A clear map of machine learning, deep learning, generative AI, RAG, agents, and computer vision, and how Radiantze combines them in production workflows.
AI is a family of related approaches, not a single product. Machine learning scores and forecasts from structured history. Deep learning reads images, audio, and free-form text. Generative models draft content. Retrieval-augmented generation grounds answers in approved documents. Agents plan multi-step work with tool access. Computer vision turns pixels into operational decisions. Radiantze helps teams match each approach to the problem at hand.
Most enterprise products combine several of these in one workflow. A single journey might use machine learning to score risk, deep learning to read a scanned document, and a generative model wrapped in RAG to answer a customer question, with an agent coordinating the hand-off. Knowing what each technology does well keeps teams from applying a generative model to a forecasting problem or a classic ML model to open-ended reasoning.
Machine learning remains a strong first production path for tabular decisions such as churn, fraud, demand, and credit. It typically needs less data and compute than deep learning, with explainability regulated teams can defend. Deep learning fits unstructured perception such as defects on a line, speech transcription, and clinical imaging assist, usually by fine-tuning a pre-trained model rather than training from scratch.
Radiantze Engineering Rule
Technology architecture only succeeds when governance, cost discipline, and operational telemetry are treated as day-one delivery requirements.
Generative AI accelerates drafting, summarization, and ideation when review gates and acceptable-use policy are part of the design. RAG adds citations and source control so answers stay tied to current policy and product knowledge. Agentic systems extend that pattern into tool calls and multi-step actions, with narrow permissions, full audit trails, and human approval on consequential writes.
Computer vision closes the loop for visual decisions such as quality inspection, field photos, and document extraction, with confidence thresholds that route ambiguous cases to experts. Across the stack, Radiantze keeps the same operating habits with clear success metrics, evaluation on production-representative data, and ownership for monitoring after launch.
A chatbot answers from a knowledge base when prompted. An agent plans steps, calls tools, and completes work across systems, with approval gates on consequential writes. Radiantze uses chatbots for policy Q&A and support deflection, and agents for case processing, research synthesis, and workflow automation. Choose the pattern from the task, not from a label.
Use the Radiantze Technologies AI map for deep dives and use-case walkthroughs. Use AI/ML integration and development when you are ready to sequence delivery, governance, and cost discipline into a production program.