AI Consulting Services

AI or artificial intelligence is a branch of computer science that focuses on designing and creating machines that imitate human intelligence and react and think like humans. The biggest benefit of AI is that it can process a substantial amount of data in ways that we cannot. And if we provide it with sufficient data, it can make decisions, recognize patterns, and even make human-like judgments.

Today, AI lies at the heart of applications like image classification and recognition, and numerous prediction systems, such as traffic light systems. From self-driving cars to AI chatbots like Google’s Bard, Amazon’s Alexa, and Apple’s Siri and generative AI tools including ChatGPT, DALL-E, and Midjourney, AI has now become part of our daily life.

AI consulting involves working with companies to understand how AI can be applied to their industry and coming up with a strategy to implement it. Its primary purpose is to break down the barriers to AI adoption due to limited access to expertise and the high cost of making AI applications.

AI consulting services help companies unlock the true potential of AI and how it can drive growth and improve operations. It’s also needed because:

• AI is not only complex but also constantly evolving, making it difficult for businesses to understand how they can implement it for their use case.
• A lot of businesses fail to leverage the true potential of AI or aren’t aware of how they can apply it to their specific industry.
• Implementing AI isn’t just expensive—it requires a significant amount of time and resources, and to ensure businesses get the most out of their investment, they need expert guidance.

At AI Superior, we leverage a robust AI engine and advanced algorithms to provide valuable insights and ensure transformative outcomes.

An artificial intelligent consultant is someone who helps businesses understand AI and provides them comprehensive guidance with the implementation, maintenance, and optimization of AI-based solutions. These professionals work with both big and small businesses and perform a range of tasks, such as:

• Assess the potential of AI for a company
• Identify and determine how AI can drive growth and improve operations
• Develop a comprehensive strategy for AI implementation based on the company’s needs
• Help select, integrate, customize, and deploy AI models
• Assist with optimizing AI solutions for improved ROI and performance
• Provide continuous support and maintenance for AI systems
• Manage different AI-driven projects
• Develop future strategies for businesses to expand

AI consulting activities can be broadly divided into five categories:

Strategy development: Strategy development activities involve assessing your company’s readiness for AI adoption and coming up with a plan of action. Artificial intelligence consultants typically have meetings with clients to determine areas for AI integration and list tools needed for adoption. The goal here is to make the necessary data pipelines and IT infrastructure to integrate AI with the company’s existing systems and ensure the development of AI systems that ensure positive user buy-in.

Proof of concept: Determining the feasibility and impact of an AI solution before implementing it on a larger scale is essential. A lot of AI projects are eventually abandoned due to a lack of assessment and evaluation. This is why our AI consultants test a small-scale system first to help you choose a solution with commercial viability, reducing the risk of failure.

Implementation: Once a plan is in place and an AI solution is deemed viable, the AI consulting company works to implement the project. This involves building the necessary framework, selecting and customizing the model, and integrating the AI solution with the existing system.

Optimization: Artificial intelligence consultants continuously optimize the deployed models to improve the quality of results to ensure the highest ROI.

Maintenance: Maintenance activities typically involve regularly checking the systems for errors and upgrading features to ensure businesses provide top-notch services to clients using AI.

The price of AI consulting can vary, depending on the scope, requirements, and complexity of the project. Other factors like customization needs, data availability, and integration requirements also influence the price. While we can’t provide an exact figure upfront without knowing the project, our AI consulting services generally start from a few thousand euros and can go up from there depending on the nature of your project. We have a transparent pricing model and offer flexible pricing options along with fixed AI development plans for startups.

The AI project lifecycle is a structured approach for the development of AI solutions that follows the existing software development cycle while addressing the unique challenges that AI/ML projects present. The life cycle comprises several phases, each with its own goals, purpose, and quality assurance criteria, which must be met before the project can move to the next stage. The stages of the AI project life cycle are:

Requirements collection & analysis: This stage involves understanding the problem and how an AI solution can help. The requirements, objectives, and constraints of the project are covered in this stage.

Data analysis, acquisition, & preparation: The team analyses existing data that’ll be used for training and evaluating the AI model. Data collection, cleaning, and preprocessing pipelines are built to ensure data quality.

Model development: This stage involves choosing the right algorithms, configuring parameters and hyperparameters, training the model using the data prepared in the earlier stage, and experimentation to get the best results.

Prospective validation: In this stage, the model’s performance is assessed to ensure it meets the predefined criteria and the desired level of reliability and accuracy.

Model deployment: The developed model and the data pipeline are then integrated into the target environment (customer’s processes). This may involve a range of activities, including integrating the model with existing legacy systems, ensuring scalability, testing the integrated system for proper functionality, and fixing any issues that might occur.

Retrospective validation: The model is tested and validated once again in the production environment to ensure it meets the desired standard of quality.

Monitoring & optimization: The AI model is constantly monitored for its performance and to ensure there are no anomalies. Improvements and updates are done regularly to enhance the model’s capability and ensure it meets the changing business needs.

The time it takes to finish the AI project lifecycle depends on a number of factors, including the availability of data, the complexity of the problem you’re aiming to solve, and the scalability you’re looking for. It can take anywhere from several weeks to a few months—we can give a better time estimate once we know more about your needs.

Data is essential for any AI project—it’s necessary for training models and making them perform specific tasks like predicting things and learning patterns. Without data, it’s impossible to train an accurate and well-performing AI model that meets your needs. Theoretically, you can use realistic randomly generated data to make a prototype, but the relevance, quality, and quantity of data greatly influence how well an AI system performs. And if you want to successfully incorporate and implement AI into your business, sufficient high-quality and relevant data is essential.

The data needed for AI depends on the nature of your project and the objectives you want to achieve. For instance, you’ll need text data for NLP, image data if your project involves computer vision or sensor data for IoT applications. Regardless of your use case, the data must accurately reflect real-world scenarios that the AI will eventually come across to get a highly reliable model.

Typically, the data you need can be divided into four categories:

• Training data that represent the problem domain. It should be labelled to indicate the desired outputs.
• Validation data used for fine-tuning and evaluating the AI model. It helps optimize the model’s accuracy and assess its performance.
• Testing data used for evaluating the trained model. This dataset should be different from the training and validation data to ensure an unbiased assessment of the model’s performance.
• Production data that the deployed model uses to perform its tasks in a production environment.

At AI Superior, we take care of data preparation for you. We help you gather and organize the data, clean it to get rid of errors and inconsistencies, and preprocess it to make it more suitable for developing an AI model, so you don’t need to worry about going through all that hassle. With our data enablement services, we can help you harness the true power of your data, allowing you to successfully meet your goals.

You can know if you’re ready to adopt AI into your project by first taking out the time to analyze what AI really is. Then, analyze the problems you’re currently facing and how AI can help to solve them. Once you have a list of ideas, use artificial intelligence consulting services to analyze your ideas and pick the best one that efficiently solves your problem. Your consultant can then help you bring your idea to life.

Today, AI is used for a range of applications, but it is mostly used for expert systems for AI-driven decision-making and predictions, AI-powered chatbots that leverage NLP and speech recognition, and smart systems that use machine vision.
Businesses, in particular, have turned to AI for forecasting for tasks like workforce planning, inventory optimization, medical diagnostic applications, and sales. Using AI for decision-making and prediction allows businesses to identify opportunities and understand customers’ behavioral patterns and needs.

Similarly, most companies already used some kind of non-AI chatbot to provide assistance to customers, but conversational AI chatbots take customer service to a whole new level. They ensure increased responsiveness and provide a more personalized experience by leveraging NLP to understand us and provide human-like support. Both large and small companies, and even banks, use AI chatbots and assistants to better serve customers.

Other applications of AI include AI for marketing where companies use the technology to get more out of their budget, AI in the banking industry for fraud detection and prevention, AI-powered assistants, AI for autonomous vehicles, and AI for processing to help companies process data quicker and more efficiently.