Generative AI Consulting and Development Services

• Identifying the Objective and Understanding Data Culture: The development process starts with a deep due diligence session where our generative AI experts understand the problem the business aims to solve or the opportunity, they wish to capitalize on with generative AI solutions. We also try to figure out the data culture in the company to prepare our strategy for the next step.  

• Success Metrics: Our team tries to establish quantifiable metrics to measure the success of the generative AI solutions that we are going to build. This might involve metrics like image realism or text coherence. 

• Data Collection: In this phase, we gather the necessary data to train the generative AI model. As we know, the quality and quantity of data significantly impact the model’s performance. This phase involves collecting existing data, purchasing datasets, or even creating synthetic data. 

• Data Cleaning and Preprocessing: We clean the collected raw data to eliminate errors, inconsistencies, and irrelevant information for a better accuracy rate. This involves tasks like handling missing values, normalization, and formatting data for compatibility with the chosen generative AI model. 

• Data Augmentation: This is an optional phase depending on the business goals. If the existing data is scarce, we use techniques like data augmentation to create additional data points that resemble the existing data. This increases the diversity and robustness of the developed model. 

Based on the identified business needs and data type, we select the most suitable generative AI approach. The fundamental approaches we use for generative AI modeling are: 

Generative Adversarial Networks or GANs: 

We use GANs for business goals related to image generation, data augmentation, and creative content generation. Here are some of the common applications of GANs: 

• Stock Photography: Creating royalty-free images for various uses. 

• Innovative Design: Generating new designs and variations of products. 

• Game Development: Creating realistic textures and environments for gaming consoles. 

• Medical Imaging: Generating synthetic medical images for training and testing diagnostic algorithms. 

• Satellite imagery: Creating additional data for tasks like land cover classification. 

• Marketing campaigns: Generating personalized advertisements based on user preferences. 

• Art and music composition: Exploring new creative possibilities. 

Variational Autoencoders or VAEs: 

VAEs are adept at learning the underlying structure of data. They can identify data points that deviate significantly from this structure, potentially indicating anomalies. VAEs can compress data into a lower-dimensional latent space while retaining essential characteristics. Here are some of the applications we developed with VAEs: 

• Fraud Detection: Flagging unusual transactions in financial data. 

• Equipment Failure Prediction: Identifying anomalies in sensor data that can predict equipment failure. 

• Image and Video Compression: Reducing file sizes for storage and transmission. 

• Recommendation Systems: Identifying patterns in user behavior for personalized recommendations. 

• Restoring Old Photographs: Filling in damaged sections. 

• Medical Imaging: Reconstructing missing data in medical scans. 

Autoregressive Models: 

We usually leverage autoregressive models for text generation and data-driven forecasting. Here are some of the applications which are powered by autoregressive models: 

• Machine Translation: Translating text from one language to another. 

• Chatbots: Creating chatbots that can hold conversations with users. 

• Content Creation: Generating creative text formats like summaries, letter writing, poems, or scripts. 

• Stock market prediction: Predicting future stock prices  

• Sales forecasting: Predicting future demand for products 

• Model Design and Architecture: We design the architecture of the selected generative AI model by considering factors like the type of neural networks, activation functions, and optimization algorithms to be used. 

• Model Training: We train the developed model on the prepared data. This is an iterative process that involves training, monitoring performance, and fine-tuning parameters as needed. 

• Model Performance Assessment: Our generative AI developers evaluate the performance of the model against the established success metrics.  

• Model Refinement: Based on the evaluation results, we refine the model architecture or training parameters to improve its performance and accuracy rate.

• Deployment: First, we develop a strategy for deploying the generative AI model into production. We have expertise in integrating generative AI solutions into existing operational infrastructure. 

• Ensuring Scalability: We ensure the deployment plan can handle potential increases in user traffic or data volume. 

• Security & Privacy: As a top-rated generative AI development company, we give special attention to implementing security measures to protect sensitive data used by the model and ensure compliance with relevant data privacy regulations like GDPR or HIPAA. 

• Performance Monitoring: ThirdEye’s generative AI experts continuously monitor the performance of the deployed generative AI model to identify any potential issues like accuracy degradation or bias. 

• Ongoing Training and Maintenance: We provide post-deployment support to update the model with new data or retrain it periodically to maintain its effectiveness, relativity, and adapt to evolving requirements.