Generative AI Consulting and Development Services

Generative AI Solutions Trusted by the Leading Companies

Unlike its predecessors focused on analysis and classification, generative AI technologies delve into the fascinating realm of content creation. From generating realistic images to composing captivating music, generative AI technologies are raising the bar of what machines can achieve. However, enterprises are still not sure how to implement these technologies due to the intricating nature of the generative AI models.   

ThirdEye is one of the leading generative AI consulting companies. We focus on two primary problem statements of enterprises, first, how to make the generative implementation process cost-effective, and second, how to integrate them into an existing operating system without disrupting the day-to-day activities.  

Along with extensive experience in AI development, we have gained hands-on expertise in implementing generative AI models like GPT, PaLM, Dall-E, Gemini, Claude, Llama, NeMo Megatron into real-world industry use cases for Fortune 500 companies. We blend our Artificial Intelligence and Machine Learning development expertise with the trending generative AI models to build bespoke generative AI solutions to cater specific business needs.  

List of Our Generative AI Services:

  • Generative AI consultation with experts to evaluate the viability and potential ROI for generative AI implementations for your business.  
  • Strategic planning and cost calculations for aligning the preferred generative AI solutions with your overall business goals.  
  • We develop custom generative AI or large language models for your unique needs. We mainly use GANs for image generation and VAEs for data compression tasks.  
  • As a leading data engineering company, we also do cleaning, processing, and potentially creating new variations of data to train the generative AI models.  
  • Integration of explainable AI or XAI techniques to ensure transparency and trust in your generative solutions.  
  • Integrate newly developed generative AI models into an existing infrastructure for smooth operation.  
  • We provide post-deployment support to ensure continuous learning approaches to keep the generative AI model relevant to changing datasets.  

Phases of Our Generative AI Development Process:

  • 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. 

Fundamental Approaches We Use for Generative AI Modeling

Generative Adversarial Networks or GANs

We can take GANs as a competition between two neural networks – a generator and a discriminator. The generator strives to create new data instances that are indistinguishable from real data. On the other hand, The discriminator tries to differentiate between real and generated data. This continuous adversarial training process pushes both networks to improve, resulting in increasingly realistic and sophisticated generated outputs.

Variational Autoencoders or VAEs

VAEs compress the input data into a lower-dimensional latent space that captures the essential characteristics of the data. This latent space can then be used to generate new data instances by sampling from it. VAEs are particularly adept at generating diverse outputs while maintaining consistency with the training data. VAEs are heavily used in image restoration, anomaly, and fraud detection.

Autoregressive Models

This class of generative AI models generates data one piece at a time, predicting the next element based on the previously generated elements and the training data. While effective for tasks like text generation, autoregressive models can be computationally expensive for complex data formats like images and videos. We leverage autoregressive models for text-based automation, data-driven predictions and chatbot development.

Generative AI Technologies and Tools We Leverage

Function of This Area: In this area of work, we gather and clean raw data to train the model. 

Technologies and Tools We Use: 

  • Data Acquisition APIs: Public data repositories APIs, Ethically used web scraping tools 
  • Data Collection: Web crawlers, Ethically used web scraping tools 
  • Cloud Storage: AWS S3, Google Cloud Storage, Microsoft Azure Blob Storage 
  • Data Cleaning & Preprocessing: Pandas, OpenRefine, Trifacta Wrangler 
  • Data Version Control Systems: Git Large File Storage or LFS 

Function of This Area: We design and build the generative AI model based on the chosen approach and data type. 

Technologies and Tools We Use:  

  • Deep Learning Frameworks: TensorFlow, PyTorch, Keras. 
  • Generative AI Frameworks & Libraries: TensorFlow.js Generative Models, StyleGAN2, Disco Diffusion, Stable Diffusion, OpenAI Gym 
  • Large Language Models or LLMs: GPT-4 of OpenAI, PaLM of Google AI, Jurassic-1 Jumbo of AI21 Labs, WuDao 2.0 of BAAI, Megatron-Turing NLG of NVIDIA 
  • Image Generation Models: Dall-E 2, Stable Diffusion, Imagen 
  • Text-to-Image Generation Models: GauGAN2, LaMDA 
  • Multimodal Models: Megatron-Turing NLG 
  • Machine Learning Libraries: Scikit-learn, XGBoost 
  • Deep Learning Development Environments: Jupyter Notebooks, Google Colab 
  • Model Visualization Tools: TensorBoard, 

Function of This Area: We assess the performance of the trained model and refine it as needed based on the evaluation results. 

Technologies and Tools We Use: 

  • Evaluation Metrics Libraries: Scikit-learn metrics module, TensorFlow metrics 
  • Generative AI Specific Metrics Libraries: Frechet Inception Distance or FID for image generation, Inception Score or IS for image generation, BLEU score for text generation, ROUGE score for text generation 
  • Model Monitoring & Logging Tools: MLflow, Weights & Biases 

Function of This Area: In this area of the development process, we integrate the trained model into a real-world application for production use. 

Technologies and Tools We Use: 

  • Container: Docker, Kubernetes 
  • Cloud Platforms: AWS SageMaker, Google AI Platform, Microsoft Azure Machine Learning 
  • Model Deployment Tools: TensorFlow Serving, PyTorch Serving 
  • API Development Frameworks: Flask, FastAPI of Python 

Function of This Area: Our generative AI experts continuously monitor the performance of the deployed generative AI model and ensure its effectiveness over time. 

Technologies and Tools We Use: 

  • Cloud Monitoring Platforms: AWS CloudWatch, Google Cloud Monitoring, Microsoft Azure Monitor 
  • Model Explainability or XAI Tools: LIME, SHAP 

Customer Success Stories

AI Floor Generation, 3D Modeling and Interior Design Software

A design firm aimed to develop an AI-powered interior design software to automate and streamline the design process. We developed a compact software powered by the latest Generative AI technologies.

Generative AI-powered Travel Planning Platform

Developed a Generative AI-powered travel planning platform as MVP for busy professionals seeking well-curated itineraries.

Generative AI-powered Document Analytics Platform for an Audit Firm

Developing a Generative AI-based document analytics platform to extract pertinent entities from a variety of file formats, such as .pdf, .xls, and .doc, originating from multiple sources.

Do you want to integrate generative AI solutions into your existing systems?

Leverage our expertise in developing custom generative AI solutions and integrating them into existing operational systems for various business use cases. We are more than just generative AI consultants; we work more as a strategic partner for enterprises, helping with end-to-end support to ensure smooth generative AI implementations and higher ROI for them. 

Please feel free to talk to our generative AI consultants to discuss your project requirements and start a free-flowing navigation of the intricate world of generative AI development.