Custom Artificial Intelligence Development Service

Data Infrastructure

We use various technologies for data infrastructure:

• MongoDB – For scalable, flexible document data storage.
• Kafka – For building real-time data pipelines.
• Spark – For large-scale batch data processing.
• AWS S3 – For cloud object storage.
• Redis – For caching and real-time analytics.
• MySQL – For relational database.
• PostgreSQL – For open-source RDBMS.
• Hadoop – For distributed storage and processing of large data sets.
• Spark – For large-scale batch data processing.

Data Analysis & Engineering

We use various technologies for data analysis and engineering:

• Python – For data preparation, cleaning, feature engineering.
• SQL – For data analysis and querying structured databases.
• Pandas, NumPy – For data manipulation and analysis.
• Jupyter Notebooks – Development environment.
• Apache Flink – Stream processing for real-time analytics.
• R – For statistical computing and graphics.
• Java – For versatile and platform-independent development.

Model Building & Training

We use various technologies for model building and training:

• PyTorch, TensorFlow – Leading deep learning & ML frameworks.
• Keras – High-level neural networks API.
• CUDA – To accelerate model training with GPUs.
• MLflow – For model tracking and model registry.
• XGBoost – Gradient boosting library for efficient model training.

Model Deployment

We use various technologies for model deployment:

• Docker – For containerization of models and dependencies.
• Kubernetes – For container orchestration and autoscaling.
• AWS SageMaker – Managed service for deployment.
• REST APIs – Exposing models via web services.
• TensorRT – For high-performance deep learning inference.

Application Development

We use various technologies for application development:

• React – For building interactive user interfaces.
• Node.js – Scalable server-side JavaScript.
• GraphQL – Flexible API layer over models.
• AWS Amplify – Rapid fullstack cloud app development.
• Flutter – For building cross-platform mobile applications.

DevOps & Monitoring

We use various technologies for DevOps and monitoring:

• Jenkins – For Continuous integration and delivery.
• Prometheus – For Time-series monitoring.
• Grafana – Visualization and analytics.
• ELK Stack – Centralized logging.
• New Relic – Application performance monitoring and management.

01

Problem Identification

Our AI consultant will call you to understand the requirements of your AI-based project. On the call, we will identify your goals or what you want to achieve like cost reduction, improving customer satisfaction, or process automation.

02

Data Collection and Preparation

The next step is to collect and prepare the data that will be used to train the AI model. We understand that the data quality impacts the accuracy of insights; so we ensure to prepare the dataset by cleaning and removing errors or missing values.

03

AI Model Development

Once the data is prepared, our AI app developers select the appropriate algorithm and fine-tune it to ensure that it provides accurate predictions. AI model development is an iterative process; it needs to be retrained with new data

04

Model Evaluation

After the AI model is developed, our AI developers ensure that the model performs well on new, data. If the model doesn’t perform well, then it is again fine-tuned by modifying data, and changing the architecture until the result is achieved.

05

Integration With Existing System

Once the model is developed, it is integrated with your existing system through API or a web service. We ensure to deploy your AI model in a way it is secured and scalable so that, the model can be updated, and retrained as required.

06

Monitoring and Maintenance

Once your model is deployed, it is monitored and maintained to ensure that it continues to provide accurate predictions. Over a period of time, we keep updating your model with new data and ensure that you can resolve the problems over time.