01
Property description generation
Turns raw listing data into publish-ready copy at scale, grounded in the actual property's details rather than generic filler.
Agentic AI in travel
Listing content that scales with your property catalog .
We build agents that turn raw listing and booking data into accurate, publish-ready content and guest support — the same real-data-grounded generation pattern proven in other production deployments.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Listing content does not scale with a human writer alone
Property descriptions, itinerary summaries and guest-facing copy all need to be accurate to the actual listing, distinct from every other property, and produced at a volume manual writing cannot sustain. An agent reads the real listing data and generates content grounded in it, so each description reflects the property it describes.
every description traces to real listing data
Use cases
Where agents earn trust in travel operations
Content and support workflows that need to scale with your catalog.
02
Guest support with booking context
Answers guest questions grounded in the actual booking and property data, escalating anything outside its scope.
03
Itinerary and amenity summarization
Summarizes complex itineraries or amenity lists into guest-facing content that stays accurate as details change.
04
Multilingual listing content
Generates or adapts listing content across languages for international travelers, grounded in the same source data.
What the mechanism delivers
Guided configuration and grounded answers, measured in production
Our closest travel-adjacent work (LLM-generated listing content for the vacation-rental marketing agency Guesthook) has no published figure attached, so it is not on this list. What is here comes from Mixam (print on demand), ARIJ Network (media) and Synera (engineering software), and it measures what booking and guest-service workflows depend on: walking a customer through an option space too large to browse, answering only from the operator's own content, and validating a multi-step output instead of generating it in one pass. Every figure links to the case study behind it.
Agents do not confirm a booking, issue a refund, or override a rate or availability rule. They assemble the options, ground every answer in your own listing and policy data, and draft what a guest-service or revenue reviewer signs off on — with each step logged.
Knowledge-inquiry response rate before the agent
1%
ARIJ Network's own knowledge base went largely unanswered outside a narrow set of scripted replies.
95.4%
Success rate for a production multi-agent product advisor
Not a travel deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations. It is the closest analog we have shipped to itinerary and booking configuration: a customer-facing agent narrowing an option space no one can browse.
2 hrs → 3 min
to generate a validated workflow
Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
Client results
Proof from production agentic deployments
We have shipped listing-content generation for the vacation-rental marketing agency Guesthook, but not yet an agent inside a hotel group, airline or OTA. These are the closest published references — a customer-facing advisor guiding a person through a billion-plus option space, a bilingual agent answering only from the client's own knowledge base, and a multi-step validation loop replacing a single generation pass.
Delivery path
From workflow audit to a production agent
TriStorm keeps content accuracy and engineering aligned.
Map content and data sources
We audit target content types, listing data quality, and review requirements — ranking automation candidates by volume and impact.
- Content & data audit
- Data quality review
- Prioritised use case
Build and validate the agent
We implement against real listing and booking data, with an evaluation suite scored for accuracy before publishing.
- Working prototype
- Evaluation suite
- Review workflow
Deploy with monitoring
Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own booking and guest-service workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in travel, answered
Where does agentic AI fit into travel and hospitality operations?
Content and listing generation at scale is the clearest case — property descriptions, itinerary summaries, and guest-facing copy that needs to be accurate and distinct across thousands of listings, not templated filler.
How is this different from a template-based description generator?
A template fills in blanks. An agent reads the actual property or itinerary data and generates content grounded in those specifics — the same real-data-grounding discipline behind Mixam's three-agent product advisor, which guides customers through more than a billion possible print-order combinations at a 95.4% success rate.
Do you have travel-specific proof, or is this adapted from other industries?
The closest is Guesthook, a vacation-rental marketing agency, where we automated property description generation with LLMs — listing content at volume, grounded in the real property data. We have not yet shipped an agent inside a hotel group, airline or OTA. For the coordination half we point to Mixam's three-agent product advisor, guiding customers through more than a billion possible print-order combinations at a 95.4% success rate; grounding output in real customer and catalog data at scale transfers directly to itinerary and guest-service content.
What other travel workflows suit an agent?
Guest support grounded in booking and property data, itinerary or amenity summarization, and multilingual content generation for international listings.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in three to six weeks, following the same TriStorm phases as any Vstorm engagement.
Do we own the system after it is built?
Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in.
Start with one workflow
Map one content or support workflow worth automating
A 30-minute call identifies the content types, data sources, and a realistic path to a working agent.