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Cloud Infrastructure, MLOps & AI Cost Control
AWS architecture, CI/CD, observability, MLOps, token budgets, fallback paths, and cost controls for AI systems that need to survive production.
MLOpsAWS architectureAI cost control
What this includes
Production work with measurable outcomes.
The practical parts of the engagement: what we design, build, integrate, test, and hand over before it reaches real users.
AWS and cloud architecture
CI/CD and environment strategy
Monitoring, alerts, and tracing
Model fallback and budget controls
Security and reliability reviews
Outcome proof
Cost dashboards, eval harnesses, and rollback paths that keep AI systems predictable under load.
See the Phobia case study →Silo links
Related field notes and proof.
Guides, case studies, and market pages connected to this service so buyers can evaluate the work from several angles.
MLOps
Putting a cost ceiling on your AI before the bill puts one on you.
Per-tenant cost dashboards, token budgets, model fallbacks, and the small infra tweaks that knocked our average client's inference bill down 47%.
Architecture
RAG without regret: a production checklist.
Twelve concrete checks before you ship retrieval-augmented generation to real users. Chunking, reranking, citation enforcement, and the eval harness that keeps it honest.
Industry
AI Lead-Gen & Automation for FinTech
Lead-generation bots, qualification flows, CRM automation, and compliance-aware AI systems for FinTech teams selling into complex markets.
Service hub