Service · 01
AI Integration & Infrastructure
LLMs, vector stores and pipelines wired into your product — infrastructure that holds up in production.
How it's built
(1) Audit — map use cases, data and a cost ceiling per request. (2) Architecture — RAG/cache/queue design with latency and failover plans. (3) Integration build — API and pipeline code plus the prompt/context layer. (4) Validation — eval suite, load tests, cost-per-request checks. (5) Ship & monitor — tracing, alerts, iteration on real traffic.
Core fundamentals
- model-agnostic design (swap providers without rewrites)
- cost ceilings enforced in code
- graceful degradation when models fail
- every prompt versioned and evaluated
Build blueprint
Deliverables
- architecture doc
- integration code
- eval suite
- monitoring dashboard
- runbook
Stack
LLM APIsVector DBRAGRedisCloud
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