Case Study
AI Product Backend
Backend development for a product using AI chat features, service orchestration, and payment flows.
- Role
- Full-stack backend development
- Stack
- Laravel, AI APIs, Stripe
- Status
- Production work
Project Summary
What the product needed
The product needed AI-assisted service flows that behaved like part of the product, not a disconnected demo.
I helped structure the backend around sessions, service boundaries, payment behavior, and user-facing flows.
Problem
Integrating AI-powered experiences with structured product logic.
My Role
Full-stack backend development
Results / Impact
Improved AI workflow reliability by separating orchestration, payment eligibility, and user-facing state into clearer backend boundaries.
Approach
Decisions in plain English
Project case study. The technical choices are written for product context first, not as backend documentation.
Private client project; public demo unavailable.
Evidence
Constraints, architecture, and proof points
Constraint
Private client work with AI behavior that needed production-grade failure handling.
Constraint
Payment eligibility and service access had to stay explicit.
Constraint
LLM responses could vary, fail, or arrive slower than normal product requests.
Architecture
REST API layer for product sessions and user-facing state.
Architecture
Service classes for AI orchestration and payment-aware workflows.
Architecture
Queue-backed processing for retryable AI tasks.
Architecture
Explicit records for billing, eligibility, and generated results.
Proof
Designed queue-backed AI workflows for safer retries.
Proof
Modeled subscription and eligibility states explicitly.
Proof
Kept AI provider logic separate from core product state.
Retrospective
System trade-offs & lessons
Real-world reflections on what failed initially, and how we refactored the architecture to achieve production stability.
We tried streaming LLM responses directly via server-sent events, but user-side network drops made connections fragile. Moving to a stateful polling system backed by database queues resolved it.
Underestimating LLM rate limits on launch day caused initial failures. We added a token-bucket limiter and auto-retry backoff queues to survive spike traffic.
Technologies