How Holida Hero scaled ai powered guest-communication with LangWatch
HolidayHero uses LangWatch to monitor thousands of guest interactions, optimize AI-generated content, and ensure every guest communication feels personalized, accurate, and on-brand — helping their team improve LLM quality without sacrificing hospitality standards.
Snapshot
Company | Industry | UseCase |
|---|---|---|
Holidayhero | Hospitality Tech | LLM monitoring, evals, prompt optimization & AI content QA |
Results at a Glance
Thousands of Guest Interactions Monitored | Daily AI Optimization Workflow | Faster Issue Detection | Hours Saved Every Week |
|---|---|---|---|
LangWatch has been running in HolidayHero’s production environment for over 2 months, monitoring thousands of guest-facing AI interactions and content generations | LangWatch is used every day by the team to improve prompts, evaluate outputs, and maintain content quality across properties | Negative guest sentiment and thumbs-down feedback automatically trigger Slack alerts, allowing fast review and improvement loops | Instead of manually reviewing outputs across multiple systems, the team now uses centralized monitoring, filtering, and Optimization Studio experiments to improve quality faster |
Pull Quote
“The analytics and evaluations are invaluable. We can track how well our AI performs and adapt accordingly.”
Hjalte H. CEO, HolidayHero
About HolidayHero
HolidayHero helps hospitality businesses improve guest experiences and streamline communication across the entire guest journey from booking to booking again.
Their platform supports hotels, vacation rentals, and hospitality operators by automating guest communication, guidebook creation, local recommendations, and personalized content delivery.
Hospitality has traditionally been built on face-to-face interactions. Guest expectations are shaped by personal attention, fast answers, and highly contextual service.
As AI becomes a bigger part of the guest journey, the challenge is not simply adding automation.
It is making automation feel personal.
That is where HolidayHero focuses its AI efforts.
The Challenge: Scaling AI without losing hospitality quality
HolidayHero uses AI to generate and improve a large amount of guest-facing content.
That includes:
digital guidebooks
house rules rewritten to match property branding
local recommendations and amenity descriptions
summarized hospitality content
automated guest emails and touchpoints
These are not internal workflows.
They are direct guest experiences.
If the output feels generic, inaccurate, or off-brand, the guest notices immediately.
As customer demand increased and LLM usage expanded, HolidayHero needed a better way to monitor and improve these systems continuously.
They were asking questions like:
Are our prompts producing the right tone?
Are outputs aligned with the property’s brand voice?
Where are guests dissatisfied?
How do we improve low-quality outputs quickly?
Which prompt or model version performs best?
Without proper observability, these questions became difficult to answer at scale.
The Goal: Continuous monitoring + better guest experiences
HolidayHero wanted AI to support two major business goals:
Personalization
Every traveler expects tailored communication.
Even with automation, guest interactions still need to feel thoughtful, relevant, and personal.
The goal was not generic automation—it was personalized hospitality at scale.
Automation
Many guest questions are repetitive.
Check-in instructions, amenity descriptions, local recommendations, and property details can all be answered faster through AI. Done well, this reduces workload for hospitality teams while improving guest response speed. But both goals depend on quality. Automation without trust creates more work, not less. HolidayHero needed visibility into LLM performance before they could scale either.
The Solution: LangWatch for Monitoring, Evaluations, and Optimization
HolidayHero integrated LangWatch into their LLM production environment to monitor and improve guest-facing AI systems. For over two months, LangWatch has been monitoring thousands of interactions and content generations daily. The team uses LangWatch across three core workflows:
Monitoring production outputs
They track LLM inputs and outputs across guest interactions and content generation to understand how their systems perform in real-world usage. This helps them identify where outputs succeed—and where quality drops.
Alerts for negative guest feedback
When guest sentiment turns negative or thumbs-down feedback is submitted, LangWatch automatically alerts the team in Slack.
This creates a fast feedback loop.
Instead of manually searching for problems, they can immediately locate the exact input and output that caused the issue, turn it into a dataset, and improve the prompt or model from there.
“If a guest interaction needs review because the sentiment was negative, we are alerted in Slack and can quickly locate the input-output and improve it.”
Optimization Studio for Prompt and Model Improvement
HolidayHero also uses LangWatch’s Optimization Studio to experiment with prompt and model performance across full datasets.
The team had previously explored DSPy internally, but making that accessible to the broader team was difficult.
Prompt Optimization Studio changed that.
Now they can:
evaluate datasets generated from production monitoring
test multiple prompt versions
run DSPy optimizers
compare iterations side-by-side
improve outputs against guest expectations and brand tone
This makes optimization collaborative instead of isolated to highly technical team members.
“The Optimization Studio gives us the ability to tweak and test different versions of our LLM features, helping us land on the most engaging and relevant outputs for guests.”
Where HolidayHero uses AI today
Guidebook Generation
Most properties have house rules and practical guest information, but traditional documentation often does not fit a digital guest experience. HolidayHero uses AI to rewrite and restructure this content so it matches each customer’s branding and feels more natural for guests.
Summarizing Local Experiences and Amenities
Local recommendations are critical in hospitality, but they are often undocumented or inconsistent. AI helps HolidayHero generate useful descriptions for experiences, amenities, and local recommendations—faster and at much larger scale. This allows hotels and hosts to deliver richer guest experiences without manually writing everything from scratch.
Guest Email Automation
Guest communication needs to happen at the right moment.
HolidayHero uses AI-powered touchpoint automation to rewrite and personalize emails across the guest journey—from pre-arrival to post-stay communication.
This helps hospitality teams stay proactive without adding manual workload.
The Results
Millions of Interactions Monitored with Full Visibility
Instead of guessing how LLMs perform, HolidayHero now has production-level visibility into guest-facing AI behavior.
This helps them continuously improve quality rather than reacting after issues escalate.
Faster Improvement loops
Negative sentiment detection and Slack alerts dramatically reduce the time between issue discovery and resolution.
The team can move from guest feedback to prompt improvement much faster.
This improves both guest satisfaction and internal efficiency.
Weekly Hours Saved Through centralized Evaluation
The team spends several hours every week reviewing performance inside LangWatch.
Without centralized monitoring, filtering, and optimization workflows, that work would be significantly slower and spread across disconnected tools.
Now improvement happens in one place.
Better Collaboration Across the Team
Optimization Studio made advanced prompt experimentation accessible beyond only highly technical users.
Instead of prompt optimization living with one engineer, the entire team can participate in improving guest-facing AI quality.
This makes AI quality an operational function—not just a technical one.
“Being able to continuously monitor and improve outcomes changed how we work with AI across the company.”
What’s Next: Personalization + Automated Guest Support
HolidayHero sees AI becoming even more central to hospitality operations. The roadmap focuses on two major areas.
Deeper personalization
Guests expect the same level of personal attention they would receive in traditional face-to-face hospitality.
HolidayHero wants AI to preserve that standard—even as automation scales.
Personalized recommendations, contextual guest communication, and adaptive experiences remain top priorities.
More intelligent automation
Questions that have already been answered should not require human repetition.
With guidebooks, amenities, and property knowledge already available, HolidayHero is building toward more immediate and automated guest support powered by AI.
Done well, this reduces workload for teams while improving service speed and operational efficiency.
Key Takeaways
Guest-facing AI needs stronger quality control than internal tools
When AI writes directly to customers, monitoring and evaluations become business critical.
Negative feedback should trigger improvement loops automatically
Slack alerts and trace visibility turn guest dissatisfaction into immediate optimization opportunities.
Optimization must be accessible across the team
Prompt quality should not depend on one technical expert.
The best AI systems improve when operations, product, and technical teams can all contribute