Panorama Engine is an experiment around deterministic analytical execution.
The goal is to make analytical decisions reconstructible after execution by treating every run as a sealed deterministic cycle producing a verifiable snapshot.
Originally developed for financial research workflows but the architecture is domain-agnostic.
Interested in feedback from people working on analytical pipelines or reproducible ML workflows.
Thanks for your reply.
Yes, bitemporal modeling helps with data lineage.
What I’m struggling with is reconstructing the process that produced an observation like ordering, constraints, fallbacks.. not just the data state.
Curious if you’ve seen that handled anywhere.
Panorama Engine is an experiment around deterministic analytical execution.
The goal is to make analytical decisions reconstructible after execution by treating every run as a sealed deterministic cycle producing a verifiable snapshot.
Originally developed for financial research workflows but the architecture is domain-agnostic.
Interested in feedback from people working on analytical pipelines or reproducible ML workflows.