The following use cases are an illustrative example per sector. Each snapshot shows what the agent evaluates before acting, what it executes autonomously when it can do so with guarantees, and what the analyst receives when it cannot.

Prescriptive Decision AI for Airlines
Airlines
Revenue management and dynamic pricing

The revenue management model closes and opens fare classes in real time across thousands of flight–date combinations. The analyst receives the decision but not the reasoning: they do not know how certain it was, what would invert it, or whether reopening Y would capture new demand or cannibalise already-secured corporate revenue. AyGLOO adds an agentic layer on top of that model with a precise decision function: maximise revenue per flight under a demand-mix constraint — not just open/close classes, but pick the combination that maximises net revenue while avoiding cannibalisation. The agent executes automatically when the signal is robust and ISA confirms reliability in that segment. When reliability is lower or ISA detects an uncertainty zone, it alerts the analyst with the scenario tree already built to decide in minutes. And when acting would destroy more revenue than it captures, it explicitly recommends not acting.

1
The agent executes closes/opens automatically when reliability is total and ISA confirms the segment. The analyst intervenes only where operational context can change the optimal decision.→ +1–2% revenue per flight on high corporate-mix routes
2
ISA confirms whether the model is reliable in the specific demand segment — corporate full-flex, early leisure, groups — before execution. In low-reliability segments, the agent routes to the analyst even if Twin rules say 100%.→ Analyst overrides backed by data, not instinct
3
CF computes the exact acceptable upgrade threshold per customer profile and the minimum price that would retain leisure demand without cannibalising corporate revenue — before any action.→ Upgrade/retention decisions quantified per profile, not generic rules
4
The scenario tree compares projected revenue (in euros) for each possible combination of open classes — including the “do nothing” scenario — before executing any change.→ €5,600 avoided destroyed revenue per flight by not reopening Y at the wrong price
Today: what a revenue analyst typically receives
IB9XXX MAD→JFK · 14 Mar Y fare: €1,847 · CLOSED Economy · Y closed by model · No additional context
With AyGLOO. Same pricing decision, fully enriched
IB9XXX MAD→JFK · 14 Mar · 31 days to departure Load factor: 74% · Y closed · Lowest open: €1,847 The agent acts
XAI Decision explanation Twin Reliability-level rules ISA Model reliability in this segment CF Upgrade threshold and demand retention price What-if Variable sensitivity Econ Revenue scenario tree Action Agent decision
1. Why this class was closed. Twin model (depth 7 · fidelity 97.1%)
100%IF booking_pace > 2.0× AND corporate_share > 65% AND days_to_departure < 35 → Confirm Y closure. Under these conditions keeping Y open reduces net revenue in all historical cases. The agent executes without manual review.
100%IF same-day_competitor_fare > fare_Y AND load_factor > 70% → Do not reopen below €2,100. The competitor gap supports upward pressure; opening Y cannibalises revenue without capturing net new demand.
87%IF last 4 departures closed Y at 28–33 days AND final_load > 88% → Consistent historical pattern. High probability to close above 90% load — the agent alerts the analyst to validate.
62%IF booking_pace > 2.0× with no other factors → Weak signal in isolation. The agent recommends not acting: the risk of destroying revenue exceeds potential capture.
Full-confidence rules let the agent execute without analyst intervention. The 87% rule is the true tension zone: the pattern is consistent but not decisive — the agent alerts the analyst with the scenario tree prepared. The 62% rule is most valuable for business: the agent explicitly recommends not acting when an isolated signal does not justify cannibalisation risk.
XAITwin
2. Model double-check in this segment (ISA)
Segment: "corporate full-flex high share · pace >2× · MAD–JFK long-haul" · Corporate (full-flex, high yield): 68% · Leisure (early purchase): 24% · Group: 8% · Remaining corporate price elasticity: low (0.18) · Closing Y will not materially reduce corporate conversion · High reliability: the agent executes with confidence.
Highest-uncertainty segment: on MAD–JFK routes with corporate share below 40%, the model has 61% historical accuracy and produced false positives in post-holiday periods. In that scenario the agent would not execute automatically — it would route to the analyst even if Twin rules say 100%.
ISA is the agent’s double-check before execution: it confirms not only that the signal is strong but that the model behaves well in the specific segment. An amber ISA blocks automatic execution regardless of Twin confidence.
ISA
3. CF. Upgrade threshold and minimum leisure retention price
Acceptable Business upgrade threshold for this profile (corporate full-flex, MAD–JFK, 31 days): maximum differential €480 over Y fare · Current Business–Economy differential: €1,380 · Do not offer upgrade now: the gap is 2.9× the historical acceptance threshold for this segment
Minimum price to retain leisure demand: if Y were reopened, fare would need to drop to €1,520 to convert 3 additional bookings · At that price incremental revenue would be €4,560 versus estimated displaced corporate revenue of €6,200–€7,800 · Do not reopen Y below €2,100.
Recommendation holds for any displaced revenue above €1,860/booking, within the historical range of the corporate segment in comparable windows.
CF is not sensitivity: it is the concrete minimal action that would change the customer/class decision. The €480 upgrade threshold is calibrated against the historical acceptance distribution for this profile on this O&D over the last 24 months.
CF
4. What-if. Which variable changes the recommendation
If booking pace dropped to 1.4×: recommendation changes to review reopening Y at €1,980 · If corporate share fell to 50%: elasticity rises to 0.41, keeping Y closed cost drops to €800–€1,200 · If competitor lowered fare to €1,700: upward pressure disappears, review reopening threshold · Corporate share is most sensitive: a 15-point drop flips the closure recommendation.
What-if identifies the exact threshold where the closure recommendation stops being valid. That becomes the automatic re-evaluation condition the agent monitors, without manual follow-up.
What-if
5. Revenue scenario tree. Economic decision function
Scenario
Projected revenue
vs current strategy
Y closed · W open ✓
€487,200
— baseline
Y reopened at €1,847
€485,400
−€1,800
Y reopened at €1,520
€481,600
−€5,600
Y closed · W closed
€471,000
−€16,200
→ Agent confirms Y closed + W open: maximises net revenue while avoiding cannibalisation · Review in 7 days if pace drops below 1.4×
The W-closed scenario (−€16,200) is most useful: it shows why closing W is not recommended even if pace and load may tempt it. Leisure elasticity makes load loss outweigh yield gain.
Econ
6. Agent decision
The agent executes — no analyst intervention
Both full-confidence rules hold and ISA confirms high reliability in this segment · Confirms Y closure automatically · Sets reopening threshold at €2,100 · Monitors booking pace and auto re-evaluates if it drops below 1.4× · Decision traceability exportable for revenue audit
or, if reliability is medium (87%) or ISA flags an uncertainty zone
→ The agent alerts the analyst: full scenario tree with projected revenue for each possible class combination · CF with upgrade threshold and minimum retention price · exact condition that would change the recommendation · analyst decides override with data, not instinct
or, if the signal is weak and isolated (62%) with no corporate share backing it
→ The agent recommends not acting: acting on a weak signal would destroy more revenue than it captures · the agent documents the non-intervention recommendation with quantified revenue at risk · monitors conditions that would validate a future action
The agent has three paths: execute, alert the analyst with context, or explicitly recommend not acting. The third is the most differentiating: in revenue management, most bad overrides are not “wrong action”, but acting when you should not.
Action

Illustrative example. Each deployment is adapted to each airline’s models, data, and operating procedures.

Estimated impact · route network with high corporate mix
+1–2%
Revenue uplift per flight
On routes with corporate share >60% · closures executed at the optimal moment with no operational delay
−80%
Manual overrides without data backing
Analyst intervenes with scenario tree prepared · corrections stop being detected weeks later in the revenue report
€5,600
Avoided destroyed revenue per flight
By not reopening Y at the wrong price when the signal does not justify it · the agent quantifies the cost of acting before acting