Pipeline Infrastructure
The threat engine runs on dedicated cloud workers behind a durable workflow engine, with a durable database for state, an in-memory cache for queues, and vector search for the Evidence Vault.
Overview
Signal verification and thesis monitoring need durable, replayable workflows — not stateless lambda calls. Every pipeline run, every drift check, and every stop-loss watch is a durable workflow with explicit state, timers, retries, and an audit trail. Workers run on dedicated cloud hardware.
Infrastructure features
Durably orchestrated workflows
Thesis Guard, Earnings Distillation, and stop-loss watchers each run as a durable workflow with deterministic replay.
Auto-scaling worker pools
Workers scale on signal volume. Earnings days and macro shocks burst capacity automatically; idle hours scale down to baseline.
Isolated tenant compute
Each user gets an isolated worker queue and isolated workflow namespace. Evidence is namespaced per user in the vector index.
Edge + cloud split
Scraping and browser automation runs on distributed edge nodes. LLM inference, embeddings, and DB writes run on dedicated cloud workers.
Stack reference
Each pipeline run uses the following components:
Operational targets
Resumable
Workflows survive crashes (durable workflows)
5 min
Scan cycle across sources
100%
Workflow audit trail