Generative AI Development Company
How do we build a Generative AI solution for businesses?
Markovate leverages advanced algorithms and data-driven insights to deliver unparalleled accuracy and relevance. With a keen focus on data security, model architecture, model evaluation, data quality and MLOps management, we can develop a highly competitive Generative AI applications for our clients.
Preprocess the data
We understand that the data may not be always ready for us so we use techniques like imputation, outlier detection and data normalization to preprocess the data effectively and to remove noise and inconsistencies. Our AI engineers also do feature engineering based on domain knowledge and experimentation to enhance the power of the AI model.
Data security
Our AI engineers use role-based access control (RBAC) and implement multi-factor authentication (MFA) for data security. They adhere to strong encryption techniques to protect sensitive data and use encryption protocols such as SSL/TLS for data transmission and AES for data storage. Additionally, they apply robust access control mechanisms to restrict access to sensitive data only to authorized users.
Evaluation of Models
We use cross-validation techniques such as k-fold cross-validation to evaluate the performance of AI models. This involves splitting the data into multiple subsets and training the model on different combinations of subsets to assess its performance based on accuracy, precision, recall, F1 score, and ROC curve. We also give great importance to Hyperparameter tuning and use different model architectures to optimize the model performance that aligns with the specific objectives and requirements of the Generative AI solution.
MLOps Management
Our MLOps will help in automation of key ML lifecycle processes to optimize the deployment, training and data processing costs. We use techniques like data ingestion, tools like Jenkins, GitLab CI and continuously do cost-impact analysis for building a low-cost solution for your business. Our team also does infrastructure orchestration to manage resources and dependencies to ensure consistency and reproducibility across environments.
Production-grade model scalability
Large models require significant computational resources, therefore we optimize the model for better performance without sacrificing output quality. For scalability, we use techniques like quantization, pruning and distillation to support growing number of requests. We also balance the need for additional resources with cost considerations, potentially through cost-optimized resource allocation or by identifying the most cost-effective scaling strategies.
SERVICES
Our Generative AI powered development services
Generative AI End-to-End Development
Once we have defined the clients problem, we develop a functional Generative AI product or service modelled and trained to give the desired outputs. We use a combination of several technologies including deep learning, probabilistic programming, NLP and neural networks to train and deploy the AI solution on various platforms.
Generative AI Consulting
We help our clients by consulting them on AI solution that is best suited to their needs. As generative AI is still evolving there could be many approaches to develop a custom AI-powered solution. That’s way, AI expert team can help clients understand the most efficient, low cost and low maintenance solution that is better suited.
Generative AI Model Replication
We build Generative AI products which are scalable and deployable easily on multiple devices. We use AWS and Microsoft Azure to easily deploy AI models. Our AI engineers has experience in containerization technology such as Docker or Kubernete which allows them to easy deploy generative AI-powered products on different platforms and devices.
Generative AI Support
We provide generative AI product Maintance and support service. Our main goal is to always maintain a high-quality content and ensure the AI model function effectively. We continuously train data and algorithms and make sure algorithms are improved over time as they learn. We also identify and rectify any issues or defects in the generated AI products.
Legal Assistant for crafting legal documents powered by Generative AI
We leveraged Generative AI to fasten legal document analysis and drafting. We utilized in-depth legal research, streamlining legal workflows, and improving document accuracy and research efficiency in the legal sector.
Incorporated sophisticated NLP and chatbot technology, tailored to legal jargon and protocols, it fosters effective user interactions, boosting communicative efficiency and making legal services more accessible.
Retail solution driven by a recommendation engine using Generative AI
We leveraged Generative AI to develop a recommendation system designed to analyze user behavior (Google Analytics, New Relic), and purchase history.
The recommendation engine enables the platform to offer personalized product suggestions tailored to each user’s genuine interests.
We helped NVMS by integrating AI for efficient property inspection and work order management. Utilizing ResNet and YOLOv8 algorithms within the robust AWS Bedrock platform, we’ve enabled NVMS to achieve precise house number and signage detection, enhancing the accuracy of their reporting.
SERVICES
Our Generative AI powered development services
We leverage our proficiency in various AI technologies, such as deep learning, machine learning, computer vision, reinforcement learning, and natural language processing.
Generative AI Consulting
Generative AI End-to-End Development
Generative AI Model Replication
Generative AI Support
We help our clients by consulting them on AI solution that is best suited to their needs. As generative AI is still evolving there could be many approaches to developing a custom AI-powered solution. That way, AI expert team can help clients understand the most efficient, low cost and low-maintenance solution that is better suited.
Once we have defined the client’s problem, we develop a functional Generative AI product or service modeled and trained to give the desired outputs. We use a combination of several technologies including deep learning, probabilistic programming, NLP, and neural networks to train and deploy the AI solution on various platforms.
We build Generative AI products which are scalable and deployable easily on multiple devices. We use AWS and Microsoft Azure to easily deploy AI models. Our AI engineers has experience in containerization technology such as Docker or Kubernete which allows them to easy deploy generative AI-powered products on different platforms and devices.
We provide generative AI product maintenance and support service. Our main goal is to always maintain high-quality content and ensure the AI model function effectively. We continuously train data and algorithms and make sure algorithms are improved over time as they learn. We also identify and rectify any issues or defects in the generated AI products.
PROCESS
What is our process for building Generative AI solutions
Data Preparation
Before we use any data, we help organization with cleaning, organizing, transforming raw data into a format that is suitable for training. This may include normalizing or standardizing numerical data, encoding categorical data, and possibly generating new features through various transformations to enhance model performance.
Data Pipeline
After gathering diverse and relevant datasets for training the generative AI model, we want to ensure data quality and relevance. Our team pre-processes the data and transforms it using techniques like data normalization, feature engineering, and imputation to minimize the data maintenance cost. Then we enhance the dataset and do data versioning to track changes and ensure reproducibility.
Experimentation
Based on the project requirements and objectives, we choose the appropriate architecture model such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), or Transformer models. Once we select the model, we train the selected model using the preprocessed quality data and evaluate it on performance metrics such as accuracy and relevance.
Data Evaluation
We rigorously evaluate the quality and relevance of the processed data to confirm its suitability for training. Leveraging advanced data evaluation tools like Guardrails, MLflow, and Langsmith, we conduct thorough assessment and validation processes. Additionally, we implement RAG techniques designed to detect and mitigate hallucinations within the generated outputs. We ensure that the model maintains high levels of groundedness and fidelity to the training data, minimizing the risk of producing inaccurate or misleading results.
Deployment
Once we have a trained model ready and any necessary dependencies into a deployable format, we deploy it to the production environment using platforms like TensorFlow, AWS SageMaker, or AzureML. Finally, we implement a monitoring system to track the model performance in production. We gather the user feedback and through the feedback loop, we improve the generative AI model over time.
Prompt Engineering
We define clear and concise prompts or input specifications for generating desired outputs from the generative AI model. We experiment with different prompt formats and styles to optimize model performance and output quality. And eventually integrate prompts seamlessly into the user interface or application workflow, providing users with intuitive controls and feedback mechanisms.
Data Preparation
Before we use any data, we help organization with cleaning, organizing, transforming raw data into a format that is suitable for training. This may include normalizing or standardizing numerical data, encoding categorical data, and possibly generating new features through various transformations to enhance model performance.
Data Pipeline
After gathering diverse and relevant datasets for training the generative AI model, we want to ensure data quality and relevance. Our team pre-processes the data and transforms it using techniques like data normalization, feature engineering, and imputation to minimize the data maintenance cost. Then we enhance the dataset and do data versioning to track changes and ensure reproducibility.
Experimentation
Based on the project requirements and objectives, we choose the appropriate architecture model such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), or Transformer models. Once we select the model, we train the selected model using the preprocessed quality data and evaluate it on performance metrics such as accuracy and relevance.
Data Evaluation
We rigorously evaluate the quality and relevance of the processed data to confirm its suitability for training. Leveraging advanced data evaluation tools like Guardrails, MLflow, and Langsmith, we conduct thorough assessment and validation processes. Additionally, we implement RAG techniques designed to detect and mitigate hallucinations within the generated outputs. We ensure that the model maintains high levels of groundedness and fidelity to the training data, minimizing the risk of producing inaccurate or misleading results.
Deployment
Once we have a trained model ready and any necessary dependencies into a deployable format, we deploy it to the production environment using platforms like TensorFlow, AWS SageMaker, or AzureML. Finally, we implement a monitoring system to track the model performance in production. We gather the user feedback and through the feedback loop, we improve the generative AI model over time.
Prompt Engineering
We define clear and concise prompts or input specifications for generating desired outputs from the generative AI model. We experiment with different prompt formats and styles to optimize model performance and output quality. And eventually integrate prompts seamlessly into the user interface or application workflow, providing users with intuitive controls and feedback mechanisms.
TOOL & TECHNOLOGY
Technology stack we use to build Generative AI-powered solutions
Our Generative AI developers recommend the best technology stack to develop digital solutions for business.
Our proud clients
AI-MODELS
Rich Expertise Across Diverse AI Models
GPT-3
A powerful language model capable of generating human-like text.
Davinci
It is a variant of GPT-3 with enhanced performance and larger capacity.
Curie
A variant of GPT-3 optimized for generating creative and engaging text.
Babbage
This smaller variant of GPT-3 is suitable for apps with limited computational resources.
Ada
A variant of GPT-3 designed for generating conversational responses.
GPT-3.5
An improved version of GPT-3, offering enhanced language generation capabilities and performance.
GPT-4
The next iteration of the GPT series, expected to provide even more advanced language generation abilities and improved performance.
DALL.E
A unique AI model capable of generating original images from textual descriptions, allowing for creative image synthesis.
Whisper
An AI model designed to enhance automatic speech recognition (ASR) systems, improving their accuracy and efficiency.
Embeddings
AI models focused on transforming text or other data into numeric representations, enabling more effective processing and analysis.
Moderation
AI models developed to assist in content moderation tasks, helping identify and flag potentially inappropriate or harmful content.
Stable Diffusion
An AI model designed for image manipulation tasks, allowing for controlled and stable editing of images while preserving their overall appearance.
Midjourney
AI models developed for recommendation systems, providing personalized suggestions and guidance during a user’s journey or experience.
Bard
An AI model specialized in generating creative and coherent storytelling narratives, mimicking the style of human authors.
LLaMA
An AI model focused on language learning and mastery, assisting users in acquiring new languages or improving their linguistic skills.
Claude
A versatile AI model designed for visual understanding and perception tasks, enabling machines to interpret and analyze visual data effectively.
EXPERTISE
Our Expertise in Generative AI Development
1. Machine Learning
Our developers possess extensive knowledge and experience in various AI development services. They can proficiently implement machine learning concepts such as predictive modeling, NLP, and deep learning to create robust Generative AI model-driven solutions that transform textual data into visual data.
2. Natural Language Processing (NLP)
Our expert team utilize NLP for sentiment analysis, named entity recognition, machine translation, and text summarization. We leverage NLP to transform raw text data into actionable insights, unlocking new possibilities for innovation and efficiency.
3. Generative Models
Our expertise in advanced machine learning and deep neural networks empowers businesses to leverage state-of-the-art AI models for a wide range of applications. From natural language understanding to image recognition and beyond, we specialize in deploying cutting-edge AI models tailored to your unique requirements.
4. Deep Learning
Our comprehensive knowledge of deep learning models enables us to leverage multi-layered artificial neural networks to model intricate patterns in data. Moreover, we adeptly utilize the deep learning architecture specifically designed for NLP tasks to engineer high-performing solutions for Generative AI models.
5. Fine Tuning
We fine-tuning Generative AI models on a smaller dataset to tailor them to a specific task, which is commonly referred to as transfer learning. By doing so, computation and data requirements for training a top-notch model for a particular use case can be reduced.
6. Transfer Learning
Our area of expertise lies in transfer learning, an AI technology that enables the utilization of pre-trained models on comparable tasks, which enhances performance and reduces training time. We possess a profound understanding of how to leverage pre-trained models to address specific issues, resulting in efficient and effective solutions.
Our Collaboration Partners
BENEFITS
Generative AI based solutions development benefits
1. Increased Creativity
Using techniques like generative adversarial networks (GANs), RNNs, VAEs, and NLP, there are a lot of possibilities to create new data which is unique in its way. We can also use Generative AI to provide new ideas and concepts which can help to stimulate and improve creative thinking.
2. Increased Efficiency
With the help of Generative AI we can increase your work efficiency. Redundant tasks can be automated, insights and predictions can be used for faster decision-making, and by tailoring interfaces, we can help you reduce errors and find areas of improvement faster in your existing products.
3. Increased Personalization
Generative AI can be also used to enhance the personalization of your product and user experiences. Be it for marketing, or learning experiences, generative AI can be vitally applied for content creation for more specific and user-centric solutions.
4. Increased Innovation
We can help you explore more datasets and patterns and generate innovative solutions to build a smarter AI product for your target audience. Also, products can be built better with new perspectives, insights, and feedbacks generated using Generative AI.
INDUSTRIES
We build AI-powered digital products across various industries
Healthcare
Fintech
Retail
Saas
Travel
Fitness
Oil & Gas
Energy
Education
INDUSTRY INSIGHTS
Industry-Specific AI Case Studies
Orchestrating Integrated Healthcare Solutions
Enhancing Security with Threat Detection
Driving Engagement with Intelligent Solutions
FAQ’s
Generative AI Development
What exactly is Generative AI?
Generative AI refers to a type of artificial intelligence that is capable of generating new data or content, such as images, music, text, or even entire virtual worlds. This is in contrast to other types of AI, such as discriminative models, which are designed to classify or recognize existing data.
What is the usual expense involved in creating a generative AI model?
The cost associated with developing a generative AI model can vary widely depending on various factors such as the complexity of the model, the amount of data required to train the model, the compute resources needed for training, the expertise of the developers involved, and the time taken to develop the model.
What are some examples of generative AI applications?
Some examples of generative AI applications include generating realistic images, videos, and audio, creating text, and synthesizing new data based on existing data.
How do you integrate Generative AI into a business?
Our process for integrating Generative AI is thorough and efficient. We start by curating diverse datasets, ensuring quality through preprocessing and versioning. We then choose the right model, train it, and deploy to production using platforms like TensorFlow. Continuous monitoring and user feedback drive iterative improvements. Finally, we engineer clear prompts for seamless integration into user workflows, optimizing user experience and business outcomes.
How do you ensure ethical and responsible use of AI in your Gen AI development process?
We prioritize transparency, fairness, and accountability in all aspects of our projects, from data collection and model training to deployment and ongoing monitoring. By adhering to strict ethical guidelines and industry best practices, we ensure that our solutions uphold the highest standards of integrity and trustworthiness.
How can I get started with your Gen AI development services for my business?
Simply reach out to our team to schedule a consultation. We’ll discuss your business objectives, target audience, and specific requirements to tailor a Generative AI solution that meets your needs. From conceptualization to deployment and beyond, we’ll be with you every step of the way to ensure success.
What exactly is Generative AI?
Generative AI refers to a type of artificial intelligence that is capable of generating new data or content, such as images, music, text, or even entire virtual worlds. This is in contrast to other types of AI, such as discriminative models, which are designed to classify or recognize existing data.
What is the usual expense involved in creating a generative AI model?
The cost associated with developing a generative AI model can vary widely depending on various factors such as the complexity of the model, the amount of data required to train the model, the compute resources needed for training, the expertise of the developers involved, and the time taken to develop the model.
What are some examples of generative AI applications?
Some examples of generative AI applications include generating realistic images, videos, and audio, creating text, and synthesizing new data based on existing data.
What is the typical timeline for developing AI models?
The process of developing an AI model can take several months to a few years. It usually involves steps such as data collection and preprocessing, selecting and designing an appropriate model architecture, training and testing the model, and fine-tuning it for optimal performance.
It’s important to note that developing AI models is an iterative process, and it may require multiple cycles of development and refinement to achieve the desired level of performance.
How do you integrate Generative AI into a business?
Our process for integrating Generative AI is thorough and efficient. We start by curating diverse datasets, ensuring quality through preprocessing and versioning. We then choose the right model, train it, and deploy to production using platforms like TensorFlow. Continuous monitoring and user feedback drive iterative improvements. Finally, we engineer clear prompts for seamless integration into user workflows, optimizing user experience and business outcomes.
How do you ensure ethical and responsible use of AI in your Gen AI development process?
We prioritize transparency, fairness, and accountability in all aspects of our projects, from data collection and model training to deployment and ongoing monitoring. By adhering to strict ethical guidelines and industry best practices, we ensure that our solutions uphold the highest standards of integrity and trustworthiness.
How can I get started with your Gen AI development services for my business?
Simply reach out to our team to schedule a consultation. We’ll discuss your business objectives, target audience, and specific requirements to tailor a Generative AI solution that meets your needs. From conceptualization to deployment and beyond, we’ll be with you every step of the way to ensure success.
GENERATIVE AI
Point of view
Our thought leadership initiative – an exclusive platform for sharing our insights and technological perspectives.
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