How to Build an AI App? Steps, Features, Costs, Trends

chirag November 21, 2024

Table of Content

Understanding the Core Components of AI Applications
Core Components of AI
How to Make an AI App - A Step-by-Step Guide
Choose the AI Model
Choose the Right Tech Stack
Collect and Prepare Data
Train the AI Model
Design the UI/UX
Develop the App
Integrate the AI Model into the App
Test the AI App
Deploy and Maintain
Game-Changing Features of an AI App
Personalized Recommendation
Chatbots and Voice Assistants
Auto-Correction and Completion
Predictive Analytics
Health Monitoring
Visual Search
Language Translation
Gesture Recognition
Facial Recognition
Image and Sticker Generation
Text to Video Creation
Sentiment Analysis
Security and Compliance
Real-Time Data Processing
How Much Does It Cost to Create an AI App?
Future Trends in AI Application Development
AI and IoT Convergence
Enhanced Personalization
Integration of Generative AI
Explainable AI and Responsible AI
Increased Focus on Edge AI
Autonomous AI Agents
No-Code and Low-code AI Development
Human-AI Collaboration
Integration of RAG in AI
Quantum Computing in AI
How to Build an AI App: Navigating Challenges and Finding Solutions
Data Quality and Quantity
Integration with Existing Systems
Ethical and Privacy Concerns
Industry-Wise Use Cases of AI Applications
Healthcare — Predictive Analytics for Patient Care
Transportation — Autonomous Vehicles
Real Estate — Property Valuation and Market Analysis
Retail — Personalized Shopping Experiences
Finance — Budget Management
Manufacturing — Predictive Maintenance
How can Appinventiv Help You Build an AI App That Makes an Impact?
FAQs

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Artificial intelligence (AI) apps have become an inseparable part of the modern business world, redefining operations and driving innovation across various industries. According to Business of Apps, the AI app sector generated $1.8 billion in 2023 and is estimated to grow to $18.8 billion by 2028. In June 2024 alone, an estimated 230 million people used AI apps, highlighting their widespread adoption and transformative potential in our personal and professional lives.

Imagine a scenario where a smart home system learns your daily routines and automatically adjusts lighting, temperature, etc., and even suggests your favorite music as you walk through the door. This personalized experience is not a distant dream but a reality made possible by the integration of AI into business operations.

The applications of AI extend far beyond smart homes; they drive innovation in nearly every domain – healthcare, finance, transportation, retail, real estate, and manufacturing. AI applications like Siri, Alexa, and Google Assistant transform personal assistance, while apps like Netflix and Spotify enhance user experience through personalized recommendations. Furthermore, Generative AI applications like ChatGPT and Gemini AI are pushing the boundaries of what AI can achieve in productivity and creativity.

Businesses leveraging AI into their applications are gaining a competitive edge, setting new standards for innovation and customer engagement. However, building an app with artificial intelligence is not an easy task, leaving businesses wondering how to create an AI app that can make a real impact and drive the intended outcome.

Well, worry not! This blog is your comprehensive guide to building AI applications, addressing all your AI dilemmas, and helping you navigate the process from start to finish. It covers transformative applications, examples, development steps, key features, associated costs, and a lot more, providing you with the in-depth knowledge essential to build an app with AI.

Understanding the Core Components of AI Applications

AI, in its most basic form, refers to the emulation of human intelligence in machines, allowing them to think and learn like humans. Thus, building AI applications involves creating a computer program capable of performing a multitude of tasks that typically require human intelligence. Reasoning, understanding natural language, analyzing sentiments, learning from experience, detecting anomalies, recognizing patterns, and making decisions are some of the most notable tasks performed by AI apps.

To gain an in-depth understanding of how to create an artificial intelligence app, it is crucial to understand the core components of artificial intelligence and their notable applications in various scenarios.

Core Components of AI

AI is a multifaceted field that encompasses various components and elements. Understanding the working mechanism of these components will give you a clear picture of how to build a custom AI application and how they will work. Here are the different integral elements of AI:

Challenges and Solutions for AI App Development

Machine Learning (ML)

Machine Learning is a core component of AI that allows systems to learn from data and enhance their performance over time without explicit programming. It involves algorithms that can analyze and interpret complex data patterns to make predictions or decisions. Its working mechanism includes:

Supervised Learning: Learning from labeled data to make predictions, such as detecting spam emails.
Unsupervised Learning: Identifying patterns in unlabeled data. It is used for clustering and association tasks, such as customer segmentation.
Reinforcement Learning: Interacting with an environment and receiving feedback in the form of rewards or penalties. It is commonly used in robotics and game AI.

Elements of ML

Neural Networks

Neural networks, a branch of AI inspired by neurology, simulate the human brain by mimicking its vast network of neurons. In a simple term, neural networks are computational models that combine cognitive science with machines to perform tasks like recognizing patterns and making decisions. Its working mechanism includes:

Artificial Neural Networks (ANNs): Basic neural networks used for simple pattern recognition tasks, such as image and speech recognition.
Convolutional Neural Networks (CNNs): Specialized for processing grid-like data such as images and videos, essential in facial recognition and self-driving cars.
Recurrent Neural Networks (RNNs): Designed to handle sequential data, used in applications like language modeling and time-series forecasting.

Deep Learning

Deep Learning is basically a subset of machine learning that uses neural networks with many layers (deep neural networks). It excels in learning from large amounts of unstructured data. Its working mechanism includes:

Speech Recognition: Transcribing spoken language into text with high accuracy.
Image and Video Processing: Enhancing and recognizing patterns in visual content.
Natural Language Understanding: Improving the accuracy of NLP tasks such as translation and sentiment analysis.

Also Read: Cost of developing an AI Voice and TTS app like Speechify

Natural Language Processing (NLP)

NLP facilitates the interaction between computers and humans in natural language. It enables machines to understand, interpret, and generate human language. Its working mechanism includes:

Text Analysis: Extracting meaningful information from text data, such as identifying customer sentiment from reviews.
Machine Translation: Automatically translating text from one language to another, such as Google Translate.
Speech Recognition: Converting spoken language into text, used in intelligent voice assistants like Siri, Alexa, and Tootle.

Working mechanism of intelligent voice assistants - Tootle

Computer Vision

Computer Vision enables machines to interpret and make decisions based on visual inputs from the world. It involves image processing and pattern recognition technologies to understand and analyze images and videos. Its working mechanism includes:

Image Recognition: Identifying objects, individuals, and scenes in images, such as tagging photos on social media.
Object Detection: Locating and identifying multiple objects within an image as seen in autonomous vehicles and security surveillance.
Image Segmentation: Computer vision in healthcare helps in dividing an image into segments for easier analysis. This aids in advanced medical imaging and object recognition.

Robotics

Robotics combines AI with mechanical engineering to create machines capable of performing tasks autonomously or semi-autonomously with minimal human intervention. These robots can be used in manufacturing, healthcare, agriculture, and logistics to improve efficiency and accuracy. Its working mechanism includes:

Robotic Process Automation (RPA): Automating repetitive tasks in business processes like data entry and customer service.
Autonomous Robots: Robots that can perform tasks without human intervention, such as AI drones and robotic vacuum cleaners.
Collaborative Robots (Cobots): Robots designed to work alongside humans in environments like manufacturing and healthcare.

Expert Systems

Expert systems emulate the decision-making abilities of a human expert. They use a combination of knowledge and rules to solve specific problems in fields such as medical diagnosis, financial forecasting, and customer support. Its working mechanism includes:

Knowledge Base: Containing a repository of specialized information, facts, and rules about a particular domain, such as a medical database for diagnosis.
Inference Engine: Applying logical rules to the knowledge base to draw conclusions or make decisions.
User Interface: Allowing users to interact with the system, input data and receive guidance or solutions.

Fuzzy Logic

Fuzzy Logic is a method of reasoning that deals with approximate rather than fixed and exact reasoning. It is used in systems that need to make decisions based on imprecise or uncertain information. Its working mechanism includes:

Control Systems: Managing complex systems like climate control in buildings.
Pattern Recognition: Identifying patterns in data where traditional binary logic falls short.

Understanding these elements of AI provides a comprehensive view of how AI systems function, how various technologies are used to perform various tasks, and, most importantly, how to create an app with AI that exceeds expectations.

How to Make an AI App – A Step-by-Step Guide

Building AI applications is a complex and challenging process that requires working with advanced technologies such as deep learning, machine learning, natural language processing (NLP), data science, computer vision, robotics, and more.

However, by following a streamlined process, you can build an AI app efficiently and effectively. Let’s explore each step in detail to guide you through the journey of how to create an app with AI:

AI App Development Process Identify Problems and Define Objectives

The first step in developing an AI app is to identify the specific problem you want to solve. This involves understanding the needs and pain points of your target audience. Once you know the problem of your target market, define the objectives of your AI app, and determine how the app will add value to your users and address their pain points.

Choose the AI Model

Selecting the right AI model is the next vital step to build an AI app. You can consider choosing AI models like BERT, GPT-3.5, T5, LLaMA, Gemini, PaLM, Mistral, Orca, InstructGPT, Flan, RoBERTa, etc., based on your app’s specific needs. Each of these models offers unique features and functionalities, ensuring the success of your AI project.

So, choose the one that aligns with your objectives, or consider building a custom model if your requirements are highly specialized. Here is a table defining the use cases of some popular AI models:

AI Model	Use Cases
BERT	Ideal for tasks such as named entity recognition, sentiment analysis, and word classification, providing deep contextual understanding.
T5	Versatile model used for translation, text summarization, and answering questions by converting tasks into a text-to-text format.
GPT-3.5	With its advanced language capabilities, thai model is an excellent choice for generating content, and providing coding assistance
Mistral	Specializes in understanding and summarizing documents, enhancing text processing efficiency.
PaLM	Effective for complex task reasoning, coding, and both text and word classification, as well as question answering.
InstructGPT	Focuses on task-based instructions and predicting user intent, improving interactions with AI systems.
RoBERTa	Enhances text generation and captures long-range dependencies and contextual nuances in language.
XLM	Provides robust multilingual capabilities
XLNet	Optimized for sentiment analysis, question answering, and natural language understanding with an autoregressive approach.
Turing NLG	Designed for creating chatbots and conversational AI applications, generating natural and engaging dialogues.
Phi	Compact and efficient, specializing in Python coding
Alpaca	Excels in text generation, Q&A, summarization, and various other NLP tasks.
Bloom	Strong in QA, text generation, and generating embeddings for diverse applications.
Vicuna	Useful for text generation, answering questions, and summarizing content, along with other NLP tasks.

Choose the Right Tech Stack

When considering how to make an AI app for Android or iOS, you must choose the right tech stack that is best suited for your app’s requirements. Consider using the right mobile frameworks, cloud services, and AI platforms like Google Cloud AI, Amazon Web Services (AWS), or Microsoft Azure to leverage their robust infrastructure and AI capabilities.

Collect and Prepare Data

Data is the backbone of any AI application. Thus, you need to invest significant time and effort in collecting relevant data from diverse sources. Cleanse and preprocess the collected data to ensure only high-quality data is used to train your AI models.

Train the AI Model

Once you have collected and prepared the data, the next step is to train the model using this data and optimize its performance. Training involves feeding the model large datasets and adjusting its parameters to improve accuracy and efficiency. Popular frameworks for training AI models include TensorFlow, PyTorch, Keras, and Hugging Face Transformers.

Design the UI/UX

The next step is to craft a user-friendly and intuitive interface that is easy to navigate and can capture attention. A well-crafted UI/UX not only enhances user engagement but also increases retention and conversion rates.

Develop the App

With your data and design ready, it is time to develop an AI app and bring your vision to reality. At this stage, your AI app development company focuses on both frontend and backend development. The frontend involves creating a user-friendly interface that allows users to interact seamlessly with your app. The backend development encompasses building and integrating the server-side components, including the AI model, database management, and security measures.

Integrate the AI Model into the App

Now, it is time to integrate the trained model into the app’s architecture, enabling it to perform its intended functions in real-time. This process includes connecting the model with the app’s frontend and backend components, ensuring that data flows seamlessly between the user interface and the AI system.

Test the AI App

Before you deploy your app to the targeted platform, you must conduct thorough testing to ensure that it functions optimally and meets the defined goals. Test the app at different parameters to ensure its quality, usability, security, and overall functionality. This includes unit testing, integration testing, User Acceptance Testing (UAT), performance testing and security testing. This step is essential to identifying and fixing any issues before the app is released.

Deploy and Maintain

Once the app is fully developed and checked for quality, deploy it to the desired platforms, such as iOS, Android, or the web. After deployment, continuously monitor and maintain the AI app to keep it running smoothly as per changing user needs and technological advancements.

Game-Changing Features of an AI App

Building AI applications requires incorporating a range of features that can significantly enhance user experience, drive engagement, and streamline operations. While the specific features of an AI app can vary based on the industry and project requirements, certain capabilities are universally valuable. Here are some common AI app development features that will make your apps stand out in today’s competitive market.

Must-Have Features of an AI App

Personalized Recommendation

An AI-powered personalized recommendation system analyzes user behavior, preferences, and past interactions to suggest tailored content, products, or services. This feature enhances user engagement, leading to increased user retention and higher conversion rates.

Chatbots and Voice Assistants

AI-powered chatbots and voice assistants provide instant, 24/7 customer support. They can efficiently handle a wide range of queries, freeing up human resources for more complex, value-added tasks.

Example of user interface in Chat & More application

Auto-Correction and Completion

The auto-correction and text completion feature assists users by automatically correcting spelling errors and predicting the next word or phrase. These functionalities improve typing speed, efficiency, and accuracy, making communication faster and reducing errors. This feature is especially beneficial in social media messaging apps and word processors.

Predictive Analytics

Predictive analytics in AI apps analyze historical data and predict future outcomes, helping businesses anticipate trends, optimize strategies, and improve performance. This feature is invaluable for data-driven decision-making in various industries, including finance, healthcare, retail, and marketing.

Health Monitoring

The health monitoring feature in AI apps tracks and analyzes health-related data, such as heart rate, physical activity, and sleep patterns. This feature provides insights and alerts, helping users identify health issues early before they become severe and maintain their well-being, thereby improving patient care.

Visual Search

Visual search enables users to search for their desired product using images rather than text. This feature utilizes computer vision technology to detect images, allowing users to find products, information, or similar images quickly and accurately. It is particularly useful in the retail and eCommerce industry, enhancing user engagement and shopping experience.

Language Translation

Language translation translates text or speech from one language to another in real-time. This feature breaks down language barriers, facilitating smooth communication and interaction in a globalized world. This functionality is essential for international business, travel, and hospitality sectors, facilitating multilingual customer support.

Gesture Recognition

The gesture recognition feature interprets human gestures, allowing users to interact with devices through movements. This feature enhances user experience by providing a more natural and intuitive way of interacting with technology. It is used in gaming, virtual reality, and smart home applications.

Facial Recognition

Facial recognition helps identify and verify individuals based on their facial features. This technique is widely used for security purposes, such as unlocking devices, authentication in apps, and surveillance. Industries such as finance, healthcare, and law enforcement can particularly leverage this feature to prevent data theft and fraudulent transactions.

Image and Sticker Generation

AI generates custom images and stickers based on user input or predefined templates. This feature enhances creativity and personalization in applications related to social media, design, and digital content creation.

[Also Read: How Much Does It Cost to Build an AI Art Generator App Like Imagine]

Image and Sticker Generation Feature in AI Apps

Text to Video Creation

AI converts written content into engaging video formats, incorporating visuals, animations, and voiceovers. This capability streamlines the content creation process, making it easier to produce professional-quality videos from text scripts with minimal effort.

Sentiment Analysis

Sentiment analysis is a core feature of AI apps that determines the emotional tone behind a piece of text. This feature is valuable for understanding customer feedback, monitoring social media, and improving customer service. By analyzing sentiments, businesses can gain insights into customer emotions and tailor their marketing strategies accordingly.

Security and Compliance

Ensuring security and privacy is paramount for any application, and AI apps are no exception. Robust security measures, such as data encryption and user authentication, protect sensitive information from unauthorized access and cyber threats. Additionally, compliance with industry regulations, such as GDPR and HIPAA, helps ensure that AI apps meet legal and ethical standards while protecting user privacy.

Real-Time Data Processing

Real-time data processing allows AI apps to analyze and act on data as it is generated. This feature is critical for applications that require immediate responses, such as financial trading, fraud detection, and live customer support. It enhances the app’s responsiveness and provides up-to-date insights, improving user experience and operational efficiency.

By focusing on these must-have features, you can create an AI app that meets the highest standards of innovation, security, and usability in today’s AI-driven age.

How Much Does It Cost to Create an AI App?

There is no one-size-fits-all formula to quote the exact cost to make an AI app; it varies widely depending on various factors. The factors affecting AI app development cost include but are not limited to the project’s complexity, features, quality and quantity of data, chosen tech-stack, platform compatibility, location of the AI development company, and so on.

On average, the cost of building an AI app can range from $30,000 to $300,000 or more. However, this is just a rough estimate; the final cost can increase or decrease depending on your unique project requirements.

Here is a table outlining the estimated timeline and cost of building an AI app, depending on the project’s complexity and features.

App Complexity	Average Timeline	Average Cost	Example
Simple AI app with basic features	4-6 months	$30,000-$50,000	Chatbot and virtual assistant for customer service
Medium AI app with moderate features	4-9 months	$50,000-$120,000	Financial literacy app like Edfundo
Highly complex AI app with advanced features	9 months to 1 year or more	$120,000-$300,000 or more	Healthcare diagnostic apps with real-time communication, like YouComm

Future Trends in AI Application Development

AI app development is rapidly evolving due to changing user expectations and technological advancements. Here are some key trends shaping the future of AI applications:

AI Development Trends

AI and IoT Convergence

The fusion of AI with the Internet of Things (IoT) is creating smarter, more connected environments. This integration enhances data analysis from various devices, leading to more responsive and efficient AI systems.

Enhanced Personalization

AI is increasingly tailored to individual user preferences, providing highly personalized and relevant recommendations, making services feel more intuitive and user-centric.

Integration of Generative AI

The rise of generative AI technologies allows for the creation of custom content, including text, images, and videos, facilitating more dynamic user experiences.

Explainable AI and Responsible AI

As AI systems become more complex, there is a growing emphasis on explainable and responsible AI development. Explainable AI ensures users can trust and comprehend AI decisions, while responsible AI focuses on ethical considerations.

Increased Focus on Edge AI

Edge AI is shifting processing power from the cloud to local devices. This trend is crucial for applications requiring immediate responses, such as autonomous vehicles and smart sensors.

Autonomous AI Agents

The development of AI agents that can operate independently is advancing, leading to more sophisticated automated systems capable of handling complex tasks with minimal human intervention.

No-Code and Low-code AI Development

Platforms allowing users to build AI applications with minimal coding are gaining traction. No-code and low-code trend empowers organizations to create and deploy AI and computer vision solutions without extensive technical expertise.

Human-AI Collaboration

With AI tools designed to enhance human capabilities rather than replacing them, humans and AI collaboration leads to more effective decision-making and creative problem-solving.

Integration of RAG in AI

Retrieval Augmented Generation (RAG) is enhancing AI’s ability to provide accurate and contextually relevant information by combining retrieval of existing data with generative capabilities. This trend improves the relevance and reliability of AI responses.

Quantum Computing in AI

Quantum computing revolutionizes AI by solving complex problems much faster than traditional computers. While still in its early stages, this AI trend is poised to advance AI capabilities in areas like optimization and simulation.

How to Build an AI App: Navigating Challenges and Finding Solutions

Building an AI app is a rewarding endeavor, but it comes with its own set of challenges that need to be overcome efficiently to realize the full potential of AI applications. Here are some of the most common hurdles you need to overcome when considering how to develop an AI application.

Challenges and Solutions for AI App Development

Data Quality and Quantity

Challenge: High-quality, relevant data is the lifeblood of AI model development. Insufficient or poor-quality data can lead to inaccurate predictions, biases, and subpar performance.

Solution: Invest significant effort and time in data collection and preprocessing to ensure your dataset is comprehensive and clean.

Integration with Existing Systems

Challenge: Integrating AI applications with existing infrastructure can be complex and may cause compatibility issues.

Solution: Design your AI components with modularity, scalability, and flexibility in mind. Regularly test and validate the integrated system to address compatibility issues early.

Ethical and Privacy Concerns

Challenge: AI applications are made to handle sensitive user information and business data, which may raise data breaches, privacy, and ethical concerns.

Solution: Implement robust data security measures and comply with relevant regulations, such as GDPR and CCPA, to protect user privacy and sensitive data.

Also Read: Explainable AI benefits every AI enthusiast should know

Now that we know how to use AI to build an app and ensure its future growth, let’s uncover the various applications of AI, transforming businesses across industries.

Industry-Wise Use Cases of AI Applications

The applications of AI in businesses are innumerable and invaluable. Whatever you imagine, AI can achieve it. Here are some compelling applications and examples of AI across various industries, showcasing how this cutting-edge technology is driving efficiency and fostering innovation.

AI Applications in Various Industries

Healthcare — Predictive Analytics for Patient Care

AI applications in healthcare are revolutionizing patient care by enabling predictive analytics that forecast patient outcomes and improve care management. These systems analyze vast amounts of data to predict disease progression and suggest personalized treatment plans. Additionally, AI-powered diagnostic tools analyze medical images to detect diseases like cancer at an early stage.

Predictive Analytics for Patient Care Quote

Example: Health at Scale uses predictive analytics to enhance patient care by forecasting potential health issues before they arise. It helps healthcare providers proactively manage chronic conditions and tailor preventive care strategies, resulting in more personalized and effective treatment plans.

Transportation — Autonomous Vehicles

AI is at the core of developing autonomous vehicles, enabling them to navigate and drive safely on the road without human intervention. These self-driving vehicles use computer vision, machine learning, automotive analytics, and sensor data to make decisions and operate efficiently.

Example: Waymo uses AI in its range of self-driving cars to interpret sensor data, recognize objects, and make driving decisions, demonstrating the potential of AI in transforming transportation.

Also Read: AI in Transportation: Benefits, Use Cases, and Examples

Real Estate — Property Valuation and Market Analysis

AI enhances real estate by providing accurate property valuations and market analysis, helping buyers, sellers, and real estate professionals make informed decisions. AI apps analyze data from multiple sources, including historical sales, property features, and market trends, to estimate the right property price and forecast real estate trends.

Example: HouseEazy, a real estate platform built by Appinventiv, leverages AI to offer precise property valuations and market insights. By analyzing vast amounts of real estate data, including property attributes and market dynamics, HouseEazy provides users with accurate home valuations and investment recommendations, helping them make informed decisions.

Price prediction in HouseEazy

Retail — Personalized Shopping Experiences

AI in the retail and eCommerce industry uses AI to create personalized shopping experiences for customers. AI in eCommerce apps like Target, Walmart, Amazon, etc. analyzes users’ browsing behavior, purchase history, and preferences to recommend products tailored to their tastes.

Example: Amazon’s recommendation engine uses AI to suggest products based on users’ browsing and purchase history, increasing customer engagement and driving sales.

Finance — Budget Management

AI in finance plays a pivotal role in offering personalized financial advice and detecting fraudulent activities by analyzing transaction patterns and identifying anomalies in real-time. Additionally, AI-powered applications help transform budget management by automating tasks such as budgeting, expense tracking, and investment analysis, helping users make informed decisions.

Example: Mudra partnered with Appinventiv to build a budget management app that uses AI to help millennials manage their finances efficiently. By analyzing spending patterns and financial behavior, Mudra keeps tabs on the users’ expenses and provides personalized budgeting advice, helping users set savings goals.

Mudra partnered with Appinventiv

Manufacturing — Predictive Maintenance

AI in manufacturing uses predictive maintenance to anticipate equipment failures and schedule timely repairs. This is one of the most integral use cases of an AI app in manufacturing that helps minimize downtime and maintenance costs.

Example: General Electric (GE) uses AI in its Predix platform to predict when industrial machinery needs maintenance. By analyzing sensor data and operational metrics, Predix helps manufacturers maintain equipment efficiency and avoid costly breakdowns.

The range of roles AI applications can play in business is vast and continually expanding. In simple terms, anything a human can do, AI apps can do better and faster—and in many cases, they already surpass human capabilities.

How can Appinventiv Help You Build an AI App That Makes an Impact?

When evaluating how to develop an AI application or how to integrate AI into an app, partnering with a seasoned AI app development company like Appinventiv can make all the difference. We possess an in-depth understanding of emerging technologies and an unwavering commitment to innovation. From initial planning to deployment and beyond, our team of 1600+ tech evangelists brings a wealth of knowledge and expertise to the table, ensuring your AI app not only meets but exceeds industry standards.

We combine technical prowess with strategic insight, ensuring that every aspect of your AI app is tailored to align with your specific business goals. Whether you are aiming to enhance customer engagement, streamline operations, or unlock new business opportunities, our custom AI solutions will stand by you through every step of the digital transformation journey in this AI-driven era.

Here are the examples of our some successful AI-driven projects:

Tootle: We developed an interactive intelligent agent – Tootle, that enhances user interactions through advanced AI, improving user engagement and operational efficiency.
JobGet: Our experts leveraged AI to build JobGet that streamline the job search process and match candidates with suitable job opportunities.
Chat & More: This advanced AI chatbot solution provides personalized customer service and engagement.
HouseEazy: We have also developed an AI-driven real estate app – HouseEazy, that automates property price predictions based on parameters like location, number of bedrooms, and other features.

What are you still waiting for? Partner with us now and witness transforming your AI vision into a groundbreaking reality. With Appinventiv, you don’t just build an AI app; you build a state-of-the-art solution that stands out in this digitally growing era.

FAQs

Q. What are the benefits of AI application development?

A. There are numerous benefits of AI application development, such as::

Enhanced Automation: One of the most known benefits of AI application development is the automation of repetitive tasks and processes, reducing manual errors.
Personalized User Experiences: AI enables tailored interactions and recommendations, adapting the products and services to individual user preferences and behaviors.
Increased Efficiency: AI-driven applications optimize operational workflows and processes across departments, resulting in faster and more accurate outcomes.
Improved Decision-Making: By providing data-driven insights and predictions, AI apps support more informed decision-making for businesses.
Predictive Insights: AI analyzes market trends and patterns to predict future events, offering valuable foresight for demand forecasting and planning.

Q. How to manage privacy, security and compliance challenges in AI development?

A. Managing privacy, security, and compliance in AI involves several key practices, such as:

For Privacy Challenge

Use data anonymization to protect personal information.
Implement differential privacy to analyze data without compromising individual privacy.
Apply secure multi-party computation for collaborative environments.

For Security Challenge

Employ robust authentication and access controls.
Conduct regular security audits to identify and address vulnerabilities.
Incorporate adversarial training to enhance model resilience against attacks.

For Compliance Challenge

Perform regular compliance reviews to stay updated with regulations.
Develop and adhere to ethical AI frameworks.
Ensure transparency and explainability in AI systems to meet regulatory requirements.

Q. What is the cost and timeline to build an AI application?

A. The cost and timeline to make an AI application varies depending on various factors including the project’s complexity, platform compatibility, chosen tech stack, data requirements, expertise of AI app development company and so on.

On average, a simple AI app with basic features typically costs between $30,000 and $50,000 and takes 4-6 months to develop, while a medium-complexity app costs around $50,000 and $120,000 and takes 4-9 months to complete. Highly complex AI applications with advanced functionalities may cost between $120,000 and $300,000 or more and require around 9 months to over a year, depending on your unique project requirements.

Get in touch with us to get a more precise estimate for the AI application development timeline.