Production AI Applications
An AI demo is a notebook; a production AI application is a distributed system. I build the orchestration layer: queue-based job scheduling for generative workloads, provider fallbacks that survive mid-flight failures, cost controls, and real-time status streamed to users — as in RealStream's thousands of concurrent jobs.
What you get
- Architecture for LLM/generative workloads: what runs synchronously, what becomes a job, where state lives
- Provider integration layer with fallbacks, rate-limit backoff, and per-request cost accounting
- Queue-based orchestration (NestJS + BullMQ) for jobs that run seconds to tens of minutes
- Real-time job status over WebSockets so users see truth, not spinners
- Failure drills: cut a provider mid-job and watch the pipeline converge instead of leak
How it runs
Step 1 — design for the failure modes first
Model providers fail mid-flight and rate-limit at the worst moment. The architecture assumes it from day one: timeouts, capped backoff, artifact cleanup, and a provider-agnostic integration seam.
Step 2 — every AI call is a tracked job
Prompt, model, version, cost, and artifacts are recorded per job. This is what makes the system debuggable and lets you see unit economics before they surprise you at scale.
Step 3 — status and evaluation loops
Users watch stages in real time (Redis pub/sub → WebSocket). For quality drift, sampled outputs go through an evaluation harness so model or prompt changes are measured, not vibes.
Case studies behind this service
FAQ
What does it take to move an AI prototype to production?
The same engineering every distributed system needs, plus AI-specific failure modes: jobs that run minutes, providers that fail mid-flight, and costs that scale with usage. Concretely: queue-based orchestration with retry policy, provider fallbacks, per-job cost tracking, and real-time status. A demo with a nicer UI is not a production system.
Which tech stack do you use for production AI systems?
Node.js with NestJS orchestrating BullMQ queues on Redis, artifacts in S3, status over WebSockets — the RealStream stack, which schedules thousands of concurrent generative-video jobs. Model access is provider-agnostic by design, so swapping or adding providers does not touch the pipeline.
Can AI agents and LLM calls be part of my existing backend?
Yes — that is usually the cheaper path. The integration seam (provider layer, queue lanes, cost accounting) is designed to attach to your existing NestJS/Node.js services without a rewrite. What must change is the assumption that every request finishes in one HTTP roundtrip.
Further reading
- Production AI Agents in Node.jsAn AI demo is a notebook; a production agent is a distributed system. The architecture, cost controls, and failure modes I learned shipping an AI orchestration platform that runs thousands of concurrent jobs.
- NestJS + BullMQ at ScaleI shipped an event-driven NestJS + BullMQ pipeline to production in 60 days. What actually breaks first at scale: retry storms, lost jobs, queue starvation, and the fixes that hold.
Who you work with
Eugene Zaiko
Eugene Zaiko is a senior full-stack engineer (5+ years, ex-PixelPlex, Syberry, Linnovate) specializing in event-driven backends, PostgreSQL at scale, and production AI systems. No account managers: you talk directly to the engineer whose fingerprints are on the code.