Airline operations
Your operations AI is about to stop answering and start acting.
Assistants that answer questions from crew, maintenance and operations data are safe: they only read. The next phase is different. When the AI’s output starts triggering bots and writing to crew, roster and ERP systems, one wrong action becomes a wrong roster, a missed rest period or a record nobody can explain. We put a governed layer between the AI and every system it touches.
The real problem
Three things that break when automation scales in an airline
Hundreds of automations, built fast
Bots and scripts built by operations teams each work on their own, but they write to the same crew, roster and HR records with no shared rules, no shared audit trail and no single owner.
AI moving from read to write
A language model is right most of the time. In a 24/7 operation, “most” is not enough: one unchecked write to a roster or a maintenance record can break a legal limit before anyone sees it.
Alerts with no record
A standby call-out is sent from a draft or cached roster instead of the published one. The change is withdrawn, and nothing records that the alert was sent. The pilot’s rest was interrupted, and neither the pilot nor the airline can prove it: a ghost call.
Try it
Watch a ghost call happen, and stop it
Same events, two lanes. The left lane alerts on the draft change. The right lane alerts only after the roster change is committed. Press Run.
A standby call-out, with and without the fix
Both sides see the same roster change. Without the fix, the alert goes out on the draft change and the app reads a replica that is a minute behind. With the fix, nothing is sent until the change is committed, the alert carries the committed version, and every step is recorded.
| Without the fix | With the fix | |
|---|---|---|
| Alerts sent to the pilot | 0 | 0 |
| Blank or wrong screen on tap | 0 | 0 |
| Stale reads | 0 | 0 |
| Audit records | 0 | 0 |
Press Run. Without the fix, the pilot gets an alert, taps it, sees a blank screen, and no record exists. With the fix, nothing reaches the pilot and every step is recorded.
Without the fix: alert on the draft change
With the fix: alert only after commit
Runs entirely in your browser on scripted example data; nothing is sent anywhere.
How we fix it
The AI proposes. Rules and people decide. The system’s own door executes.
1. The AI only interprets
It turns a request, a note or a message into a typed, structured request. It never writes to a system itself.
2. Fixed rules decide
Flight-time and rest limits, permissions, MEL limits and part numbers are checked by deterministic rules, the same way every time.
3. A person approves
Where the operation requires it, the dispatcher sees the proposed action and approves it with one click.
4. Executed and recorded
Only committed, approved actions go out, through each system’s supported interface, with a record of what happened and why.
Building blocks
What sits between your AI, your bots and your systems
Commit-aware event queue
Crew and ground alerts wait until the roster change is committed and legal. A withdrawn draft sends nothing.
Read-load separation
Machine-speed queries from apps, dashboards and bots read from a fresh replica, not from the crew or maintenance database itself.
Outbox debounce gate
Bursts of dispatch changes are combined and checked, so only the final, verified message goes out.
Governed AI execution bridge
The single path from the AI’s structured request, through the rules and the approval, to the system that executes it.
Before the next phase
Sort the automations you already have
You do not have to rebuild hundreds of automations. You need to know which ones can hurt you.
- Which only read, and which write to crew, roster, maintenance or HR records
- Which go through supported interfaces, and which copy tables or click through screens
- Which respect permissions, effective dates and approvals, and which leave an audit trail
- Fix the risky ones first. Leave the harmless ones alone.
Start with one workflow
The 2-week diagnostic for airline operations
We take one workflow, for example standby call-outs or one action the AI will trigger, and hand you:
- A map of the systems, events and rules the workflow touches
- Its failure points, including where alerts can fire before commit
- A target design for the governed route, with the approval step
- A test plan and the acceptance criteria for going live