AI Application services
Starting at
$
40
/hrAbout this service
Summary
FAQs
What kind of AI applications do you develop?
We develop a wide range of AI applications across various domains. This includes, but is not limited to, applications utilizing: * Machine Learning: Predictive analytics, recommendation systems, fraud detection, sentiment analysis. * Deep Learning: Image recognition, natural language processing (NLP) for chatbots or text summarization, speech recognition. * Computer Vision: Object detection, facial recognition, quality control in manufacturing. * Generative AI: Content generation, code generation (where applicable). * Robotic Process Automation (RPA) with AI: Automating complex, intelligent tasks. We are adaptable and can tailor solutions to your specific industry and needs.
How long does it typically take to develop an AI application?
The timeline for developing an AI application varies significantly depending on several factors, including: * Complexity of the problem: Simpler tasks require less development time than highly intricate ones. * Data availability and quality: Extensive data cleaning and preparation can prolong the process. * Scope of the application: A full-fledged enterprise-level application will take longer than a proof-of-concept. * Required accuracy and performance: Achieving higher accuracy often requires more iterative refinement. We provide a detailed project timeline after the initial discovery and requirements definition phase.
What's included
AI Application Development and Deployment Service
This service provides end-to-end support for the entire lifecycle of an Artificial Intelligence (AI) application, from initial concept and design to development, testing, and deployment. It encompasses: * Requirements Gathering and Analysis: Collaborating with stakeholders to define the problem, identify use cases, and gather specific functional and non-functional requirements for the AI application. * AI Model Selection and Development: Researching, selecting, and developing appropriate AI models (e.g., machine learning, deep learning, natural language processing, computer vision) based on the application's needs and available data. This includes data preparation, feature engineering, model training, and hyperparameter tuning. * Application Architecture Design: Designing a robust and scalable architecture for the AI application, integrating the AI models with necessary front-end interfaces, back-end services, databases, and third-party APIs. * Software Development: Writing clean, efficient, and well-documented code for the entire application, adhering to best practices and coding standards. This includes developing APIs, user interfaces, data pipelines, and integration modules. * Testing and Quality Assurance: Conducting comprehensive testing, including unit tests, integration tests, system tests, and user acceptance tests (UAT) to ensure the AI application functions correctly, meets performance criteria, and delivers accurate results. This also involves validating AI model performance and bias. * Deployment and Infrastructure Setup: Deploying the AI application to the chosen infrastructure (cloud, on-premise, edge devices), configuring necessary environments, and setting up monitoring and logging systems. * Documentation and Training: Providing comprehensive technical documentation for the application and AI models, as well as user manuals and training materials for end-users and administrators. * Post-Deployment Support and Maintenance (Optional Add-on): Offering ongoing support, bug fixes, performance optimization, and model retraining/updates to ensure the AI application continues to operate effectively and adapt to changing requirements or data.
Skills and tools
Backend Engineer
ML Engineer
Web Developer
DeepAI
Google ML Engine
MongoDB
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