |
Afterwards, the AI model training is fine-tuned in a filigree. If the scenario includes high requirements with precise parameters, training continues with observation - such conditions are more expensive. To find artifacts and patterns in clustering, it is preferable to opt for self-training. For projects in robotics and simple tap to earn games, reinforcement (encouragement or punishment - the “carrot and stick” method) is used.
In order for the model to perform productively in real-world scenarios, we need to assess correctness and speed of response. Therefore, the measurement parameters include precision and repeatability, ROC-AUC metrics, where there is no need to cut off the threshold (for an unbalanced database), F-score, specifying the proportion of positive solutions, MSE mean square error and R-squared coefficient of determination. An error within 5% is considered acceptable; when reduced to 1 and 0.1%, the result is considered highly accurate.
In IoT, peripheral operation on the device is preferred as it preserves privacy and provides fast performance. At the core of IoT is data generation, the essence of which is the convergence of AI with IoT. This synergy builds up the functionality of the two parts, giving birth to AIoT. However, to enhance the power and scalability of the functionality, it is better to apply cloud-based technologies using embedded API protocols. If it is important to hear customer feedback (convenience, clarity, speed), we build in a feedback function.
Updating the AI model is a necessity to avoid “drift” when the underlying patterns become outdated and the accuracy of the response decreases. Therefore, iterative testing extends the model's lifecycle. Automated unit testing, periodic integration testing to evaluate the aggregate performance of individual functions, and UAT acceptance testing are the three mandatory “whales” of performance evaluation and testing.
It clearly democratizes and simplifies business process transformation when users themselves develop and deploy AI models for marketing and production logic and workflows without writing code. For example, Flow's seamless integration dynamically selects the right data and prepares AI solutions based on it.
Meanwhile, the Neumann bottleneck (VNB) problem must be overcome so that the processor can't wait for RAM to provide access to the process. The goal is to increase the speed of data retrieval and transfer from the database or storage. Even the high speed of multi-core processors with 32-64 GB or more of RAM may not justify the investment in capacity when the transfer of information from the cloud is limited. To solve the VNB problem, they expand the cache, introduce multithreaded processing, change the bus configuration, supplement the PC with discrete variables, use memristors and compute in an optical environment. In addition, there is also modeling by principle of biological processes such as quantization.
Digital AI paradigm in parallel processing increases energy consumption and time of learning processes. For this reason, qubits in superpositions (multiple positions in one time period) and entanglement positions are preferable to classical bits, provided that stability is preserved. For AI, quantum technologies are better due to the reduced cost of development and data analysis in multiple configurations. “Tensorization” compresses AI models and enables deployment on simple devices while improving the quality of the raw data.
Pattern poisoning (introduction of malicious elements) in AI, presence of adversarial vulnerabilities lead to misclassification. This is why a holistic approach should include protection principles from the development stage to testing and deployment to minimize challenges and risks.
NLP principle is introduced into the algorithms when planning the AI's work as a psychologist - natural speech is analyzed and the patient's psycho-emotional mood is clarified. Then the questions are answered with a generated answer that is close to human sound and intonation. There are also genetic algorithms, when bots are created to solve millions of problems and then the worst ones are cut off, leaving the best ones. The combination of successful developments and the subsequent generation of new adapted and tested models, based on the predecessors and a number of iterations, leads to a complete solution of the problem.
AI program development should be a creative approach. For example, you can make a chatbot in the form of a funny animal or bird, a funny elf or a spirited plant, or something pragmatic like an arbitrage bot for trading. Those who read Kurt Vonnegut remember the story about a supercomputer that acquired human thinking. Therefore, if the character will voice lines, using previous communication, give tips and short press releases about new products, customers will love and get used to AI, will trust. Sales growth will be at least 10-20%.
To identify financial and time costs, contact Merehead with your task and questions: AI development cost starts from $20,000 and takes up to a quarter in terms of time. Application development time for medium complexity applications with logic chains at three to five levels is twice as long and the price reaches $100,000. For complicated mathematical projects with expert analysis and 99.9% accuracy of answers - up to $500.000. Let's develop a project roadmap and plan the expected profitability results before starting work.
