Raw output, uncertain scope, inconsistent authority, duplicated controls, hidden execution.
Human load ≈ all actions × review cost
EROC Replay · Enterprise Reality Observation & Capture
Your enterprise is already executing through AI, automation and hidden workflows.
The problem is not whether AI exists in the organisation. The problem is knowing which choices can create consequences, under whose authority, and how much human attention is actually required.
EROC observes the enterprise, ACEI simulates the consequence boundary, and Replay measures what would change — before anything is allowed to execute.
No backend required
No write access
No production execution
Historical files stay in your browser
The adoption problem
Enterprises are trying to scale AI by asking humans to watch more AI.
That does not scale. Review-all turns human attention into the safety mechanism. Shadow AI, shadow IT, stale authority and hidden execution paths make the review queue noisier while the real consequence boundary remains unclear.
Pre-screened, consequential residuals with state, authority, evidence and policy visible.
Human load ≈ exceptions × resolution cost
Live zero-consequence replay
See the enterprise. Simulate before commit.
The file is parsed locally. This page has no upload endpoint.
Live Shadow requires a server-side read-only connector.Credentials will never be placed in this browser demo. Railway or Cloudflare Worker is the intended runtime boundary.
01ObserveEROC
→
02Candidate statenot authority
→
03Evaluatestate · authority · policy
→
04SimulateACEI shadow
→
05Measureattention · exposure
Shadow decision distribution
SIMULATEDWhat ACEI would have done
Review-all baseline—
→
Human residual—
Reality surfaces
What EROC found
Shadow AI—
Shadow IT—
Shadow Execution—
Shadow Authority—
Shadow Workflow—
Sample event tape
No dataset loaded
Decision → authorization → consequence
| Event | Surface | Action | Authority | Policy | Consequence | Shadow outcome |
|---|---|---|---|---|---|---|
| Run the replay to inspect the governed residual. | ||||||
What the buyer gets
A measurable adoption path instead of a leap of faith.
The first engagement can start with synthetic data or a stale export. No production write access is required to establish where the real risks, review burden and safe delegation opportunities are.
Map shadow AI, IT, execution, authority and workflow against how the enterprise actually operates.
Run the same activity through ACEI without changing the live outcome.
Quantify review reduction, authority gaps, consequence exposure and candidate action classes.
Move only evidence-backed, bounded and reversible action classes toward governed execution.
Controlled transition
Try before you buy becomes simulate before commit.
01Synthetic Livezero access
02Historical Replaystale / anonymised export
03Live Shadowread-only connector
04Governed recommendationhuman decision
05Bounded executionREHT at consequence
Lowest-friction starting point
Start with what already happened.
Take a stale or anonymised activity export. Replay it. See what would have required human judgment, what could have been screened automatically, and where authority or policy was actually missing.
EROC Replay is a demonstration and evaluation surface. Observations are candidate evidence, not operative authority. Simulation results do not authorize execution. No browser score, promotion candidate or replay outcome can grant execution rights. Production execution remains subject to the normal ACEI / REHT authorization boundary and external enforcement.