Now accepting projects — Q3 2026

Travel & Tourism: Custom AI Solution

Travel AI’s real sweet spots are multilingual service triage, itinerary draft generation, and destination content multilingualisation — the work that consumes most labour every day. Our design principle is augmentation: AI drafts, humans sign; anything involving money or irreversible action (quoting, cancellation, compensation) requires human checkpoint.

// AI use cases to start with

Multilingual service triage + draft replies

Route cross-language / timezone inquiries to the right queue in the first second; pre-generate draft replies for human review.

Itinerary draft generation

Produce day-by-day drafts from traveller preferences + destination knowledge so planners refine instead of typing from scratch.

Destination content multilingualisation

Auto-produce ZH / JA / KR variants of EN destination content (human QA before publish), reducing the marketing team’s multilingual maintenance load.

// How EKel would deliver it

01Define hard constraints first: which data must never reach commercial LLMs, which use cases are off-limits (quoting, cancellation, compensation), which require mandatory human review.
02Choose deployment: traveller PII / payment fields go through private deployment or compliance LLM gateway; public destination content may use commercial LLMs. Models prefer Llama / Mistral / Gemma open-source families.
03Build a reference dataset from real multilingual inquiries, itinerary customisation, refund / change scenarios — 100+ cases with explicit hallucination testing (wrong destination info cost is real).
04Post-launch: full audit log + monthly human-sampled review + regression tests on every model upgrade. If the client already runs Salesforce, our stronger recommendation is Salesforce + Agentforce.

// Best fit

→Travel businesses with high service volume, multilingual travellers, deep itinerary customisation, that want AI to free human time for high-judgement work.
→Operators with clear compliance boundaries and data classification (especially cross-border transfer for international travellers).
→Programs that want to prove ROI on one vertical use case (e.g., multilingual service) before rebuilding the whole service platform.

// Sources

Salesforce Travel, Transportation, and Hospitality Salesforce Trust & Compliance

Back to industries See travel Salesforce →

// Custom AI architecture

Travel AI is a four-layer multilingual-first stack — human-confirmed before any booking.

// LAYER L4

User layer

Travellers, service agents, itinerary planners, marketing, and partner ops — interacting via web, mobile, chat, or agent desk. Every AI surface keeps a human-review escape hatch (especially anything affecting booking value).

Traveller / AgentItinerary PlannerPartner Ops

// LAYER L3

Application layer

Built with **Vibe Coding** — itinerary draft generation, multilingual service triage, partner-response quality monitoring. **Agentic workflow** fits cross-partner status / confirmation flows; anything affecting money or irreversible action (cancellation, compensation) requires human checkpoint.

Vibe CodingAgentic WorkflowHuman-in-loop

// LAYER L2

AI layer

LLM Gateway + product / partner / policy RAG + eval pipeline + guardrails. Common model mix: Llama / Mistral / Gemma open-source models alongside commercial LLMs for multilingual generation. Commercial / B2B customer data never reaches commercial LLMs.

LLM GatewayLlama / Mistral / GemmaEval · Guardrails

// LAYER L1

Data layer

Vector DB (packages, destinations, policies, FAQs embedded) + structured data (preferences, trip history, interactions) + full audit log. Traveller PII and PCI fields stay in private deployment; PCI scope is not expanded.

Vector DBTraveller RecordsAudit log

// Pre-launch · 6 operational disciplines

Six things to resolve before any travel AI launch.

Traveller PII & PCI classification

Which fields may go to commercial LLMs, which require private deployment, and which (credit card, passport) must never be indexed? Without privacy / PCI / cross-border-transfer clarity, do not start.

Pricing & inventory source of truth

AI does not quote prices, does not declare inventory. Any price / room / seat shown to a traveller must come from a real-time GDS / PMS / OTA call — AI generates itinerary descriptions and packaging suggestions only.

Multilingual hallucination checks

LLM cross-language generation hallucinates most often on destination details (visa, opening hours, cultural taboos). The eval set must cover every primary language × primary destination combination and re-run on every model upgrade.

Marketing copy compliance & brand boundary

AI-generated marketing that touches health, safety, or price commitments goes through a human-review queue. Brand-voice prompts are versioned to prevent drift.

Partner response tracking

If AI monitors partner response quality (hotel confirmation latency, DMC execution quality), thresholds and scoring must be transparent to partners — black-box ratings invite disputes.

Eval & travel context dataset

Before launch, run 100+ real scenarios (multilingual service, itinerary customisation, refund / change / compensation, sudden disruptions). Re-run on every model version. No pass, no ship.

// FAQ

Five questions that come up most in travel AI discussions.

01Which travel AI use case has the best ROI?

Three consistently high-ROI use cases: (1) **multilingual service triage** — route cross-language / timezone inquiries to the right queue in the first second + pre-generate draft replies for human review; (2) **itinerary draft generation** — produce day-by-day drafts from traveller preferences + destination knowledge so planners refine instead of typing from scratch; (3) **destination content multilingualisation** — auto-produce ZH / JA / KR variants of EN destination content (human QA before publish). All three are augmentation, not decision replacement. Money-affecting use cases (quoting, compensation) are best deferred until guardrails are mature.

02Why not use off-the-shelf travel AI products?

If a vertical travel SaaS fits, buy it — engineer’s judgement, faster and cheaper. Custom is right when: (1) your product structure is unusual (semi-customised tours, niche destinations, large group bookings) and SaaS schema does not match; (2) language combinations exceed what off-the-shelf was designed for (e.g., TC + SC + JA + VI mixed destinations); (3) deep integration with proprietary GDS / PMS / loyalty is required. If the client already runs Salesforce, our stronger recommendation is **Salesforce + Agentforce** — service / loyalty / marketing AI in one platform.

03How do traveller PII and AI coexist?

Principle: traveller PII, passport, credit card, family structure never go to commercial LLMs. Practical move: redact before prompting (name → [TRAVELLER], passport → [PASSPORT], card numbers fully stripped) so the LLM sees only anonymised preference context. When a use case truly needs PII (e.g., generating a name-personalised welcome letter), route through private deployment or a compliance LLM gateway. Taiwan privacy law + cross-border transfer (especially for JP / KR / EU destinations) must be designed on day one.

04Where does agentic workflow fit in travel?

Good fits: (1) cross-partner status checks (asking hotels for booking confirmations, DMCs for pickup times); (2) traveller data enrichment (assembling a full profile from multiple sources); (3) large-group logistics coordination (meals, seating, room assignments). Bad fits: (1) money-affecting decisions (quoting, compensation); (2) irreversible operations (cancellations); (3) final-confirmation steps in direct traveller communication. Principle: agents handle “gather / look up / propose,” humans decide “commit / change / communicate externally.”

05You have no published travel AI case — is that a risk?

Honest answer: we have no publicly referenceable travel AI case. But the methodology (LLM Gateway design, RAG pipeline, eval framework, guardrails, Vibe Coding custom apps, agentic workflow design + limits) is industry-neutral; we have applied this discipline in financial-services, aviation, retail, government, and education AI work — multilingual / compliance / PII tiering / eval pipeline are not travel-only problems. Destination-specific knowledge and commercial intuition we will learn alongside your team from week one — that is how enablement-mode work runs.

Travel AI is most stable with “multilingual + draft + human-signed.”

In 30 minutes we can map your compliance constraints, multilingual load, and acceptable risk — then decide which AI use cases truly belong in production.

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