We unified our stack into AQEA Engine (CORE / COMPRESS / CRONOS / SCIENTIFIC)

We’ve been building nextX AG around one constraint: in regulated / on‑prem / air‑gapped environments, “best guess” AI is often unusable. You need outputs you can reproduce and audit.

This week we aligned our product surface into a single platform: AQEA Engine:

• CORE™ — deterministic knowledge + proof chains (auditability-first)

• COMPRESS™ — embedding compression + steerable “Lenses” (focus/shield) without retraining base models

• CRONOS™ — deterministic, zero‑shot time‑series analytics designed for edge/on‑prem constraints

• SCIENTIFIC™ — research & discovery (product-ready; website packaging update in progress)

Links:

• Engine overview: https://nextx.ch/aqea-engine

• CORE™: https://nextx.ch/core

• COMPRESS™: https://compress.aqea.ai/

• Enterprise pipeline (Prism → Compress → Lens → safety cascade): https://compress.aqea.ai/enterprise

• CRONOS™: https://nextx.ch/cronos

• AQEA Lab (public experiments + signed evidence bundles): https://engine.aqea.ai/ui

• Example experiment: https://engine.aqea.ai/ui/experiments/collider_yield

What I’d love feedback on from other builders:

1) If you’ve sold into regulated orgs, what “audit artifacts” did customers actually require (logs, replayability, provenance, proof standards)?

2) For air‑gapped/on‑prem deployments, what usually breaks adoption first: deployment friction, evaluation methodology, or ongoing maintenance?