This architecture contains several components that allow you to split the responsibility of the different parts of the future solution like Data Integrations and Model Serving and make the development process more structured and smooth.

Working with our clients, Godel uses this architecture as a starting point of customisation (adaptation/ aligning), with respect to the customer needs, because we understand that architecture first of all should fit the business purposes.

I could describe architecture’s components as:

Data Sources: The foundation of AI Architecture can be external or internal, and can include structured data from relational databases, semi-structured data like JSON files or APIs, and unstructured data such as text, images, and videos. For example, Databases, APIs, IoT devices, CRM systems, social media streams, and logs.

Data Integration: The data integration component focuses on transforming raw data into formats suitable for analysis and modelling. This involves aggregating data from multiple sources, cleaning it to remove inconsistencies, and enriching it to fill gaps. This layer also implements governance policies to ensure compliance with privacy regulations like GDPR and maintain data lineage and metadata.

Model Development: Model development is the heart of AI Architecture, where algorithms are trained and optimised on datasets. This component includes data preprocessing, feature engineering, and experimenting with various machine learning or deep learning models. AI/ML Sandbox environments should support collaboration among data scientists and engineers, and version control of datasets, code, and models.

Model Re-Training: AI/ML models need regular retraining to remain effective. The model re-training component automates the process of updating models using fresh data to address challenges like data drift or changing user behaviour. Retraining pipelines typically involves data processing, re-evaluating hyperparameters, and ensuring new models are better than the older ones before deployment. This component is closely integrated with Model Monitoring to trigger retraining events based on performance metrics or thresholds.

Model Serving: The model-serving component operationalises AI by making trained models accessible for inference. Models are deployed on scalable infrastructure on a special AI/ML such as Kubernetes or serverless platforms, ensuring they can handle varying workloads. Endpoints are typically exposed through RESTful or gRPC APIs, allowing seamless integration with applications or systems. Advanced serving platforms enable features like A/B testing, multi-model inference, and version management to optimise deployment strategies.

Model Monitoring: Once in production, AI models require continuous monitoring to maintain their reliability and accuracy. The model monitoring layer tracks metrics such as prediction accuracy, latency, resource utilisation, and drift detection. Specialised tools can identify performance degradation due to changes in data distribution or concept drift, triggering alerts or automated retraining workflows. This component also ensures compliance by auditing decisions for transparency and accountability.

Insights: The insights components transform raw model predictions into actionable business intelligence. This is achieved through analytics dashboards, real-time alerts, or APIs integrated into decision-making systems. Effective insights delivery helps organizations close the loop between AI outputs and strategic actions, ensuring maximum business impact.

Usually, the responsibilities of each component are discussed with stakeholders at the beginning of the development process. If the case where a customer does not have a clear vision of the future solution, Godel could play a consulting role. We deliver tailored end-end solutions to achieve business-critical missions through technology, solving problems for our partners to achieve their goals: from technology transformation, complex consolidations, escalating infrastructure costs and more commonly over the past year, harnessing AI. Only after the business requirements were discussed and the main responsibilities of the architecture’s components were defined could we speak about the technical stack.