Case study

AI Workflow Orchestrator

A guarded orchestration layer for research, retrieval, approvals, and content workflows that runs with auditability and clear operator control.

AI-enabled Systems

AI as a capability inside a larger product system.

Launch profile34-day production launch

42% faster turnaround

<200ms internal API p95

6 core automations

Highlights

LLM routing

approval gates

observable workflow history

Project overview

A guarded orchestration layer for research, retrieval, approvals, and content workflows that runs with auditability and clear operator control.

Problem

The client needed a way to coordinate multiple AI tasks without brittle one-off scripts and manual follow-up.

Solution

Designed an event-driven workflow engine, added queue-backed execution, and surfaced a clean operator UI for monitoring every job.

System flow

Architecture diagram

API gateway

Queue workers

Prompt routing

Realtime status stream

Audit log

Tech stack

Next.js • NestJS • OpenAI • PostgreSQL • Prisma • Redis

Deployment pipeline

01Audit trail for every AI action

02Retry-safe queue processing

03Guardrails for each workflow step

Challenges

Scoped work into a sequence of solvable releases so product quality stayed high while the system evolved.

Result

Teams launch and observe AI work from one place with clearer ownership and predictable delivery.