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Audit ledger and replay
AI workflows need receipts, replay, and history before they become trusted operations.
When AI participates in real work, teams need to know what happened, who acted, which source was used, what changed, what failed, and how the state can be replayed. Folium designs audit and event-replay layers that make AI work inspectable.
Buyer search intent
What this page is built to answer.
A buyer wants AI audit trails, event replay, state history, decision ledgers, action receipts, workflow replay, or AI evidence ledgers.
Question
Can we replay what AI did?
Question
How do action receipts work?
Question
Can we see state history and decision changes?
Question
How do we preserve audit records without leaking secrets?
Folium answer
The answer is a controlled operating path.
Folium turns the search problem into a decision-ready workflow: what to inspect, what to build, what to govern, what to measure, and what the business should own after launch.
01
Define event types, state transitions, action receipts, decision records, source references, and redaction rules.
02
Create replayable timelines for review, incident response, support, training, and evidence packets.
03
Separate private logs from public-safe proof and customer-facing status.
04
Use ledgers to support accountability, correction, rollback, and improvement.
Delivery workflow
How Folium moves from search intent to working capability.
The work is deliberately sequenced so the buyer can see the pressure, approve the boundary, inspect the build, and decide the next stage.
01
Event map
Name state changes, actions, approvals, provider events, notifications, failures, and human decisions.
02
Receipt schema
Define source, scope, actor, time, permission, outcome, evidence, and boundary fields.
03
Replay design
Create state history, filtered timelines, incident views, and support replay paths.
04
Boundary review
Redact secrets, private records, and unsupported public claims while preserving accountability.
Useful outputs
What a serious buyer should expect to receive.
These are the artifacts that turn AI interest into something a business can inspect, challenge, fund, support, and improve.
audit ledger schema
event replay map
action receipt format
decision ledger
state-history review surface
Related Folium paths
Go deeper from this buyer need.
FAQ
Questions this search usually hides.
These answers keep the page useful for humans while giving search engines and AI answer systems a clear view of the service boundary.
Does event replay mean public logs?
No. Folium separates private operational logs, customer-facing status, and public-safe proof records.
Why do AI workflows need action receipts?
Receipts make state-changing work accountable by recording actor, source, permission, action, result, boundary, and next owner.
Start here
Turn the search into the first reviewable workflow.
Folium can help translate this need into scope, architecture, data boundaries, working surface, evaluation, governance, and a practical next-stage decision.
Common questions
Questions this page answers.
Does event replay mean public logs?
No. Folium separates private operational logs, customer-facing status, and public-safe proof records.
Why do AI workflows need action receipts?
Receipts make state-changing work accountable by recording actor, source, permission, action, result, boundary, and next owner.