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Aviation: Custom AI Solution

Aviation AI should not begin as a generic service chatbot. It should start from the most time-consuming and measurable workflows in service recovery, operational knowledge, B2B account collaboration, and passenger communication.

// AI use cases to start with

Service recovery assistant

Use disruption context, fare rules, loyalty tier, and compensation policy to prepare the next best service options with clear sources and constraints.

Baggage / complaint triage

Classify service text, emails, forms, and attachments by issue type, urgency, missing information, and owning team.

B2B account copilot

Combine corporate travel contracts, demand history, SLAs, open cases, and renewal risk so account managers can prepare meetings and follow-up tasks.

// How EKel would deliver it
  1. 01Choose one measurable workflow first, such as complaint triage time, recovery handling time, or B2B meeting prep time.
  2. 02Create an aviation reference dataset covering disruptions, compensation, baggage, loyalty, and corporate account cases.
  3. 03Constrain AI responses to policy, SOPs, CRM data, and verifiable integration sources.
  4. 04Monitor with evals, human sampling, and logs to prevent wrong commitments, wrong compensation, or data leakage.
// Best fit
  • →Airline or travel service teams with high service volume, complex workflows, and frequently changing policies.
  • →Organisations whose service agents still search multiple systems manually — AI can collapse this into a single query surface.
  • →Teams that want one KPI-backed AI sprint before rebuilding a whole service platform.
// Sources
Salesforce Travel, Transportation, and HospitalitySalesforce Agentforce for Industries
Back to industriesSee aviation Salesforce →
// Custom AI architecture

Aviation AI is not a chatbot — it is a four-layer policy-bound system.

// LAYER L4
User layer
Service agents, airport ground crew, members, corporate-travel managers — interacting via web, mobile, or CCaaS surfaces. Every interface keeps a human-review escape hatch.
CCaaS · WebAgent deskMobile · Slack
// LAYER L3
Application layer
Built with **Vibe Coding** — custom apps embedded in service-recovery / baggage / B2B copilot flows. Complex scenarios (e.g., multi-leg disruption coordination) run as **agentic workflows** — AI agents chain PNR, loyalty, compensation policy, and partner systems. One use case, one KPI — no “unified AI platform.”
Vibe CodingAgentic WorkflowCCaaS hooks
// LAYER L2
AI layer
LLM Gateway + policy / SOP RAG + decision matrix + eval pipeline + guardrails. AI responses are bound to internal policy and SOP — no free-form output.
LLM GatewayPolicy RAGEval · Guardrails
// LAYER L1
Data layer
Vector DB (policies, SOPs, compensation rules embedded) + structured data (PNR, member, case) + full audit log. Customer-data classification decides what may leave the data centre.
Vector DBPNR · MemberAudit log
// Pre-launch ops checklist

Every item ticked — that is what “ready to handle real cases” means.

01
Policy boundary

Compensation bounds, cabin upgrades, and cancellation policies must be quantified in a decision matrix first — AI does not improvise “special handling.”

02
High-risk escalation rules

VIP / media-sensitive / cross-airline / high-value cases must escalate to humans — this rule lives outside AI, enforced by case routing.

03
PNR sync latency

Disruption and cabin changes must reach AI context in seconds — nightly batch is not enough. Use Platform Events / Kafka against the PSS.

04
Hallucination monitoring

Continuous sampling after launch (auto-eval + human review). Alert thresholds for high-consequence outputs like compensation commitments or rule answers.

05
Human-in-the-loop

AI prepares the proposal; agents send the final reply — especially for cases that might surface in the press.

06
Eval & reference dataset

Before launch, run 100+ real aviation scenarios (compensation, baggage liability, loyalty rules). Re-run on every model version change.

// FAQ

Five questions clients ask most about aviation AI.

01Can AI service fully replace human agents?
No — especially not in aviation. AI handles policy-quantifiable cases (flight lookup, simple disruption compensation, loyalty-rule lookup), which is typically 60–70% of call volume. The remaining 30–40% — special customers, media risk, cross-airline service, out-of-policy requests — must go to humans. The point of AI is to let agents spend time on the latter, not to replace them.
02Is compensation calculated by AI automatically or confirmed by an agent?
Two-step. AI computes the policy-recommended range (cabin × tier × delay × route → bounds); the agent picks a final amount within that range and sends it. This blocks AI over-promising and frees the agent from arithmetic. Every final decision is logged to the audit trail.
03What if AI sees stale PSS / GDS data?
Two layers. (1) The integration layer uses event-driven sync (Platform Events / Kafka) instead of batch, reducing latency from hours to seconds. (2) AI prompts must carry a data timestamp — if the answer reflects PNR state from five minutes ago, say so. This prevents customers hearing stale info as if it were current.
04How do you handle multilingual? Aviation service spans CJK, English, Cantonese...
Frontier models handle CJK + English + Cantonese + Korean with comparable quality. The real multilingual challenge is consistency in the policy documents themselves — the same compensation policy phrased differently in EN and ZH will cause AI to answer inconsistently. The practical move: keep policy as a single-language source of truth (usually EN) and translate AI responses on the fly, labelled “translated from EN.”
05Why not use off-the-shelf aviation AI service products?
If a vertical SaaS fits, buy it — engineer’s judgement, not consulting line. Custom is right when: (1) the carrier’s loyalty / compensation structure does not match the SaaS schema; (2) deep integration with proprietary PSS or partner systems is required; (3) multilingual / multi-region / cross-partner accounting exceeds what off-the-shelf was designed for. The first two are common at larger carriers. However, if the airline already runs on Salesforce (Service Cloud / Sales Cloud / Loyalty Management), our stronger recommendation is **Salesforce + Agentforce** — AI agents run directly on top of the customer 360 and existing case routing, without bolting on another vendor layer. Data permissions, audit trail, multilingual support, and cross-cloud integration come built-in — exactly the things pure vendor SaaS struggles to match.

Aviation AI has to explain where every answer came from.

We can use your real service cases to design the eval set, then decide which aviation workflow should enter the first AI sprint.

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