Building an AI solution requires a combination of tools that support data handling, model development, deployment, and ongoing monitoring. At AEHEA, we carefully select tools based on the scale of the project, the type of model required, and the level of customization needed. Some projects can be built entirely with low-code platforms. Others may need full-stack development using cloud infrastructure and advanced machine learning frameworks. The goal is always to match the tools to the task — not the other way around.

The core foundation begins with data tools. You’ll need ways to collect, store, and clean data. Tools like Google Sheets, PostgreSQL, or cloud storage buckets often handle the raw data. For cleaning and preparation, we use Python libraries such as Pandas, or platforms like Trifacta and Dataiku that allow for visual data wrangling. These tools help transform unstructured or inconsistent data into a format ready for training. Proper preprocessing is essential before any model development begins.

For building and training the AI models themselves, the most widely used frameworks include TensorFlow, PyTorch, and scikit-learn. These provide the flexibility to create custom models for image recognition, natural language processing, forecasting, or classification. If you’re not building from scratch, platforms like OpenAI, Hugging Face, or Google Vertex AI offer pretrained models and APIs that can be integrated quickly into applications. These services are especially valuable for teams without deep machine learning expertise who still want to build powerful AI-driven features.

Once the model is ready, deployment tools take center stage. Docker is used to containerize models and ensure consistent performance across environments. Kubernetes helps scale deployments, while tools like MLflow or Weights and Biases track model versions and performance metrics. For automation and system integration, we often use n8n, which connects APIs, data sources, and AI endpoints into smooth workflows. At AEHEA, we assemble these tools to create AI systems that are not just smart, but stable, maintainable, and tightly aligned with business goals.