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Company · Approach

Operational truth first. Automation last. People throughout.

Most AI projects in travel start with a model and work backward. We start with what is actually true in your operation, then route it, predict it, and only then automate, with a person approving every decision that matters.

Start with a Discovery Sprint Book a discovery call
Five climbers roped together along a sunrise ridge, each carrying a different tool.
Field noteA method is a rope team: every phase is tied to the next, and nobody climbs alone.

Principles

Six principles we don't bend

Truth before intelligence

If your supplier data says a museum opens on Mondays and it doesn't, no model will save the itinerary. We fix the inputs first.

Route before you predict

A forecast is only useful if the plan can respond to it. We model constraints (windows, distances, pacing) before we model demand.

Predict before you automate

Automation should act on signals you trust. Forecasts run in shadow mode first, so we can compare them with what really happened.

Draft, don't decide

The system proposes; your people decide. Anything that spends money, changes a traveler's day or speaks for your brand needs a human yes.

Measure against the old way

Every pilot has a baseline captured before we start, so 'it feels faster' becomes a number you can check.

Make everything reversible

Every AI feature ships with an off switch, a fallback path and an audit log. Turning it off should be boring.

Method

Five phases, in this order

Each phase earns the next. Plenty of engagements pause after the second phase and are better for it.

  1. 01

    Operational truth

    Discovery Sprint · 3–4 weeks

    We map suppliers, hours, capacity, policies and content into one model, and measure where staff time goes today. You get a data-gap list, a baseline and a ranked opportunity map.

  2. 02

    Routing

    Pilot · early weeks

    Route Engine encodes your constraints: opening windows, drive times, pacing and energy budgets, accessibility and weather exposure. Your team confirms the days it builds are days they'd sell.

  3. 03

    Prediction

    Pilot · shadow mode

    Signals forecasts demand and cancellation risk alongside your current process without acting on it, so predictions can be checked against what actually happened.

  4. 04

    Automation

    Pilot · final weeks

    Booking Flow, Concierge or re-planning playbooks take on routine work within approval thresholds you set. Exceptions go to a person, with context.

  5. 05

    Operate and improve

    Ongoing

    We watch grounding, override rates, latency and cost, re-run evaluations as seasons change, and retire anything that isn't earning its keep.

Humans decide

Draft, don't decide

The most useful thing AI can do in travel is produce a very good first draft (a routed day, a parsed confirmation, a re-plan with its reasoning shown) and hand it to someone who knows the territory. They ink the final line. As trust is earned and measured, you may choose to let routine drafts go straight through. That's your call, made workflow by workflow, and never ours by default.

  • Auto-approved only when you say so: low-risk, reversible actions such as filing a confirmation or reissuing a voucher.
  • Always a person: refunds, supplier cancellations, changes to a traveler's day, and anything outside policy.
  • Visible reasoning: every draft states what it changed and why, so review takes seconds.
  • Overrides are data: each edit your team makes teaches the system what right looks like in your operation.
Read the field note
A mechanical drafting arm sketches faint route options while a human hand inks one final line.
Field noteMachines are generous with drafts. People sign their name to one.

Checklist

How we measure

We agree on the scorecard before the pilot starts and report it every week, including the weeks it looks bad.

0 of 10 ready
01Before the pilot
02During the pilot
03Before scaling

Side by side

Governance built in, not bolted on

Responsible AI isn't a review at the end. It's a set of defaults that run from the first week.

Typical AI rolloutHow we roll out
Starting pointA model demo and a hope that the data will follow.An audit of the data and decisions the model will touch.
Traveler dataCollected broadly, just in case.Minimized, consented, and tagged by sensitivity: mobility, diet, health, family.
Who decidesThe model, unless someone happens to catch it.Your team, with approval thresholds set per workflow.
When it's wrongDiscovered by a traveler.Caught by evaluation sets, monitoring and a human review queue.
Turning it offA project.A switch, with a documented fallback.
LeavingYour data in someone else's format.Your data exported in standard formats on request.

Questions

Questions about our approach

Do we have to go through all five phases?

No. Some clients stop after operational truth and routing because that alone returns enough hours. The phases are an order of operations, not a mandatory sequence.

What if our data is too messy for AI?

That's normal, and it's exactly where the first phase starts. Our data engineering work exists because almost every operator has suppliers listed three ways and policies living in someone's inbox.

Can we let the system act without approval?

For specific, low-risk workflows, yes, once the override rate shows it has earned it. You set the thresholds, and you can tighten them at any time.

How do you handle responsible AI and traveler data?

It's designed in from the sprint: consent, minimization, bias reviews and safety testing. Our Responsible AI & Traveler Data Governance service goes deeper when you need formal policies or an independent review of an existing system.

Related

Keep exploring

Start with what's true

A Discovery Sprint takes three to four weeks and ends with a baseline, a ranked list of opportunities and one pilot worth running.

Start with a Discovery Sprint See engagement models