RAG product workflows
Retrieval-augmented experiences that connect user intent, relevant source material, model output, and product feedback clearly.
AI applications / 03RAG · Generation · SaaSFull-stack delivery
AI application engineering / Models inside useful workflows
AI WITH AREAL JOB.
I build AI-enabled product features around a clear user outcome—from RAG and content generation to the interfaces, backend orchestration, persistence, and recovery states that make them usable.
Available for focused AI features and complete product workflows
AI product scope
The model call is only one part of the feature.
A dependable AI experience needs product context, controlled data flow, legible states, backend coordination, and a responsible path when output is uncertain.
Content generation systems
Generation flows with useful input design, progress, retry, persistence, history, and delivery behavior across mobile or web.
AI service integration
Backend orchestration, validation, prompt boundaries, structured responses, error handling, and data flow around model capabilities.
Human-centred product states
Interfaces that communicate what the system is doing, what users can trust, and how they can recover when output is incomplete.
Delivery / Clear ownership
A practical path from brief to production.
- 01
Define the AI job
Start with the decision or workflow being improved, the available context, and the limits users need to understand.
- 02
Design the complete lifecycle
Map input, retrieval or generation, validation, persistence, presentation, retry, and failure states before polishing the interface.
- 03
Integrate with real product data
Connect the model capability through controlled backend services and explicit data boundaries rather than direct client-side calls.
- 04
Test usefulness and recovery
Review representative outputs, edge cases, latency, user feedback, and operational behavior before treating the feature as complete.
Relevant evidence
Proof through shipped systems.
Project questions
Before we start.
Can you add AI to an existing SaaS product?
Yes. The first step is identifying where AI improves a real workflow, then integrating it through the existing product architecture with clear states and boundaries.
Do you only build chat interfaces?
No. Useful AI can appear inside search, content generation, document workflows, recommendations, admin operations, or a structured decision flow.
How do you handle unreliable output?
The product needs validation, constrained inputs and outputs where practical, recoverable error states, useful user controls, and clear communication about what the system produced.
Available for select remote work
Have a product to build?
Share the problem, current stage, and desired outcome. I will respond with the clearest next step.