Adaptive AI Development Company
Harness the power of adaptive AI to transform your business operations and improve customer satisfaction. With deep expertise in machine learning, deep learning, neural networks, NLP and other AI subsets, our adaptive AI developers build highly performant custom adaptive AI solutions.
Fullstack Developers
Software Products Delivered
AI Solutions
Total Years of Experience
Our Adaptive AI Development Services
Consulting and Strategy Development
Custom Adaptive AI Model-based Solutions Development
Maintenance and Upgrade
Model Integration and Deployment
Data Engineering
Our Technical Expertise in Adaptive AI Development
Machine Learning
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Cloud Computing
Predictive Analytics
Our Adaptive AI Systems Development Process
Identify the Problem
The first step in developing an adaptive AI solution is to identify the problem you want to solve. This involves understanding the business need or user requirement that the AI will address, as well as any constraints or limitations on the solution.
Gather Data
Once you’ve identified the problem, you need to gather relevant data to train your AI model. This may involve collecting data from a variety of sources, including internal company data, public datasets, and user-generated content.
Clean and Preprocess Data
After you’ve gathered your data, you need to clean and preprocess it to ensure that it’s suitable for training your AI model. This involves removing duplicates, handling missing values, and transforming the data into a format that can be used by your model.
Develop and Train the Model
With your data cleaned and preprocessed, you can begin to develop your AI model. This involves selecting an appropriate algorithm or approach, tuning hyperparameters, and training the model on your data.
Validate and Test the Model
Once your model is trained, you need to validate and test it to ensure that it’s performing as expected. This involves evaluating the model’s accuracy, precision, recall, and other performance metrics, and making any necessary adjustments to improve its performance.
Deploy the Model
Once your model has been validated and tested, you can deploy it in a production environment. This may involve integrating the model with other systems, such as a web or mobile app, and setting up infrastructure to support the model’s ongoing operation.
Monitor and Update the Model
After your model has been deployed, you need to monitor its performance to ensure that it’s continuing to perform as expected. This may involve setting up monitoring and alerting systems and periodically updating the model to improve its performance based on new data or user feedback.
AI Models We Have Expertise in
GPT-3
A set of OpenAI models capable of performing natural language processing tasks such as text generation, summarization, translation and question answering.
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Davinci
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Curie
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Babbage
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Ada
GPT-3.5
A set of OpenAI models, including the highly capable and cost-effective Gpt-3.5-turbo, that improve on GPT-3 and can generate text or code.
GPT-4
A set of OpenAI models that can solve complex problems with high accuracy, thanks to its advanced reasoning capabilities and broader general knowledge.
DALL.E
DALL·E by OpenAI generates realistic images and artwork based on text prompts. It can produce images of a specified size, modify pre-existing images and generate variations of user-provided images.
Whisper
Whisper is a general-purpose speech recognition OpenAI model that can perform language identification, speech translation and multilingual speech recognition.
Embeddings
OpenAI's Embeddings are numerical representations of linguistic units like words and phrases that capture the semantic meaning and relationships between them.
Moderation
Moderation models are machine learning OpenAI models designed to assist in content moderation tasks, such as identifying and removing inappropriate or harmful content from online platforms.
Stable Diffusion
Midjourney
Bard
Google's Bard, powered by LaMDA, is a text-to-text generative AI chatbot designed to generate human-like responses to natural language prompts, making it capable of engaging in conversations with humans.
LLaMA
Our Adaptive AI Systems Development Stack
AI Frameworks
Cloud Platforms
Integration and Deployment Tools
Programming Languages
Databases
Algorithms
Supervised/Unsupervised Learning
Clustering
Metric Learning
Fewshot Learning
Transfer Learning
Ensemble Learning
Online Learning
Neural Networks
CNN
RNN
Representation Learning
Manifold Learning
Variational Autoencoders
Bayesian Network
Autoregressive Networks
Long Short-term Memory (LSTM)
Our Artificial Intelligence Portfolio
MakeMyTale
The AI-Powered Story Creation Platform
MakeMyTale is a cutting-edge story creation and sharing platform that leverages advanced AI technology to deliver a truly personalized experience. Its user-friendly interface empowers users to shape the theme and characters of their story with ease. The platform’s AI-powered audio and video creation capabilities bring stories to life by generating captivating audio and visual versions. Additionally, the option for co-authoring enables seamless sharing with a global audience.
Vrapy
Automated Attendance via Face Recognition
Vrapy is a revolutionary platform that uses facial recognition technology to automate attendance tracking. The platform offers a range of features including 100% automation, integration with existing cameras, mobile device detection, and real-time attendance alerts. Vrapy also provides insights into attentiveness levels and detects theft and violent behavior. With its heatmap generation feature, Vrapy provides valuable insights into space utilization, making it a comprehensive solution for attendance tracking.
ReCrisp
AI SEO Optimizer
Hi Arya!
World’s First Robotic Tea Maker
Arya is the First Chai making robot having the capabilities of AI. It can detect a user’s face using computer vision and reply back with an exact recipe name by predicting the user’s behavior using Machine Learning(ML). It uses Speech recognition and NLP to interact with the user to take the next order.
Our Adaptive AI-based Development Services Cater to a Wide Array of Industries
Big Brands Trust Us
Our Engagement Models
Dedicated Development Team
Our blockchain developers are hands-on the cognitive technologies to deliver high-quality services and solutions to clients.
Team Extension
Our team extension model is intended to help clients who want to extend their team with the right expertise required for their project.
Project-based Model
Our project-based model and software development specialists are there for customer collaboration and specific client project engagement.
Get Started Today
1. Contact Us
Fill out the contact form protected by NDA, book a calendar and schedule a Zoom Meeting with our experts.
2. Get a Consultation
Get on a call with our team to know the feasibility of your project idea.
3. Get a Cost Estimate
Based on the project requirements, we share a project proposal with budget and timeline estimates.
4. Project Kickoff
Once the project is signed, we bring together a team from a range of disciplines to kick start your project.
Start a conversation by filling the form
Once you let us know your requirement, our technical expert will schedule a call and discuss your idea in detail post sign of an NDA.
All information will be kept confidential.
FAQs
What is adaptive AI development?
Adaptive AI development refers to the process of building AI systems that can automatically adjust and improve their performance based on feedback from the environment or user interactions.
How is adaptive AI different from traditional AI?
Traditional AI systems are static and operate based on a fixed set of rules or algorithms. Adaptive AI systems, on the other hand, can dynamically adjust their behavior and learn from experience.
What are some examples of adaptive AI?
Examples of adaptive AI include recommender systems that learn from user behavior to make better recommendations, chatbots that improve their responses based on user feedback, and autonomous vehicles that adjust their driving behavior based on real-time sensor data.
What are the benefits of adaptive AI?
Adaptive AI can improve system performance, enhance user experience, and reduce the need for manual intervention or programming
Do you also sign NDA?
Yes, we can sign an NDA and ensure that your information is kept confidential.
What are some challenges of developing adaptive AI?
Challenges of developing adaptive AI include managing complex data sets, addressing issues of bias and fairness, and ensuring the safety and reliability of the system.
What is the future of adaptive AI?
The future of adaptive AI is likely to see continued growth and innovation, with applications in fields such as healthcare, finance, and education, as well as the development of more sophisticated algorithms and techniques for building adaptive systems.
What are some challenges of developing adaptive AI?
Challenges of developing adaptive AI include managing complex data sets, addressing issues of bias and fairness, and ensuring the safety and reliability of the system.
Insights
Data annotation: The key to AI model accuracy
Data annotation is adding labels or tags to a training dataset to provide context and meaning to the data.
Optimizing pre-trained models: A guide to Parameter-efficient Fine-tuning (PEFT)
Parameter-efficient Fine-tuning (PEFT) is a technique used in Natural Language Processing (NLP) to improve the performance of pre-trained language models on specific downstream tasks.
How to build a private LLM?
Language models are the backbone of natural language processing (NLP) and have changed how we interact with language and technology.