Industry · Travel & Hospitality
The AI understands the request. It doesn't get to decide.
We build guest-facing systems for a living — including our own hotel concierge product. The lesson from it: be deterministic on purpose, and put a human on the hook for everything else.
- AI Agents
- Generative AI & RAG
- 30-day AI Sprint
Why now
Guests message rather than call, and they expect an answer now, in their language, at 2am. Template bots that confidently invent a pool closing time do more damage than an unanswered message. The systems that work answer only from the property's own information and convert everything else into an owned, clocked work item.
The problem
Where travel and hospitality teams lose time today.
- 01
Calls nobody answers
Front desk is serving the queue in front of it while the phone rings, and the guest who wanted an extra towel is now writing a review.
- 02
Requests get lost between shifts
A request taken verbally at 11pm has no owner, no clock and no record when the morning shift arrives.
- 03
Multilingual guests get worse service
Staff coverage across languages is uneven by shift, so response quality depends on who is on duty.
Constraints
What makes this harder than a generic chatbot.
- 01
Grounded answers only
The assistant answers from the property's own FAQ and nothing else. No general knowledge about what hotels usually do — that's precisely how a bot invents a spa you don't have.
- 02
A fixed menu of actions
Structured request types the system can actually fulfil, plus an explicit fallback so nothing stays unclassified. Open-ended action space is where guest-facing AI goes wrong.
- 03
Every request gets a clock and an owner
Response and resolution tracked as separate SLAs against a named staff member, so 'the bot handled it' is never the last thing anyone knows.
Integrations
Systems we wire into.
- Property management systems
- WhatsApp Business API
- QR-based guest web
- Native staff apps with push
- Booking engines & OTA feeds
- Twilio voice
- Revenue management tools
Services
What we build for travel and hospitality.
- AI guest messaging agents for hotelsMessaging agents that answer from the property's own information, turn requests into structured work items, and escalate anything else to a named staff member.Read
- RAG for hotel and travel contentRetrieval scoped to your own FAQ, policy and destination content — multilingual, cited, and explicitly silent where the content doesn't exist.Read
- 30-day AI sprint for hospitalityScope it to a single property and the highest-volume request types. In 30 days you get working software running on real guests, with the numbers to decide about the rest of the estate.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in travel and hospitality: the questions we get.
Have you actually built one of these?
Yes — AI Hotel Concierge is our own product, built in-house through Phase 0 with 42 recorded architecture decisions, six fixed request types and three shipped surfaces. The case study on this site walks through both the guest and admin sides.
What stops it inventing property details?
It answers only from the property's own FAQ content, and when the FAQ doesn't cover a question it creates a work item for staff instead of guessing. That constraint is the product, not a limitation of it.
Do we need to replace our PMS?
No. The concierge sits alongside it and writes requests where staff already work. Replacing a PMS to add guest messaging is a two-year project to solve a two-month problem.
Contact
Talk to us about AI in travel and hospitality.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read