A Guide to Build an AI Solution for Your Enterprise

Did you know Artificial Intelligence has become a part of our life? Knowingly or unknowingly, we interact with AI regularly. In fact, it also exists at our home in the face of “Alexa.” When it comes to the corporate world, AI is already disrupting different verticals. 

Today, AI adoption has undoubtedly become a necessity of the digital landscape. The report says, by 2023, the AI market will expand to 2 trillion. AI lends a hand to enterprises in making informed decisions and boosts productivity and, thereby, profitability. Still, if you haven’t integrated AI into your company system, you are actually lagging behind the competition. 

You should look forward to building an AI solution for your enterprise and implementing it to reap its benefits. Before that, let’s know some details, facts, benefits, and more about this latest technology. 

What is an Enterprise AI Solution?

An enterprise AI solution is crafted and implemented within an enterprise to resolve specific business challenges or make business processes seamless. It arrives with the application of natural language processing, machine learning, computer vision, and more AI techniques to build intelligent systems that can analyze data, automate tasks, and offer insights. 

The customer experience is the chief reason that about 40% of businesses use artificial intelligence. 

The best part about such solutions is that you can customize them per the organizations’ unique needs and use them in various industries, like retail, manufacturing, finance, healthcare, and more. 

Enterprise AI solutions play a major role in advancing and improving the functionality of enterprises. Holding the caliber to process large data volumes and automating routine jobs, such as AI-based enterprise-level solutions, these solutions grease the wheels of the businesses. Besides, it improves operational efficiency and the decision-making process while diminishing the cost and a lot more. 

For example, predictive maintenance systems reduce downtime and maintenance costs, and AI-powered chatbots and customer service tools enhance the customer experience. 

Benefits of Developing AI Solutions for Businesses

Eyeing to the latest statistics by Grand View Research, in 2022, the global AI market valuation surpassed $136.55 billion. And by 2023, it’s likely to expand with a CAGR of around 37.33%. 

Talking about the AI adoption status, IBM reported that around 53% of the IT firms worldwide have paced up the adoption of AI in 2022, which was about 43% only in 2021. 

By 2030, because of AI, the global GDP will reach around $15.7 trillion. 

Clearly, organizations are increasingly developing and including AI solutions in their business system. So, what behind-the-scenes factors magnetize brands and enterprises to adopt AI in their businesses? 

Why Build Enterprise AI Solution?

1. Improved Decision Making

As we have read (above), AI systems assist with prediction, real-time insights, and data-analysis-based recommendations. This way, organizations become capable of detecting patterns and unveiling the striking opportunities, assisting brands in making well-informed and strategic decisions. 

2. Automated Recruitment

What’s the job of a recruitment team? Obviously, choosing a well-qualified and suitable candidate is crucial. When it comes to businesses, they find it challenging and need help hiring the right applicant. 

According to about 85% of recruiters, AI is a valuable technology that will automate and take the place of some parts of the hiring process. 

Here, AI crops up as a savior that eases the hiring process picks the best candidate, tracks them thoroughly, and makes the hiring process run per these protocols. Besides, this way, it bestows the organizations a chance to save money and time throughout the process. 

3. Grab the Competitive Edge in The Market

Organizations get varied new revenue systems, cost savings, and an enhanced customer experience using AI-based systems. In such wise, the companies catch up with the competitive edge in the market before the ones that are still lacking because of not implementing AI. 

4. Cybersecurity

Cyberattacks’ volume and complexity are shooting up that are ahead boosting cybercrime. When hackers gulp a huge data volume from a website or app may be because of a software fault in a cyberattack. 

In the cybersecurity market, global AI was valued at $17.4 billion and, by 2032, is likely to hit $102.78 billion with a CAGR of 19.43%. 

With the increasing adoption of AI tools, cyber-attacks may get nosedived, boosting the effectiveness of cyber defenses. 

5. Smart Pricing

A service or product value estimation is one of the toughest jobs for any business. To emerge with the apt price, it’s important to consider and evaluate varied factors, like manufacturing costs, customer feedback, competition prices, consumer activity, and more. 

With the assistance of AI and ML techniques and a vast data volume, one can build an ideal pricing strategy. 

According to the updates of the Economist Intelligence Unit adoption study, around 54% of financial services firms holding 5k+ employees have already adopted AI.

5. Customer Relationship Management (CRM)

You can improve the effectiveness and productivity of your staff only by combining customer contact management with powerful AI capabilities. This way, it can assist with easy scheduling, appointment tracking, seamless meetings organization, and other manual tasks, for they may demand assistance. 

6. Scalability & Flexibility

AI solutions meet changing business needs, making them highly customizable and flexible. Enterprises are free to seamlessly build and adapt their AI per their needs that change over time. 

7. Improved Customer Experience

AI technologies, like machine learning and natural language processing, can back to build personalized customer experiences. This leads to higher loyalty, improved customer experience, and increased customer retention rates. 

Gartner says various organizations using AI for digital commerce will likely witness a 25% improvement in cost reduction, revenue, or customer satisfaction. 

8. Enhanced Efficiency

AI-powered solutions can help automate routine and repetitive tasks, permitting employees to target more strategic and complex activities. Ahead, you can catch up with boosted productivity, diminished operational costs, and paced-up decision-making. 

9. Rapid Product Development

AI is best at making the development cycle shorter, which ahead reduces the time it takes to design and market for swift ROI.

Considerations for Choosing AI Technologies 

With AI, you can harness the advantages of other technologies that exist within the AI ecosystem. 

1. Machine Learning Algorithms

A type of artificial intelligence, machine learning permits software apps to predict outcomes more accurately without being programmed specifically for that. ML algorithms use past data as input and produce new output values.

Used in a wide range of apps, Machine Learning helps businesses understand their customers. By accumulating customers’ data, machine learning algorithms relate it with behaviors with time and know the links. This way, it assists in customizing product development and marketing approaches to customer demand. 

For example, Google reaps the benefits of ML to emerge with advertisements in searches. 

According to IBM, in 2022, around 35% of brands reported that they were making the best use of AI technology in one or the other way in their business. It’s four percentage points more than in 2021. 

2. Natural Language Processing

The branch of artificial intelligence, Natural Language Processing (NLP), targets backing computers to let them understand and respond to spoken and written language. NLP runs programs that help translate one language to another, like Google Translate. 

NLP holds the caliber to process huge unstructured digital data volume, like reports, forum posts, newspaper articles, and more. It assists in pacing up the process of drawing insights from data and making up-to-date decisions. 

3. Computer Vision

Being dedicated to building automated systems, computer vision in AI interprets visual data as motion pictures or photographs as humans do. Well, the concept behind it is to give instructions to computers and ask them to interpret and understand images on a pixel-by-pixel basis. 

4. Robotics

Robots with AI hold the computer vision that permits them to navigate and access their environment and react accordingly. Through the machine learning process, robots comprehend how to perform the tasks given by humans. 

Technical Requirements to Develop, Deploy, & Operate Enterprise AI Apps

Within an enterprise AI app resides AI/ML models with a common business app logic, unified data image, workflows, visualizations, and integrations into other OS. 

Below are the technical needs for enterprise AI solution development:

• ML Ops Services – This monitors, manages, and controls ML models in production.
• New Tech Stack – It allows standardization, reusability, and scalability across models, data integration, app development, and ongoing operations.
• App Development Tooling – It builds UI, data models, app logic, and workflows faster.
• Polyglot Persistence – It stores, extends, and manages a unified data image. 
• Complicated data integration – It Integrates complex data from varied data types that stay in different data stores. 
• Rich model development tooling – Provides a rich experience with a managed notebook, language bindings, multiple runtime support, and library integrations. 

By 2030, the global AI market is expected to be valued at $1.5 trillion and more, with a CAGR of 38.1%. 

How to Develop an Enterprise AI Solution?

1. Identify Business Issues

Here, you should define the business problem that your AI solution should intend to resolve. Be it operations-relevant, customer experience, costs, or revenue, AI can provide the solution to any challenge your department is encountering. Post that, you need to iron out and align it with your organization’s goals. 

2. Data Assessment

To build an effective and needed enterprise AI solution, data accumulation and assessment hold importance. In this step, data quality, quantity, structure, relevance, preprocessing, and cleaning is done. Every aspect of data is considered and dually checked to train the AI models efficiently. Every essential step is taken, be it data splitting, normalizing, categorizing, and more. 

3. Picking the Right AI Technologies

You can get various AI algorithms and technologies available in the market, but choosing the apt one suiting your business issue is crucial. While selecting the aptest one, you need to consider a few factors, such as data size and complexity, problems to be solved, and the expected outcome from the troubleshooting. Evaluating varied AI algorithms and technologies’ pros and cons will let you make an informed decision. 

4. Crafting Data Pipeline

For the success of AI solutions, the data pipeline makes a difference. It’s a series of procedures that move data from its source to all the possible AI models. Now, it comes to data pipeline designing and implementation. It includes a sequence of decisions around data storage options, data sources, and the processing steps. Considering pipeline designing, security, scalability, and efficiency are key, so you should meet the AI models’ needs. 

5. AI Models Training

The target is to develop and train AI models that can resolve your business issues and emerge with valuable insights. The data we got from the pipelines will help train the models, and with the algorithms, you can generate the predictions. 

Next, you must evaluate the models’ performance, which demands comparing predictions the models generated to the real outcomes and determining the models’ accuracy and reliability. Ahead, if required, you can make improvements and refinements. 

6. AI Solution Deployment

Well, you may call this the final step to developing enterprise AI solutions. Here, you have to integrate the current enterprise systems and the processes with the AI solution you develop. Ahead, you need to ensure that this integration goes seamlessly and offers the expected value to the businesses. 

While you perform AI solution integration, you need to connect the AI solution to APIs, databases, or other systems to let the data and information exchange run smoothly. 

7. Evaluation & Monitoring

The ongoing monitoring and evaluation process incorporates constant performance monitoring of the integrated AI solution. You evaluate how it’s impacting the business and make enhancements and refinements per your expectations. 

Let you know that these refinements may demand data pipeline modification, algorithm updation, or improvement in integration. 

Per a recent survey, around 91% of top firms are reported to have an ongoing investment in AI. 

8. Ongoing Improvement Plan

In this step of enterprise AI solution development, the objective is to make a plan to ensure the AI solution stays dynamic and constantly evolves with time to catch up with the changing business requirements. 

Obviously, with time, businesses need to upgrade with the emerging technologies and use cases being caught up with the light in the AI field. For that, you should attend workshops and conferences, conduct research, or engage with experts in this field. Also, stick up with the goal of making your AI solution better with time and fulfilling diverse business needs. 

How to Implement an AI Enterprise Solution?

The successful implementation of an AI enterprise solution is essential to stop it from going in vain. 

Below are the six ways to implement AI solutions in your organization to meet your expectations seamlessly. 

1. Define Your Goals Precisely

First, start with identifying your business’s issues and how to resolve them using AI solutions. In such a manner, you can establish the objectives of your project and what you want to resolve using your AI solution. Make your project goals best aligned with the latest market trends and business goals. 

You can ask the below questions yourself to get support while you attempt to pinpoint your business challenges and the best suitable AI solution. 

• What are the chief issues your current business model is facing?
• How can an AI solution resolve that problem to benefit your business?
• Does an AI solution hold the caliber to resolve the existing business challenges you are encountering?
• What are the results you expect your AI solution should crop up with?
• What are the chief hurdles in accomplishing these results?
• How will you evaluate your business success with your AI solution?

Surely, the above-mentioned questions will assist you in meeting your business requirements seamlessly and in a better way. 

2. Create Well-Structured Use Cases

Use case development will give you the certainty that your AI solution development is seamlessly achievable. Also, you will get a surety that your project ideation will definitely meet your business needs and resolve the issues you are facing. 

Below is how you can develop a robust AI use case for your business.

• Precise project goals and workable AI app ideation.
• Determine KPIs to evaluate your project’s success. 
• A case owner allotment to manage the development, testing, and validation of the use case. 
• Pinpoint essential AI data to catch up with the objective.
• Within data, recognize unique use cases.
• Evaluate the legal and ethical implications of an AI solution.
• Assess current capabilities and technology to back your solution development.
• Plan for prospects of issues or roadblocks. 

IBM says that in 2022, per the latest AI facts and statistics, approximately 54% of companies already using AI witnessed cost savings and improved efficiencies. 

3. Measure Your Business’s Internal Capabilities

Before you craft a pathway to enterprise AI solution implementation, you should decide on your internal potential being a business to accomplish your goals. 

You can ask the below questions yourself:

• Does your business have the required in-house experts proficient in such a process?
• Or do you have enough budget to outsource the team?

Sometimes, it’s better to integrate a current SaaS or another popular solution for small businesses or enterprises. While other big or mid-sized brands can hire teams to meet their AI project needs or outsource a customizable solution to an enterprise AI app development company. 

4. Data Collection

Your AI model training is essential, so your data accumulation is necessary. AI’s core feature is to learn from the existing dataset and mimic how humans think and act. You can create data for your AI app by following the below steps:

• Crop up with critical data for your enterprise AI solution
• Define data and its source availability.
• Examine, analyze, and build data summaries
• Data sourcing
• Data integration
• Data cleansing
• Data preparation for your AI model

Hire an Enterprise AI Solution Development Company

Whether you build simple software or an enterprise AI solution, you must hire an AI app development company. You should look for the following aspects in your AI development company:

• Experience in developing innovative AI solutions for your enterprises
• Certified teams skilled in managing complex AI technologies
• Cost-effective services
• Provide custom AI app development solutions
• Expert in AI, ML, language processing, and deep learning.

1. Define AI Learning Model

Here we will know if human involvement is needed at the start or throughout the whole learning process of your AI model.

Two types of training models are used in the market:

• Supervised Learning
• Unsupervised Learning

With the supervised learning model, you need to train your machine using labeled class data samples and make them understand the difference between right and wrong. Once your machine reviews thousands of data samples, it’s the model’s turn to start recognizing the patterns themselves. 

The World Economic Forum (WEF) once said that the growth of automation and machines by 2025 would diminish 85 million jobs.

On the contrary, unsupervised learning includes machine learning independently by attempting to identify patterns according to the data provided. In such a case, you don’t teach the machine about correct or incorrect data. It entirely depends on the machine to figure out itself. 

2. Plan Human Intervention

Here, you will decide the role of humans in AI development. Here you will determine when and where your AI model will demand humans’ support for success. 

During the development process, you may need to possess human intelligence experts’ proficiency, and their involvement may rely on your solution’s nature. But, one thing that’s sure about this is you need to identify such points in the starting stage to know the staffing requirements early and catch them. 

By being aware of human intelligence’s role in your AI solution development, you can be sure that your model performs as expected and delivers the desired outcomes. So, you should consider this fact for sure. 

Key Challenges in Developing AI Solutions

1. Get Only the Best Quality Data Sets from Sources

• Problem: Necessarily, for better AI capabilities, the best quality of data should be available. You need to be sure that the data your AI model is getting is accessible, clean, secured, and well-controlled. But, until now, it’s not possible to configure AI algorithms to regulate or avoid inaccurate or low-quality data. 

Possible Solution: With the assistance of AI experts and data source owners, businesses can beat this challenge of AI implementation. 

2. Data Storage & Security

• Problem: Obviously, for an AI solution development, one needs a huge data volume to train the algorithms. With large data volumes, companies can attract numerous business opportunities. But, simultaneously, it may crop up data security and storage concerns. Well, these issues have reached heights globally, as data comes from millions of users all over the globe. 

Possible Solution: Businesses should be sure about a better data management environment for training algorithms and sensitive data for AI apps are considered and used. 

3. AI Integration

• Problem: While implementing AI solutions into your business, first, you should integrate AI into your current systems. You need the AI solutions providers’ support with extensive proficiency and experience. Well, it takes a lot of work to shift to AI compared to only appending new plugins to your existing website. 

Possible Solution: You must consider and secure data storage, input, and infrastructure. Also, compatibility with every AI demand is essential for the seamless operation of the existing systems. Also, once you accomplish the transition, the employees should be trained on how to use the new system best. 

Today, about 3 billion voice assistants are in use, and by 2023, the figure will reach 8 billion. 

4. Proper & Required Skillset

• Problem: Training people or searching for a skillset and expertise in a specific niche for AI implementation and deployment is one of the usually faced challenges. Various organizations don’t even realize its importance, and a few intentionally keep themselves away from seamless AI technology adoption and make the best use of it in automating their processes. 

Possible Solution: Companies should consider spending extra budget on artificial app development training, purchasing and licensing capabilities from giants in the IT industry, or hiring AI development experts. 

5. Legal Concerns

While an AI app development and implementation, you need to consider varied legal issues. The algorithm accumulates data for the users that are sensitive. The data governance systems and erroneous algorithms installed in your AI apps will always lead to incorrect predictions that will bring the company huge losses. Besides, it may violate rules and regulations, making the company get trapped by legal challenges. 

6. Infrastructure

• Problem: AI-powered solutions revamp our lives and provide us access to high-speed internet. Did you know how AI systems grab these speeds? Let you know that it entirely relies on suitable infrastructure and exceptional processing capabilities of a company. However, varied brands still work with outdated infrastructures, devices, and apps to run their IT operations. It’s all because they don’t prefer spending on something other than the expensive requirements of system updates. Instead, they decide not to choose AI implementation. Traditional legacy systems are one of the biggest challenges to overcome by various IT firms. 

Possible Solution: Companies that adopt AI or develop an enterprise AI solution should be all set to take existing IT services to a level ahead, replacing the old and outdated infrastructure. 

7. Computation

The IT industry usually encounters challenges, and because of that, they need to be routinely updated. Compared to other industries, IT has been one of the faster-developing ones. As we have discussed, for developing AI systems, a large amount of data is needed, and to process it, you need to hold computing power. Generally, startups and small-budget brands struggle to finance and reach that computation level. 

8. Rare & Expensive

As mentioned above, AI deployment, integration, and implementation need a specialist, like a data engineer or a data scientist, with the required skill set and proficiency. The biggest challenge here is hiring these experts is quite expensive and, right now, rare in the IT world. Organizations with a small budget usually find hiring a suitable specialist for their project demands tough. Besides, the AI-based system will demand further training also. 

Gartner reported that by 2025, the global wearables AI market size is likely to be valued at $180 billion. 

AI Implementation Success Stories in Varied Industry Verticals

1. Healthcare 

Image Recognition

After text recognition, image recognition has cropped up, an AI-relevant app. 

Infervision is one of the famous healthcare firms that perform image recognition amazingly. It has emerged as a perfect assistance in doctors’ routine jobs. Their product diagnoses lung cancer accurately from CT scans. It also backs radiologists’ work to diminish their workload and prevent them from performing repetitive work.  

2. Automotive 

Autonomous Vehicle Technology

AI success stories mostly talk about self-driven cars; lately, they are grabbing the headlines. Elon Musk first discussed autopilot in vehicles in 2013; in 2015, Tesla launched Autopilot. However, we can’t enjoy all the self-driving functions today, but with time, every year new features are coming out. Currently, autopilot by Tesla is all set to act independently but soon to approach any time. It’s best for highway driving. 

The upcoming leader after Tesla in autonomous vehicle technology is General Motors. In 2017, they launched Super Cruise, which is well-suited for highway driving. They are dedicatedly working on releasing Ultra Cruise, aiming for a hands-off driver-assistance system that frees drivers from giving any input. 

3. Social Media 

Text Recognition

AI’s main application, text recognition, is used by top social media platforms, like Instagram or Twitter, for identifying hate speech, offensive comments, or terrorist language. Using AI, posts including unacceptable content can be effectively and easily filtered without human interaction. 

4. Customer Service 

One of the popular examples of offering satisfying and convenient customer solutions with AI is Netflix’s recommendations. Netflix arrives with a feature customized for each account that chooses and recommends TV shows and movies based on their viewing history. This way, every account receives similar-themed movies and stories. Again this recommendation algorithm depends on AI, with no need for human intervention. 

5. Sustainability 

Wind Power Prediction

Renewable energy, such as wind power, is becoming increasingly essential. This is because it’s not constant, and power stations can’t entirely rely on it. Google popped up with an ultimate solution to predict expected wind power using machine learning. Data based on historical turbine and weather forecasts, one can obtain wind power predictions for the 36 hours ahead. 

6. Retail & E-commerce 

Customized Product Design Recommendation

Nike has already debuted an AI system on their online store that allows customers to design sneakers of their choice. Besides improving sales, it helps collect useful customer data that the ML algorithms use for designing future sneakers and offer personalized recommendations.  

As reported by Juniper Research, Machine learning spending in 2023 will grow by about 230%. 

7. Manufacturing

While maintaining quality and consistency in Cheetos manufacturing, PepsiCo built an autonomous AI solution that boosted efficiency. During manufacturing, sensors detect issues (if any), and next, the trained AI makes recommendations for settings adaptation. 

“The project brought together a powerful mix of technology, applied modeling skills, and subject matter expertise to create innovation on the factory floor,” Dylan Dias, CEO of Neal Analytics, who partnered with PepsiCo on the pilot project. 

8. Financial Services

NedBank built a chatbot, namely, Electronic Virtual Assistant (EVA), to provide easy and quick self-service options to customers. Now, EVA handles around 80% of financial institutions’ inquiries at only a fraction of the cost. 

“The Electronic Virtual Assistant handles 80 percent of the inquiries for which it is programmed at just 10 percent of the cost of live agents. That becomes a significant saving as we roll it out more broadly. Even better, it frees up live agents for exception handling—managing the trickier calls that need live agents. That’s what the Microsoft Bot Framework does for us.” 

Steven Goodrich: Head of Technology, Nedbank Wealth

9. Sports and Fitness

Peloton, a fitness equipment and service store, lets its users accomplish custom fitness goals and needs. While using the Peloton bike for a test ride, they get fitness suggestions depending on the information they receive. With the PowerZone numbers showcased on screen, the users get the motivation to push themselves towards their fitness goals. On their personalized profiles, they get information about the classes they are conducting with strong recommendations and their progress meters. 

Cost to Develop Enterprise AI Solution

The cost to develop an enterprise AI solution relies on varied factors, like your project’s scope, complexity, and specific needs. However, we can’t give a fixed cost but can put forth an estimate. Custom AI solution development costs may range from $60000 to $300,000 per solution. 

You can hire an AI app development company holding years of experience and a good portfolio. Besides, there are more factors you should consider while choosing a partner for your AI solution project. 

Why Choose Appquipo for Enterprise AI Development

Holding a proven track record in developing custom enterprise AI solutions, Appquipo is a leading AI development company usually chosen by worldwide clients. Below are some reasons that will affirm our words. 

1. Domain-Specific Knowledge

Whether it’s finance, manufacturing, healthcare, transportation, or any domain, Appquipo has worked with clients belonging to almost all industry verticals. The team has industry-specific knowledge that allows them to grasp a better understanding of exceptional business needs using which they develop custom AI solutions. 

2. Proficiency in AI Technologies

Experienced data scientists, AI developers, and machine learning engineers at Appquipo stay updated with the latest AI technologies and frameworks. This assists them in cropping up tailored AI solutions that make the clients stay ahead of the curve. 

3. End-to-End AI Development Services

Appquipo provides complete end-to-end AI development services, including data exploration and analysis, AI strategy consulting, model development, testing, deployment, and maintenance. From development to implementation, the experts ensure everything goes well regarding AI solutions implementation in the organization. 

4. Customized AI Development Solutions

The AI professionals at Appquipo know every business holds unique needs, so one-size-fits-all solutions will not go with all. It demands a tailored approach to designing and developing custom AI solutions aligning with the business budget, timeline, and objectives. 

5. Data-Driven Strategy

Appquipo considers using data analysis and insights best to drive successful AI model development. Moreover, our team follows advanced data analytics techniques for data analysis, pattern identifications, and predictive and prescriptive model development that can drive meaningful business results. 

6. Agile Development Methodology

We follow an agile development mode that leads to iterative and incremental AI solutions development. This ensures the AI solutions are developed in a collaborative and flexible way, considering regular updates and feedback to align the solutions with the changing business needs. 

7. Quality Assurance & Support

We lay extra emphasis on the QA and offer ongoing support services for AI solutions we develop. Our team conducts rigorous testing and validations so that the AI models we develop are accurate, reliable, and perform the best. 

Final Note

While wrapping up, we would like to advise you if you are also seeking to develop AI solutions for your business that may be effective for your customer service, enhance your business process, boost productivity, and a lot more; it’s better to start right here. There’s no need to wait for an apt time, as every time is better to invest in an AI enterprise solution. 

By 2025, the global AI software market is predicted to experience a rise in the upcoming years and will reach $126 billion.  

While you take your plan ahead, be sure that you consider every essential aspect of your journey to develop a custom AI solution. Undoubtedly, you will get a hit with the idea of conducting all the steps yourself, but let you know that besides this job, there are a plethora of things you need to look at while running your business on the right track. 

So, for this, you can take the help of an AI solution development company holding a team of expert data science professionals and field-expert software developers. The firm will assist you in developing a competitive AI solution following the best practices aligning with your business requirements. 

Contact Appquipo today to discuss your business needs and bring your project vision to catch up with reality!

 

FAQs on Building AI Solutions for Enterprises