Custom AI Model Development

The end goal is an optimized, deployment-ready AI model tailored to the client's specific needs. We handle the full model development lifecycle, bringing both strategic and hands-on capabilities.

Business Needs Analysis

Mapping out the exact problem statement, success metrics, and desired model outcomes based on the client's use case

Modeling Approach Recommendation

Determining the most suitable modeling techniques based on the problem, data, and performance objectives - such as regression, classification, clustering, and deep learning.

Model Prototyping

Leveraging the client's data to rapidly build model prototypes, validating different modeling approaches.

Model Architecture Design

Designing optimal model architectures, including neural network layers and parameters, feature engineering steps, and algorithm selection.

Model Training

Using the client's computing resources and data, training customized models using techniques like supervised, unsupervised, or reinforcement learning.

Model Evaluation

Rigorously evaluating model performance using test data sets and metrics like accuracy, precision, recall, F1 score, and confusion matrices.

Model Optimization

Improving model performance through techniques like hyperparameter tuning, loss function changes, and additional data samples.

Model Deployment

Package and deploy the model within the client's production IT infrastructure and workflows, with proper versioning and monitoring.

Model Monitoring

Setting up ongoing model performance tracking to detect data drift, new training needs etc. to facilitate continuous model improvement.