LLMOPS / AI MONITORING
Monitoring, costs, quality — monthly retainer
Monthly retainer keeping your AI systems in top shape: API cost monitoring, hallucination detection, RAG quality evaluation, alerts and board reports. Natural upsell after Rapid RAG or AI Starter deployment.
Key Features
Metrics dashboard: costs, latency, answer quality
Alerts on quality degradation or cost spikes
RAG evaluation (RAGAS / LangSmith)
Monthly report with optimization recommendations
How We Work Together
A proven methodology that delivers results
Discovery
We start with understanding your business, challenges, and goals through workshops and interviews.
Design
Together we design the solution architecture and create a detailed implementation plan.
Deliver
Iterative implementation with regular demos and feedback loops to ensure alignment.
Support
Post-launch support, knowledge transfer, and ongoing optimization recommendations.
Use Cases
- API cost monitoring and optimization
- Hallucination detection and reduction
- RAG system quality evaluation
- LLM call optimization and caching
Ideal For
- Companies with live AI systems
- After Rapid RAG or AI Starter deployment
- Teams monitoring API budgets
Not Ideal For
- Companies without deployed AI yet
- Fully manual processes
- One-off projects without monitoring needs
Deliverables
Deliverables
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01Metrics dashboard (LangSmith / Grafana)
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02Alert and escalation system
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03Monthly board report
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04Cost and quality optimization recommendations
Technology Stack
Timeline
monthly retainer
Estimated project duration
Related Case Studies
RAG Document Processing System
At Insly, I led development of a RAG (Retrieval-Augmented Generation) system that gives insurance brokers fast, context-aware answers about policy details. The system combines traditional search with vector embeddings to handle complex queries across 23 different insurance providers.
Challenge
Insurance brokers needed to quickly find relevant information across thousands of policy documents from 23 different insurers, each with unique formats and terminology.
Microservices Migration
CloudAcademy needed to migrate their content authorization service from Kotlin to Go as part of a broader standardization effort. I led this migration while ensuring zero downtime and creating new microservices following DDD patterns.
Challenge
Legacy Kotlin service had performance bottlenecks and was difficult to maintain. Team needed to standardize on Go for better consistency across microservices.
Ready to Transform Your Business?
Let's discuss how I can help you achieve your goals. The first consultation is free.