You will get AI System Audit - LLM Agents, RAG Pipelines, Production Infrastructure
Project details
Your AI system works in development. But what happens when OpenAI returns 429? When Claude times out? When an agent hallucinates and feeds bad data to the next agent?
Most AI systems fail silently in production. I find these failure points before your users do.
What I check:
• LLM API failure handling (429, 503, timeouts)
• Missing fallback chains and circuit breakers
• Agent pipeline reliability - output validation between handoffs
• RAG pipeline resilience (timeouts, empty results)
• Token budget management and cost control
• Graceful degradation paths
What you get:
• Starter ($100): Written audit report with severity ratings (Critical/High/Medium/Low) + prioritized fix list + small critical fixes within existing architecture
• Standard ($500): Starter + implementation of circuit breakers, retry logic, LLM fallback chains, degradation modes
• Advanced ($1,500): Standard + Prometheus monitoring, alerting setup, regression test suite
Proof, not theory: Production multi-agent platform with all these patterns running - github.com/MooCot/ai-content-platform
Who this is for: teams with AI systems live in production who want to know what breaks before customers do.
Most AI systems fail silently in production. I find these failure points before your users do.
What I check:
• LLM API failure handling (429, 503, timeouts)
• Missing fallback chains and circuit breakers
• Agent pipeline reliability - output validation between handoffs
• RAG pipeline resilience (timeouts, empty results)
• Token budget management and cost control
• Graceful degradation paths
What you get:
• Starter ($100): Written audit report with severity ratings (Critical/High/Medium/Low) + prioritized fix list + small critical fixes within existing architecture
• Standard ($500): Starter + implementation of circuit breakers, retry logic, LLM fallback chains, degradation modes
• Advanced ($1,500): Standard + Prometheus monitoring, alerting setup, regression test suite
Proof, not theory: Production multi-agent platform with all these patterns running - github.com/MooCot/ai-content-platform
Who this is for: teams with AI systems live in production who want to know what breaks before customers do.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI-Generated Code, AIOps, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$100
|
Standard
$500
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 15 days |
Number of Revisions | 2 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | |
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | - |
Model Documentation | - | - | |
Model Monitoring | - | ||
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$200 - $2,000
Additional Revision
+$50Frequently asked questions
About Slava
AI Systems Engineer | LLM Agents - RAG - NestJS - LangGraph
Kyiv, Ukraine - 10:56 am local time
Most AI developers will get it working. I focus on keeping it working.
What that looks like in practice:
Multi-agent pipelines, RAG over 50k+ documents, NestJS backends
with circuit breakers and proper fallback chains. At EU DisinfoLab
I built all of this from scratch — not as a side project, as the
actual production system.
LangGraph pipelines where agents actually know when to stop and why.
RAG that returns the right documents, not just the closest ones —
brand-isolated Qdrant collections, score gating, episodic memory.
NestJS backends with per-provider rate limiting, 429-aware retry,
circuit breakers, SSE streaming under 200ms first token.
AI eval tests that catch prompt degradation before it hits production.
---
Current work: EU DisinfoLab (since Oct 2024) — built the full AI layer,
LangGraph pipeline processing 10k+ items, RAG over 50k docs,
Kubernetes from scratch with GitOps and zero-downtime deploys.
Stack: TypeScript · NestJS · LangGraph · Zod · Qdrant · Redis · BullMQ
Kubernetes · OpenTelemetry · OpenAI · Claude · Gemini · Vertex AI
English: strong written, async-first communication.
Steps for completing your project
After purchasing the project, send requirements so Slava can start the project.
Delivery time starts when Slava receives requirements from you.
Slava works on your project following the steps below.
Revisions may occur after the delivery date.
Codebase access & initial review
Map architecture, identify AI components and integration points
Failure mode analysis
Test LLM API failures, timeouts, rate limits, malformed outputs