Platform Differentiator — Replay Audit

See it exactly as it was

All data is held in a time-sliced store — view any entity or decision exactly as-at any date and time, across the whole platform.

When a regulator, a board, or a customer asks what the system knew and when, "something changed" isn't an answer. You need to stand inside the exact moment — and show the precise state the platform and its agents were acting on.

See It Live Talk Auditability
Every entity time-sliced by design
As-at replay, not just change logs
Whole platform, not one dataset
The Problem

Knowing something changed isn't the same as knowing what it saw

Audit logs tell you something changed, but not the full state the system — or the model — actually saw at that moment. Reconstructing "what did it know, and when" becomes guesswork: stitching together per-table change history and hoping the picture holds up. When defensibility matters, hope is not a control.

What buyers ask
What state did the AI actually see?
Can we reconstruct any entity as-at a past date?
Is this defensible to a regulator?
Or do we only have change logs?
The Shift

From a change log to a point-in-time replay

The old way
  • Audit logs + per-table change history
  • Cannot reconstruct full as-at state
  • No view of what the model saw
  • Defensibility by reconstruction and hope
With Brain-Stem
  • All data time-sliced by design
  • View any entity or decision as-at any timestamp
  • Whole-platform replay, not just datasets
  • A defensible, point-in-time audit trail
a change log → replay any moment, platform-wide
How It Works

Versioned as it happens, replayable on demand

1
Everything is versioned as it happens
Each entity carries its history — no separate audit pipeline to wire up. The record of how state evolved is built in, not bolted on.
2
Pick any date and time
Query any entity or decision exactly as-at that moment — not a summary of what changed, but the actual state, restored.
✓
Replay the whole platform
Not just one dataset — reconstruct the state the system and its agents actually operated on. Stand inside the exact moment a decision was made and show, precisely, what the platform saw.

Built on the platform's own time-sliced foundation — platform_history, get_entity_history and std_v1 versioning — so as-at replay is shipped and proven, not a roadmap promise.

Verified against 12 leading AI platforms

Others time-travel datasets — we replay the whole platform

Data-lakehouse incumbents like Databricks Delta Time Travel and Palantir do genuine as-at reconstruction — for datasets and tables.

The Brain-Stem wedge is whole-platform: every entity and every decision is replayable, not just the data tables underneath them.

Get Started

Stand inside any moment

See whole-platform replay audit running live — and how it turns "what did it know, and when" from guesswork into a defensible answer.

See It Live Talk Auditability

Any entity. Any decision. Exactly as it was, at any moment.