Our mastery in various AI models allow us to build robust solutions.
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As a forerunner of Generative AI development services, our team dedicatedly helps clients by consulting them on ideal AI solutions for their requirements. We leverage the expertise in numerous AI technologies from Machine Learning to NLP, to create powerful Generative AI models and solutions built after ChatGPT, Stable Diffusion and DALL-E. This way, our AI experts help clients understand the most effective, low-cost and maintenance solution ideal for their requirements.
Our platform employs state-of-the-art techniques, including Transfer Learning, Fine-Tuning, and Meta-Learning, to efficiently train models on your proprietary data, ensuring optimal performance and minimizing resource consumption. Additionally, our service provides robust customization options, enabling you to fine-tune the model's parameters and hyperparameters to best suit your application.
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The OpenAI API Integration services make it easy to start using the OpenAI API to power your apps. With our developers' bespoke experience in OpenAI API integration, we create applications that can translate languages, write creative content, answer questions in an informative way.
With proficiency in diverse Machine Language and Natural Language Processing subsets and toolkits, our AI professionals specialize in developing custom large language models and LLM-based solutions that understand, generate and process content.
DALL.E developed by OpenAI, can create realistic images and artwork from text prompts. It can create images of any size, alter present images, and generate variations of user-provided images.
Stable Diffusion is a text-to-image AI that creates detailed images from text prompts. It can also be used for tasks such as inpainting, outpainting, and image-to-image translations guided by text.
Midjourney is an AI-powered image that creates artistic images in response to contextual prompts.
Whisper is a speech recognition OpenAI model that can perform language translation, identification and multilingual speech recognition.
Deepgram is a high-accuracy speech recognition tool popular for its ability to transcribe audio in real time. It is a considerable choice for apps that need real-time transcription.
Claude is a large language model (LLM) by Anthropic, trained as a virtual assistant that can be integrated with business workflows. Claude, accessible through a chat interface and API in Anthropic’s developer console, can perform numerous conversational and text-processing tasks.
PaLM 2 is Google’s next-generation large language model that builds on their legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than their previous state-of-the-art LLMs, including PaLM1.
LLaMA 2 is an open-source large language model (LLM) developed by Meta that enables developers and organizations to build generative AI-powered tools and experiences. With its commercial license and easy deployment options on cloud platforms such as Microsoft Azure, Amazon Web Services, and Hugging Face, LLaMA 2 is the perfect solution for creating a private LLM.
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We start by identifying the target audience and their requirements, as well as any specific challenge that needs attention to be addressed. This step ensures the Generative AI model is addressing the purpose and maximizing user benefits.
The next step is to collect data that will be used to train the ChatGPT model. Using various technologies, we will identify the data sources, gather the data, and preprocess it to create the AI model. The quantity and quality of the collected data will directly impact the accuracy and effectiveness of the resulting generative AI model.
Designing the generative AI model is the next step after data collection. This involves selecting the appropriate architecture, hyperparameters, training methodology, and optimization algorithms. Our AI engineers utilize machine learning, deep learning algorithms, and neural networks to design the solutions tailored to the problem
After designing comes training and fine-tuning the model. This includes putting the preprocessed data into the model and adjusting the model parameters according to the results obtained. The resulting Generative AI model is tuned-up until its performance on the validated dataset is acceptable.
Test to evaluate the resulting Generative AI product performance. This includes using a different data set to observe how well the model can predict new inputs and produce appropriate responses. The testing process helps find areas of improvement and provides insights about how the model can be improved.
Once the model is ready, our team deploy it on current deployment services and ensures they integrate easily with existing products and services.
Our Technology stack to develop AI solutions for business. Our team of developers, testers and analysts are equipped with a powerful stack of AI and machine learning frameworks, including:
Have a question in mind? We are here to answer.
What is Generative AI?
How to integrate Generative AI into a business?
How much does a generative AI model cost?
How much time does it take to develop AI models?
Developing AI models includes data collection and processing, designing a model architecture, training and testing the model, and fine-tuning it for optimal performance. This may take several months as it’s an iterative process and needs multiple cycles of development and refinement to achieve the desirable performance.
Which industries benefit from Generative AI?
Generative AI usually benefits every industry and business type by maximizing productivity, automating tasks, enabling new creation forms, facilitating deep analysis of complex data sets, or even building synthetic data on which future AI models will train. Many different federal applications also use generative AI technology.
What is the challenge regarding Generative AI?
The biggest concern regarding Generative AI is the potential to recognize or validate content that has been created by AI instead of a human being. Another challenge, addressed as “technological singularity”, is that AI will become sentient and surpass human intelligence.
What are some examples of Generative AI?
Some popular Generative AI interfaces are Bard, ChatGPT, Dall-E, Midjourney and DeepMind.
How do you ensure data security in AI development?
What kind of support do you offer after deployment?
