Answers from your property, not from the internet.

• 01

  Property FAQ retrieval

  Facilities, policies and timings answered from the content you maintain, with the gaps in that content reported back to you weekly.

• 02

  Destination and concierge content

  Recommendations from your own curated list and partner agreements — not from whatever the model recalls about the city.

• 03

  Multilingual coverage

  The same grounded content served in the languages your guests arrive with, evaluated per language rather than assumed equal.

• 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.

1. 1

   Data inventory

   What docs / databases / APIs / feeds matter? What's PII vs public? What changes daily vs quarterly? We map it before writing code.

2. 2

   Retrieval architecture

   Hybrid (BM25 + vector) retrieval, chunking strategy, reranking, citations. Tuned for your domain not a generic benchmark.

3. 3

   Eval suite

   Real-world question set with golden answers. Faithfulness, context precision, latency. So you know when changes ship a regression.

4. 4

   Guardrails

   Prompt-injection defense, PII scrubbing, refusal patterns, output schemas. Especially critical for customer-facing deployments.

5. 5

   Production + iterate

   Cost-effective inference (caching, fallback models), monitoring, weekly eval reports. Fine-tune when the data justifies it.

• Property management systems
• WhatsApp Business API
• QR-based guest web
• Native staff apps with push
• Booking engines & OTA feeds
• Twilio voice
• Revenue management tools

FAQ

RAG for hotel and travel content: your questions.

• Our FAQ is thin. Is that a blocker?

  It's the first deliverable, not a blocker — the system reports what guests asked that it couldn't answer, which builds the FAQ from real demand rather than a marketing team's guess.

• Can it recommend restaurants and activities?

  From your curated list and partner agreements, yes. From general knowledge, no — that's how a concierge ends up recommending a place that closed last year.

• Should I use RAG or fine-tuning?

  RAG for facts that change. Fine-tuning for style, format, or domain reasoning. Most production systems use both — and we'll tell you which mix is right after a 1-week discovery.

• Can I use my own LLM (open-weights)?

  Yes. We work with Llama, Mistral, Qwen, and similar — useful for data residency, cost predictability, and air-gapped environments.

• 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
• 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

• RAG for banking and financial servicesRetrieval over circulars, product terms and internal procedure — returning the clause, the revision date, and a link to the page it came from.Read
• RAG for healthcare and clinical documentsRetrieval over clinical guidelines, payer policy and internal SOPs — with the citation, the version, and an explicit refusal when the library doesn't cover the question.Read
• AI product search and catalogue RAGSemantic search and grounded product Q&A over your catalogue, reviews and size guides — with stock and price read live rather than remembered.Read