Start with a discovery session to define the user problem, data vailability, and success metrics
Select the best-fit AI approach, whether that’s leveraging foundation models, supervised learning, or custom training
Build a proof of concept using tools like LangChain, scikit-learn, or OpenAI APIs to validate core functionality
Iterate on the model and UX in parallel, using real data and user feedback
Deploy with a production-grade pipeline, including model versioning, monitoring, and retraining hooks
Ensure your infrastructure is cloud-optimized, secure, and cost-efficient from day one