AI Development Services

Our AI development services help you unlock your business's true potential by streamlining your business workflows and enhancing user experience in your software. We offer a comprehensive suite of AI services from consultation to launch that helps you get a complete solution.
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Leverage Our AI Development Services To Scale Up Your Operations

Hire AI engineers from Appic Softwares to build reliable AI softwares that uses data-driven solutions to automate your workflow with the help of machine learning, Big data, computer vision, and natural language processing.

Here are all the AI development services offered by us:

MVP

AI MVP Development

We design AI-integrated Minimum Viable Products that deliver key capabilities to facilitate genuine user validation, iterative improvements, and quick market launch. We identify the specific problem the AI solution aims to solve and understand your target users' needs. Moreover, AI MVP development is ideal for startups and companies looking to validate their product ideas quickly without a large upfront investment.

Custom Model

Custom AI Model Development

Custom AI model development involves creating an AI solution tailored to specific business needs, offering a more precise and efficient approach compared to off-the-shelf models. The process starts with identifying the unique problem that the AI can help with customer successs. Our AI developers use frameworks like TensorFlow, PyTorch, or other specialized libraries to build your custom AI model.

AI Integration

AI Integration Services

We assist startups and enterprises in integrating AI into business operations that streamline business operations and automate workflow. Our team works closely with your business software which enables quick integration and minimal downtime that helps increase work efficiency.

Enterprise AI

Enterprise AI Development

Our AI developers help businesses evolve by integrating AI into their operations. Moreover, through our custom Enterprise AI development services we ensure that it enhances business decision-making, automates the majority of the workflows, and fulfils your requirements.

Generative AI

Generative AI Development

Leverage our generative AI development services to generate AI text, images, and even videos. We use tools such as PaLm, Gemini, Chatgpt, and more to build robust generative AI tools and solutions. From AI call centers to image generation, our developers can help you develop all.

Multi-Agent System

Multi-Agent System Development

A multi-agent system consists of various AI agents that work, sense, act, and learn independently to fulfil a particular task. These models can be used across industries and thus can be trained according to their needs.

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  • 95%

    Client Retention Rate

  • $10M+

    Investment Raised by Startups

  • 300+

    Mobile Apps Developed

Our AI Development Process

Our roadmap to develop an AI model is based on your project requirements. Although we have a unified process of data collection and training the AI model depends on how you want it to function. Here is our AI software development process:
Consult Our AI Developers
Analysis

Project Analysis And Data Collection

We align our team of Business analysts with you to better understand your project idea. The BA develops a blueprint plan for how we will move forward with the development. We will also identify different streams through which we will collect data ensuring it's relevant to train the AI model.
Algorithm

Algorithm Selection And Model Training

Our AI engineers will then select the most suitable AI algorithm model based on your project requirements. After selecting the model they will be working very closely keeping an eye on the data streams and training the model based on it.
Testing

Testing & Analysis

We then use separate databases to ensure accurate predictions based on different scenarios. From AI in knowledge base management to workflow automation and customer support, our AI developers ensure that every AI model functions smoothly.
Deployment

AI Deployment And Integration

Once we have trained and tested the model, we will integrate it with your software and ensure it's scalable and compatible. We will be continuously testing and training the AI model to ensure it works smoothly even after integration.
Maintenance

AI Model Maintenance And Continuous Training

We establish regular maintenance scheduling which ensures that your model does not degrade and that it gets trained on regular feedback and evolving data.

Our industry-specific AI Development Services

Healthcare Image

Healthcare

AI is revolutionizing healthcare by making diagnostics more accurate, personalizing treatments, and automating routine processes. It can analyze complex medical data, including images and patient records, to detect conditions like cancers or heart disease earlier than traditional methods. This technology enables personalized treatments by tailoring therapies based on a patient's genetic profile and medical history.

Trending AI Technologies We Use To Build AI Apps

Here are all the trending AI technologies used by our AI engineers to develop engaging and powerful AI apps. We portray our expertise while developing these apps making sure you get an edge over your competitors.
NLP

Natural Language Processing

Our AI developers use frameworks such as Transformers, Hugging Face, and spaCy to enable machines to understand, interpret, and respond to human language, making interactions between users and AI systems more intuitive. NLP is primarily used for text analysis, sentiment analysis, speech recognition, text summarization, and more.
ML Frameworks

Machine Learning Frameworks

These frameworks are used for training and scaling up an AI model. We use PyTorch, TensorFlow, scikit-learn, and more to develop deep-learning models to carry out tasks like image recognition, predictive analytics, speech processing, and more.
Generative AI

Large Learning Models & Generative AI

Technologies like GPT-4 and DALL-E enable AI systems to generate human-like text, images, and other creative content. These models have found applications in content creation, image generation, and even coding assistance.
Computer Vision

Computer Vision

This technology powers applications that require image recognition, object detection, and facial recognition. It is used in fields like healthcare for medical imaging, in autonomous vehicles for navigation, and in retail for inventory management. Popular libraries used by our AI developers for computer vision include OpenCV, YOLO (You Only Look Once), and Detectron2.
Edge AI

Edge AI

By using frameworks such as TensorFlow Lite and ONNX our AI developers build software for edge devices like smartphones, IoT devices, and sensors, allowing real-time processing without relying on cloud servers. This is particularly important for applications requiring low latency, such as autonomous drones, wearable devices, and smart cameras.
No-Code/Low-Code

AI In No-Code/Low-Code Software

Platforms like Bubble, FlutterFlow, and Appgyver integrate AI components, allowing businesses to build AI-driven apps with minimal coding. You can hire FlutterFlow experts, Bubble.io developers, and more from us and let them enable rapid prototyping and deployment of AI features like chatbots, sentiment analysis, and predictive analytics for you.

What Are The Different Types Of AI Solutions?

Our team of AI developers uses technologies such as DALL-E, Midjourney, Snowflake, and more to craft innovative AI solutions. Let's look at all the AI solutions that our developers have worked on:

AI Text Models

AI text models, often referred to as natural language processing (NLP) models, are specialized algorithms designed to understand, generate, and manipulate human language. At Appic Softwares, our skilled AI developers bring a wealth of expertise to the table when it comes to creating robust AI text models tailored to your specific needs.

Tools Used:

  • OpenAI
  • Mistral
  • Hugging Face
  • LLaMA2
  • Gemini
  • LaMDA

Image And Video Generation Models

Image and video generation models are advanced artificial intelligence systems designed to create visual content from various inputs. These models leverage deep learning techniques to understand patterns in existing visual data and generate new images or videos that resemble the original dataset.

Tools Used:

  • DALL-E
  • Stable Diffusion
  • Midjourney
  • Leonardo

RAG Model

Our AI developers use the capabilities of databases with the capabilities of generative LLMs to generate texts. The RAG (Retrieval-Augmented Generation) model is a state-of-the-art approach in natural language processing that combines retrieval mechanisms with generative models. This hybrid framework aims to improve the quality and relevance of generated text.

Tools Used:

  • Unstructured
  • Airbyte
  • LangChain
  • Llama Index

ETL Model

The ETL (Extract, Transform, Load) model is a fundamental process in data integration and data warehousing that facilitates the movement of data from multiple sources to a destination, typically a data warehouse or a database. This model is essential for organizations that need to consolidate data from various systems for analysis, reporting, and decision-making.

Tools Used:

  • Fivetran
  • Talend
  • Hevo Data
  • Skyvia
  • Xplenty
  • PySpark

How We Ensure Secure AI Software Development?

We implement multi-layer strategies while developing an AI model ensuring that it does not malfunction or provide unwanted data to the cyber attackers. Here are some of the steps taken by us to develop secure AI software:

Secure Design

We Implement Secure Design Principles

The initial step to developing a secure AI model starts with us developing an architecture that minimizes vulnerability by defining a security model that considers data handling, access control, and the overall flow of the information stored in your database. We also adopt threat modeling by defining a process in which an attacker could exploit the vulnerability.
Data Security

Data Security And Privacy

Data defines how an AI model would function, thus it's too important to maintain its security. To ensure data privacy, our AI developers implement data encryption both at rest and during transmission. Encryption methods like AES (Advanced Encryption Standard) are used to safeguard sensitive data, preventing unauthorized access during data storage or communication.
Authentication

Robust Authentication And Access Control

To prevent unauthorized access to AI systems, we implement strong authentication mechanisms. This includes multi-factor authentication (MFA), where users must provide two or more verification factors to gain access. Moreover, our AI developers also implement role-based access control to ensure that users only have access to the data and functions.
Training

Secure Training Practices

The training phase of AI models often requires access to large datasets, which makes it a crucial point for implementing security measures. We use secure multiparty computation (SMC) and homomorphic encryption during the training process to ensure that sensitive data remains encrypted while computations are performed on it.
Model Safety

Model Saftey Against Adversarial Attacks

With the help of gradient masking and optimization techniques, we reduce the vulnerability towards adversarial attacks where malicious actors manipulate the input data to deceive the AI system. To counter this, we adopt adversarial training, where the AI model is exposed to adversarial examples during the training phase that makes it more secure.
Compliance

Compliance With Standards

To align with industry best practices, we ensure that our AI software development process complies with recognized security standards. Standards such as ISO/IEC 27001 provide a framework for managing information security, ensuring that data protection practices meet global benchmarks. which makes your AI software compliance friendly.
Transparency

Transparency And Explainability

A key aspect of secure AI software is ensuring that the model's decisions are transparent and explainable. Our AI engineers adopt explainable AI (XAI) techniques, which help clarify how a model arrived at a specific decision. This not only builds user trust but also allows us to detect and correct biases or potential flaws in the AI's reasoning which makes it more accurate.
Response Plan

Incident Response And Recovery Plans

Despite all precautions, breaches can occur. Thus, having a well-defined incident response plan is critical. This plan outlines steps to take in case of a security breach, including containment, investigation, communication, and recovery. By having a plan in place, we ensure that any security incidents are handled swiftly, minimizing potential damage and ensuring the continuity.

Total revenue of mobile apps is expected to grow at a CAGR of 9.27%, and it is estimated to reach $614.40 billion by 2026.

Thus if you are planning to digitalize your services then it's the right time. Consult Our App Developers!

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Our Different AI Model Development Services

Various types of AI models are designed to solve specific problems or solve particular tasks. Every AI model is built using various technologies, frameworks, and programming languages. Here is a breakdown of all the different types of AI models that we develop:

Supervised Machine Learning Model

Supervised learning involves training models on labeled data, where the input-output pairs are known, making it easier to learn relationships between variables and create an AI algorithm. These models are typically used for tasks like classification and regression and excel in scenarios where a large amount of historical data is available to train the system.

Technologies

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Keras

Programming Languages

  • Python
  • R
  • Java

Unsupervised Learning Model

Unsupervised learning deals with unlabeled data, making it effective for discovering hidden patterns, groupings, or anomalies within a dataset. It's widely used in applications like market segmentation, anomaly detection, and customer grouping. These models are invaluable when it is difficult or time-consuming to label data.

Technologies

  • TensorFlow
  • PyTorch
  • Scikit-learn

Programming Languages

  • Python
  • R

Deep Learning Models

A subset of the machine learning technique, deep learning models use neural networks with multiple networks to analyze and learn from a large set of data. These models are particularly powerful for tasks involving unstructured data, such as images, audio, and text.

Technologies

  • TensorFlow
  • PyTorch
  • Keras

Programming Languages

  • Python
  • Java
  • C++

Generative Models

Generative models focus on creating new data samples that resemble a given dataset, making them useful in fields like creative arts, content generation, and image synthesis. They can create realistic images, texts, or audio, often blending new creative elements with patterns observed in the training data. These models are at the forefront of AI-driven content creation, producing results that are almost indistinguishable from human-generated content.

Technologies

  • TensorFlow
  • PyTorch

Programming Languages

  • Python
  • Julia

How Much Does It Cost To Build An AI Software?

The cost of developing AI software depends on several factors such as the scope of the project, frameworks used, Data availability, and more. Here are some factors that influence the cost of developing AI software:

Scope And Complexity Of An AI Model

The complexity of the AI project plays a significant role in determining costs. A simple chatbot or sentiment analysis tool will be less expensive compared to complex projects like predictive analytics models, autonomous systems, or advanced natural language processing (NLP) solutions.

Regulatory Compliance And Security Research

For industries like healthcare, finance, and insurance, AI development must adhere to strict regulatory standards and compliance requirements (e.g., GDPR, HIPAA). Thus, the research work increases which also increases the cost of developing AI software.

Data Quality And Availability

AI models rely on large amounts of high-quality data for training. The cost can increase if the necessary data is not readily available, requiring data collection, cleaning, or annotation processes. Data preparation is often a time-consuming process, including activities like data labeling, removing inconsistencies, and ensuring the dataset is suitable for training, which can add to the overall cost.
However, if you partner with us, you can expect the cost of developing an AI model to be between $20K to $80K which totally depends on the factors listed above.
Get Actual Cost Of Development
AI Software Cost

Technologies and Platforms We Use

Our Achievements

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Inspiring Customer Reviews

I wanted to take a moment to express my gratitude and appreciation for Shubham Jain. Shubham quickly assumed the leadership of the project during a critical phase when things were uncertain. Despite facing numerous challenges and obstacles along the way, Shubham remained a calm and steady presence, always putting the needs of the project first. He took the time to truly understand the project specifications and overall concept, diving deep to identify potential issues before they could become problems.
Ujjwal Verma profile picture
Ujjwal VermaCEO, City Of Car
This is one of the best team I have worked with, very professional, submitted within the deadline, and always ready to cooperate. Did a great job, submitted the project one time, and had prompt communication, I will be happy to work with him again. I will also recommend Appic Softwares to anyone looking for a software development company. They were very professional and took the time to listen to our requirements and also provided recommendations that greatly enhanced our software.
Maqsood Maharaullee profile picture
Maqsood MaharaulleeCEO, Furniture.Mu
The team exceeded my expectations by delivering the project not only on time but also ahead of schedule, which was truly impressive. Their professionalism and attention to detail were evident in every aspect of the work they did. I am genuinely delighted with the results, as they went above and beyond to ensure everything was perfect. The entire process was seamless and hassle-free, making it a pleasure to work with them. I highly recommend their services to anyone seeking quality and reliability!
Monica Bindra profile picture
Monica BindraCo-founder, Laiqa
Shubham and his team truly went above and beyond in delivering the project, showcasing their dedication and expertise throughout the entire process. Their commitment to achieving high-quality results was evident in every detail, as they took extra steps to ensure the final outcome exceeded my expectations. They met the project requirements and also consistently demonstrated a willingness to go the extra mile to ensure satisfaction. I am thoroughly impressed with their work.
Lamine Niasse profile picture
Lamine NiasseFounder, SimpleTechX Inc.
The quality of work delivered by the team has been truly exceptional, far surpassing my expectations. Their attention to detail and commitment to excellence were evident in every aspect of the project, making the entire experience smooth and satisfying. They demonstrated a high level of professionalism, ensuring that all my requirements were met with precision. Based on this impressive performance, I am highly inclined to hire them again for my next project, confident that they will deliver outstanding results once more.
Samuel Kyalo profile picture
Samuel KyaloOperations Head, Primemetrics
Very satisfied with the customer service of Shubham. Avinash was taking care of my project and has been very helpful and answered all my questions. He was very professional and replied very fast and with the proper knowledge for my project. We worked on a QR code manu platform and will be happy to again collaborate and work on some other project. They were very clear about the development phase and ensured that my product was delivered within the set time frame.
Miro Gordan profile picture
Miro GordanCEO & Founder, NolimitQR

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AI integration has many strong benefits that you may provide your company. Artificial intelligence (AI) has the potential to improve corporate outcomes by increasing operational efficiency, optimizing decision-making, and stimulating creativity. AI can analyze large datasets and derive insightful information that helps you make data-driven decisions and strategic decisions. Artificial intelligence (AI) can automate monotonous jobs, streamline workflows, lowering human error, and provide your team with more time to concentrate on high-value work. AI also makes individualized consumer experiences possible, which raises satisfaction and engagement levels. AI gives you the ability to be flexible and quick-thinking in the current competitive environment, seizing opportunities instantly and obtaining the upper hand. Adopting AI puts your company at the forefront of technological development and opens up a world of opportunities for productivity, growth, and creativity.

Artificial intelligence (AI) models work by analyzing vast volumes of data using sophisticated algorithms to find patterns, connections, and representations. The model modifies its internal parameters during training in order to reduce prediction errors. These models have particular structures built for tasks like language processing and picture recognition. To make sure the model is accurate, it is evaluated on fresh data after training. After validation, the model can be used to forecast or make judgments in real-world situations. With time, it can learn and get better thanks to feedback loops and exposure to fresh data.

To guarantee the development of innovative and successful AI solutions, we make use of a wide range of tools and technologies. A variety of industry-standard platforms, libraries, and frameworks are included in our toolset. Machine learning frameworks such as TensorFlow and PyTorch are widely used because they provide flexible environments for constructing and honing intricate models. These frameworks provide flexibility and scalability in the deployment of different neural network topologies. We use libraries like NLTK and spaCy for natural language processing jobs, which helps us handle and analyze text data effectively. We also use cloud platforms such as AWS, Azure, or Google Cloud to gain access to scalable resources for management, deployment, and model training. Our staff is skilled with deep learning frameworks such as scikit-learn, a flexible machine learning toolkit that can be used for a wide range of tasks, and Keras, which makes the building of neural networks easier. With the help of these technologies, we can create recommendation engines, predictive models, and other AI solutions that are exactly tailored to your company's requirements. In the end, our strategy focuses on choosing the greatest tools and technologies to meet the project's needs, making sure that we provide AI solutions that are not just creative but also incredibly practical and effective.

Depending on the intricacy and nature of the project, there might be a wide range in the timeframe for defining the scope, organizing, and carrying out an AI project. Various projects have unique needs that affect how long they take in total. The schedule can be affected by a number of variables, including the availability of high-quality training data, the difficulty of the AI algorithms involved, the requirement for specialized model architectures, and the integration of the solution with already-existing systems. It could also be necessary to go through iterative testing and development phases in order to improve and optimize the AI model's functionality. Each AI project must be approached with a customized timeframe since it is important to take the time to carefully define the scope, plan, and carry out the work in order to guarantee that the final solution closely complies with the project's objectives and desired results.

We employ a thorough approach that includes strict data encryption, access controls, and safe architectural design to guarantee the security of our AI projects. We place a high priority on user authentication, carry out frequent security audits, and deploy cutting-edge intrusion detection systems to identify threats instantly. To remain ahead of new security threats, our team adheres to industry best practices, updates software components frequently, and offers ongoing employee training. The confidentiality, integrity, and availability of our AI solutions.

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