Enterprise Generative AI Development
SERVICES
Our Enterprise Generative AI development services
Generative AI End-to-End Development
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 Enterprise AI solution on various platforms.
Generative AI Consulting
We help our clients by consulting them on Enterprise Generative AI solutions that are 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 why, our 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 Enterprise-level 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.
MedMe
AI-Assisted Diagnosis. Leveraging AI, MedMe transforms patient symptom descriptions into actionable insights using advanced NLP algorithms. It connects symptoms to diseases and treatments through medical knowledge graphs, seamlessly integrates with e-prescription platforms, and employs AI for health data analysis, triggering alerts for potential health risks or medication non-compliance.
Roam
Car Subscription Solution. The solution uses city infrastructure APIs to integrate traffic and parking data, enhancing subscription flexibility. It syncs with fitness trackers and calendars. The AI system processes this data, predicting needs and adjusting subscriptions dynamically. It suggests appropriate car types, mileage allowances, or alternative transport options based on changing user needs and lifestyles.
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 Enterprise Generative AI 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 for Enterprises
Generative AI Development for Enterprises
Generative AI Model Replication for Enterprises
Generative AI Support for Enterprises
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.
AI MODELS
AI powered models we are experienced in
PROCESS
Our Generative AI Development Process for Enterprises
Our Generative AI developers undertake a meticulous approach to better understand your company’s objectives and how to create an engaging, user-friendly, and smooth Generative AI solutions for your target audience.
Define the problem
We need to first understand the problem we want to solve using a generative ai product. We start with a conversation with our clients and put together their specific requirements to identify the type of content to be generated, their target audience, and content use cases.
Gather Data
Data collection is the next step in the generative AI process. Using various technologies we first identify the source of data and then collect the data and preprocess it to build the AI model. Examples of sources can be existing product designs, user-generated content, or user-research.
Design the model
Our AI engineers then use appropriate ML and DL algorithms and neural networks to architect the solution model based on the problem.
Train & Evaluate the model
Once the model is designed with the preprocessed data, we train the model, adjust the parameters and evaluate the output for quality and accuracy checks.
Deploy the model
After the model is ready, our team deploy it on existing deployment devices and make sure they are integrated into existing product or service.
Monitor and optimize
Monitoring the generated content, its efficiency, accuracy and optimization is key to the whole process. We continuously monitor the generative ai product and test the outputs and make necessary adjustments to get the desired results.
Define the problem
We need to first understand the problem we want to solve using a generative ai product. We start with a conversation with our clients and put together their specific requirements to identify the type of content to be generated, their target audience, and content use cases.
Gather Data
Data collection is the next step in the generative AI process. Using various technologies we first identify the source of data and then collect the data and preprocess it to build the AI model. Examples of sources can be existing product designs, user-generated content, or user-research.
Design the model
Our AI engineers then use appropriate ML and DL algorithms and neural networks to architect the solution model based on the problem.
Train & Evaluate the model
Once the model is designed with the preprocessed data, we train the model, adjust the parameters and evaluate the output for quality and accuracy checks.
Deploy the model
After the model is ready, our team deploy it on existing deployment devices and make sure they are integrated into existing product or service.
Monitor and optimize
Monitoring the generated content, its efficiency, accuracy and optimization is key to the whole process. We continuously monitor the generative ai product and test the outputs and make necessary adjustments to get the desired results.
Our proud clients
EXPERTISE
Our Expertise in Enterprise 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 diffusion model-driven solutions that transform textual data into visual data.
2. Fine Tuning
Fine-tuning Stable Diffusion models on a smaller dataset can 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.
3. 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 Stable Diffusion deep learning architecture specifically designed for NLP tasks to engineer high-performing solutions.
4. 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
ABOUT US
Pioneering AI Solutions
Since 2020
Pioneering AI Solutions Since 2020
We’re business strategists and pioneers in AI technologies. Our in-depth knowledge of Artificial Intelligence technologies, models, and frameworks allows us to build a flawless AI ecosystem specifically designed for your business needs. By partnering with us, you’re not just solving problems, you’re leveraging AI to transform your existing systems and set the stage for sustainable success and growth.
65+
AI Solutions
30+
AI Engineers
12+
Work Experience
What our clients say:
TOOL & TECHNOLOGY
Technology stack we use to build Generative AI Enterprise solutions
Our Generative AI developers recommend the best technology stack to develop digital solutions for Enterprises.
BENEFITS
Enterprise Generative AI 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.
FAQ’s
About Enterprise Generative AI Development
How will Generative AI integrate with our existing Enterprise systems and workflows?
Generative AI integrates with existing systems and workflows by leveraging APIs and middleware for seamless data exchange. It complements current processes through machine learning and automation, enhancing efficiency without disrupting established operations. This allows Generative AI to provide insights and augment decision-making while fitting into the existing technological ecosystem.
What approach do you use for model validation, testing, and performance evaluation?
We employ cross-validation methods and metrics such as accuracy, precision, recall, and F1-score to rigorously validate and test models, ensuring robust performance.
What regulatory and compliance considerations do you address for Enterprise Generative AI projects?
We ensure compliance with relevant regulations and standards, collaborating with legal experts to adhere to GDPR or industry-specific requirements.
Can you provide an estimate of the total costs involved in implementing and maintaining the Enterprise Generative AI solution?
The total costs encompass development, infrastructure, personnel, and ongoing maintenance, varying with project complexity. We provide detailed cost breakdowns during project scoping.
What is the expected Return on Investment (ROI) of implementing AI in Enterpise systems?
The ROI of AI implementation varies per project and includes factors like efficiency gains, cost savings, revenue generation, and improved decision-making. Detailed ROI analysis is provided during project planning.
What is the expected timeline for Enterprise Generative AI development and deployment?
The timeline for Generative AI development and deployment varies depending on the project’s complexity. Typically, it ranges from several months to over a year, including data preparation, model development, testing, and deployment phases.
Are there scalability options to accommodate future business growth or changing requirements?
Yes, we develop solutions are designed with scalability in mind, capable of handling larger datasets and increased demands, allowing adaptation to evolving business needs.
How will Generative AI integrate with our existing Enterprise systems and workflows?
Generative AI integrates with existing systems and workflows by leveraging APIs and middleware for seamless data exchange. It complements current processes through machine learning and automation, enhancing efficiency without disrupting established operations. This allows Generative AI to provide insights and augment decision-making while fitting into the existing technological ecosystem.
What approach do you use for model validation, testing, and performance evaluation?
We employ cross-validation methods and metrics such as accuracy, precision, recall, and F1-score to rigorously validate and test models, ensuring robust performance.
What regulatory and compliance considerations do you address for Enterprise Generative AI projects?
We ensure compliance with relevant regulations and standards, collaborating with legal experts to adhere to GDPR or industry-specific requirements.
Can you provide an estimate of the total costs involved in implementing and maintaining the Enterprise Generative AI solution?
The total costs encompass development, infrastructure, personnel, and ongoing maintenance, varying with project complexity. We provide detailed cost breakdowns during project scoping.
What is the expected Return on Investment (ROI) of implementing AI in Enterpise systems?
The ROI of AI implementation varies per project and includes factors like efficiency gains, cost savings, revenue generation, and improved decision-making. Detailed ROI analysis is provided during project planning.
What is the expected timeline for Enterprise Generative AI development and deployment?
The timeline for Generative AI development and deployment varies depending on the project’s complexity. Typically, it ranges from several months to over a year, including data preparation, model development, testing, and deployment phases.
Are there scalability options to accommodate future business growth or changing requirements?
Yes, we develop solutions are designed with scalability in mind, capable of handling larger datasets and increased demands, allowing adaptation to evolving business needs.
GENERATIVE AI
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