Generative AI Development Services

AI model fine-tuning adapts pre-trained models to new tasks, datasets, or domains, enhancing performance, speeding up convergence, and optimizing computational resources. We employ the following techniques:

• Architecture Modification
• Hyperparameter Tuning
• Data Augmentation
• Domain Adaptation
• Transfer Learning

Leverage the power of transfer learning in generative AI to develop efficient and effective models across diverse applications and domains. Optimize model development with reusable knowledge and seamlessly enhance performance. Our expertise includes:

• Pre-trained Generative Models
• Feature Extraction

Data collection and annotation are crucial for training high-performance generative AI models. The quality and diversity of your data directly impacts model performance. Our services include:

• Data Collection
• Data Preprocessing
• Image, Text, Audio, and Video Annotation
• Data Augmentation
• Quality Control

Our expertise in Natural Language Processing (NLP) and Text Processing helps businesses enhance efficient communication, sentiment analysis, and content understanding. In the realm of NLP and Text Processing, our proficiency is reflected through: 

• Sentiment analysis: LSTMs, Neural Network Transformer
• Machine translation: Rule-Based, Statistical, Neural, or Hybrid Machine Translation
• Speech recognition: HMMs, Deep Neural Networks, N-gram Models, MFCCs, Spectrogram, CTC, Attention Models and Transformers, Seq2Seq
• Named entity recognition: Rule-Based Techniques, HMMs, CRF, SVMs, RNNs, Bidirectional LSTMs, BERT
• Scoring search results: TF-IDF, LSTMs, VSM, ML Models, LTR, BERT
• Recommendations: word2vec, doc2vec, RNN Attention
• Finding vulnerabilities in source code: Encoder-Decoder Seq2Seq, LSTM
• Topic modeling: LDA, Non-negative Matrix Factorization, LSA, HDP, Correlated/Dynamic/Supervised Topic Models

Address biases, cybersecurity threats, and overfitting models with our effective ML solutions. As an established AI/ML development services company, we help businesses choose the right ML models for their specific problems, considering their infrastructure requirements: 

• Supervised learning: Linear/Logistic Regression, SVMs, Decision Trees, Random Forests, Neural Networks, GBMs, KNNs, F1 Score/ROC-AUC, MSE/RMSE/MAE
• Unsupervised learning: DBSCAN/Hierarchical/K-Means Clustering, PCA, t-SNE, Autoencoders, Apriori/Eclat, Isolation Forest, One-Class SVM, GANs, SOMs
• Semi-supervised learning: Self/Co-training, Transductive Learning, Label Propagation/Spreading, Graph-based Semi-supervised Learning, Deep Learning
• Reinforcement learning: Q-learning, DQN, REINFORCE, PPO, A2C, SAC, model-based RL, MCTS, multi-agent RL

As leaders in deep learning engineering, our artificial intelligence experts transform your data into actionable insights, driving change and enhancing performance. We support you at every stage, from assessing your data’s suitability for training algorithms to deploying customized models and refining accuracy. Our team specializes in: 

• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs): LSTMs, GRUs
• Autoencoders
• Generative adversarial networks (GANs)
• Transformer models
• Deep reinforcement learning