Pragmatic write-ups on AI architecture, production patterns, and the trade-offs we hit on real client work.
Most AI systems we audit were shipped on vibes: someone tried a few prompts, it looked fine, it went live. Here is how we retrofit real evaluation onto a running system without pausing the roadmap.
A working comparison of three of the most common LLM platforms — latency, throughput, compliance posture, and what actually breaks when you push them to production.
A frank look at what it actually takes to keep an agent alive in production — circuit breakers, retries, state, and the failure modes nobody warns you about.
A pragmatic decision guide for choosing between prompting, RAG, and fine-tuning — based on the trade-offs we hit on real client engagements.
What guardrails actually do (and do not do) on Bedrock, Azure OpenAI, and OpenAI's hosted API — and where you still need to build your own.
How to decompose a real workflow into agents, route between them, and keep the whole system observable — without building a science project.
A pragmatic comparison of pgvector, Pinecone, Qdrant, Weaviate, and OpenSearch — with the questions we ask before recommending any of them.
Concrete techniques we use to keep Bedrock spend predictable on production workloads — model selection, caching, batching, and the cost surprises to watch for.
