Built for hotel operations. Not retrofitted from a chatbot.
A purpose-built architecture that uses AI precisely where it earns its cost, and deterministic processing everywhere else.
Three tiers. 99.65% deterministic.
Caleta maintains a real-time knowledge graph of every entity in the hotel: rooms, guests, reservations, staff, events, equipment zones, and the relationships between them. Every operational event updates entity embeddings that encode the full relational context.
A multi-dimensional risk model scores every event across five dimensions. Only the situations that exceed risk thresholds reach AI reasoning: roughly 0.35% of total events. The result: 99.65% of processing is deterministic and sub-millisecond. AI is reserved for the situations that genuinely need it.
Nearly 300x fewer AI calls than the obvious approach.
Our architecture uses Nearly 300x fewer AI inference calls than a naive approach that sends every event to an LLM. At 100 properties, the naive approach exceeds standard API rate limits. Ours runs comfortably within them.
Less energy per property than a light bulb.
Per property, the entire system runs on less energy than a light bulb. Our three-tier filtering eliminates the computational waste of processing routine events with expensive AI models.
Gets smarter from every decision your staff makes.
Every recommendation generates labeled training data through staff accept, modify, and reject decisions. The system improves continuously without manual retraining. The longer Caleta runs at a property, the more precisely it understands which situations matter and which don't.
This feedback loop is also what makes the system harder to replicate over time. A new entrant starts from zero. Caleta starts from every shift decision your staff has made since day one.
Want to go deeper?
We're happy to walk through the architecture in detail, for technical teams, investors, or integration partners.
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