The architecture, decision frameworks, and standards behind Orion's platform and AI work. Organized by the same nine layers as the Quantum Leap initiative — every engagement runs against this playbook.
Click any layer for the deep page — reference architecture, tools, build-vs-buy, and the five mistakes we keep seeing.
Refusal patterns, audit trails, RBAC, model registry, change control. The layer that lets a regulated business actually ship.
Tracing across LLM calls, cost-per-request, latency budgets, drift detection. What runs the system in production after the demo is over.
Eval rubrics, regression suites, LLM-as-judge calibration. The discipline that turns "demo worked once" into "ships every Friday".
Tool-use boundaries, agent loops, function calling, sandboxing. Where models stop describing the world and start acting on it.
Workflow engines, agent planners, multi-step composition. Coordinating models, retrieval, and tools into a system.
Selection, prompting, fine-tuning, distillation. The choices upstream of every decision the system makes.
Hybrid search, freshness windows, reranking, chunking. The boring stack that consistently beats vector-only RAG demos on real corpora.
Ingest, schema, lineage, PII handling. The layer most teams underestimate until the second time it breaks.
Compute, network, serving, memory. The substrate that decides what the rest of the stack can even attempt.
What goes into a document-intelligence system that actually ships — layer by layer, with the choices that turn a demo into a production pipeline.
Read →A nine-layer decision framework for what to build, what to buy, and what to defer when standing up a production AI system.
Read →We write the answer once and put it here. Send the question and a paragraph of context — happy to share early drafts on layers still in progress.