Aether Decision Intelligence

Aether is the executive decision layer for Atlas-bound technology economics. It consumes sealed evidence from the econometrics pipeline, including priors, posterior outputs, Monte Carlo results, P10/P50/P90 quantiles, scenario evidence, SHA-256 hashes, FRED and Treasury macro inputs, tenant configuration, regime classifications, and shock assumptions. Each Aether run is linked to its upstream economic run, producing a customer-controlled decision package with source provenance, degradation status, validation artifacts, and deterministic replay.

Aether supports capital decisions where internal technology economics must be evaluated against external conditions. Customers can use it for recession-risk analysis, inflation and interest-rate shocks, yield-curve stress, GDP and industrial-production sensitivity, vendor exposure, AI workflow pricing, technology portfolio stress, modernization timing, and board-level capital allocation. Macro inputs may include unemployment, CPI inflation, Fed funds, Treasury curve signals, GDP, industrial production, consumer sentiment, rate-shock assumptions, recession indicators, and customer-defined scenario variables.

Aether provides a controlled interface between deterministic economic computation and executive consumption. Outputs include CFO summaries, Operator Confidence Reports, board briefs, scenario reports, governance narratives, regulatory drafting support, safe-decline records, and structured artifacts for Claude, GPT, Bedrock, or internal LLM workflows. Numeric claims must trace to source evidence through grounding validation. Boundary validation blocks unsupported advisory language. Failed, missing, malformed, stale, degraded, or ungrounded inputs produce safe-decline records with failure codes, gaps, and required remediation.

Aether runs in the customers AWS environment. Customers retain control of deployment, data, macro assumptions, scenario inputs, evidence bundles, and decision artifacts. The product is built for finance, audit, strategy, technology, government, and sovereign AI environments that need LLM-assisted executive output without placing language models inside the calculation path: sealed evidence in, validated decision artifacts out.