Automated AI dev: building an MVP in 1 month with ValueXI

Our approach: automation of typical elements

While the topic of Artificial Intelligence is no longer as mysterious as it was a decade ago, businesses still grapple with questions like, "Where do we begin with AI implementation?" and "How can we quickly test hypotheses and assess the implementation feasibility?"

Off-the-shelf solutions often lack the necessary flexibility and intuitiveness. While developing from scratch can be time-consuming, requiring experienced specialists and significant investments. Moreover, both approaches come with numerous pitfalls along the development path that can derail the project.

At WaveAccess, we advocate for a hybrid approach that combines the strengths of the custom development concept with the use of ready-made solutions. At its core lies the automation of typical elements of AI development, enabling us to implement an MVP into business processes within just 1 month with further customization for specific tasks.

Advantages of the hybrid approach to AI project development

AI project development cycle

A standard roadmap for implementing any Machine Learning / AI project includes ten stages: from assessing data quality to launching and supporting the solution. This journey is not straightforward and comes with many nuances that the team must keep in mind constantly.

Structure of Machine Learning project development

The cyclical nature of development, as depicted in the scheme above, characterizes the iterative nature of such projects, which is associated with the unpredictability of working with data and AI. However, during the initial iteration, standard approaches are almost always used, overlooking the specifics of the particular task.

It is precisely at this stage that an excellent opportunity arises for automation, as a result, reducing time and costs for the development and implementation of an MVP.

Routine work on an AI project

Typical elements of AI development, encountered universally regardless of the project's specifics, industry, or data itself, include:

• Recurring data preprocessing chores: filling in missing values, encoding categorical variables, scaling features
• Tasks prone to repetition: hyperparameter tuning, model training, validation
• Continuous monitoring and updates: ensuring model relevance, guaranteeing solution efficiency

While these actions are standard, they require significant resources and specialized knowledge. Even for an experienced Data Science team, these processes consume a lot of time and financial resources, increasing the likelihood of errors. In the absence of specialized expertise in AI development, costs and risks significantly increase.

Identifying patterns

Encountering these routine tasks repeatedly prompted us to analyze all accumulated experience in working with AI and the best global practices in this field. We discovered clearly defined problems, methods, and mistakes that occurred regardless of the industry or project scale.

The need to address these issues from project to project led us to first create a set of utilities and then an engine to speed up the development process of typical solutions using Machine Learning. This approach has significantly reduced the development time of AI-based projects while maintaining high solution quality.

The next step was to automate model deployment into production, create a user-friendly interface, and provide a range of tips to achieve optimal results. This is how the ValueXI platform was born.

Journey to automating AI development

From idea to integration of a ready-made model within 1 month, in the cloud or locally

ValueXI is a customizable low-code platform that accelerates the development and implementation process of an AI project MVP for any business. This is possible thanks to the automation of many tasks related to data processing, model training, and deployment of Machine Learning models. It is a more flexible and reliable tool than off-the-shelf solutions, as well as faster and more cost-effective than developing from scratch.

With this solution, we help companies embrace Artificial Intelligence, allowing them to create an MVP in just 1 month, significantly saving resources on implementing AI into their systems, and reducing associated development risks.

ValueXI implementation scenarios:

Scenario 1: Custom solution from scratch

Develop a full-fledged AI-based solution tailored to your business processes at a 2-3 times faster development pace, significantly reducing costs compared to engaging a dedicated team of multiple data specialists.

Scenario 2: New AI product feature

Easily integrate ValueXI into existing solutions, enhancing their capabilities and providing added value. Flexible configuration allows scaling resource usage based on data volume.

Scenario 3: AI as a development tool

Thanks to the short development and hypothesis testing cycle, you can achieve faster data preparation and analysis for model training, seamlessly integrate AI models into your environment, and deploy new features quicker than competitors.

Additional module: Data enrichment for AI model

In any scenario, expand model capabilities with a pre-trained GPT model (compliant with data confidentiality requirements), a large language model (LLM) management module, and a document management (OCR) module, either within your organization's perimeter or in the cloud.

Extract value from data and make business processes more efficient

Automation with ValueXI

While each AI project is unique, they all entail common repetitive tasks that can be automated. Despite the inherent uncertainty and risks in the AI domain, results can be attained faster, cheaper, and more efficiently. Our experience underscores that automation and a platform approach are pivotal success factors, regardless of the data or industry.

This approach, alongside ValueXI, continues to demonstrate its effectiveness through real-world examples across various industries.

Case study

For instance, with ValueXI's assistance, we automated and optimized the processing of incoming requests, reducing operational costs for an engineering company.

Our client received approximately 3000 equipment repair requests daily, and manual processing inevitably led to prolonged customer waiting times and documentation errors. Additionally, about 20% of requests marked as warranty repairs actually required non-warranty repairs, resulting in missed revenue.

The text mining module developed and integrated with ValueXI addressed these issues. It automatically predicts repair types based on request text, with an accuracy ranging from 80% to 98%, tracks correspondence between predictions and assigned categories, efficiently distributes requests into subcategories, and corrects errors in data.

These changes significantly accelerated request processing, reduced error risks, cut operational costs, and optimized the use of company specialists' time.

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Join us for a demo ! We'll walk you through how ValueXI works with data, trains models, and integrates them into business systems.