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Your hotels are not equally right for every guest.
ParaVida determines which environments fit which people, explains why, and lets your existing digital experience use that intelligence.
Show meNinety seconds. Real engine, real portfolio, no slides.
Same portfolio. Same night. Same price.
Change the human, and the answer changes.
Twelve properties in ten markets. Nothing about them moves below — not the amenities, not the rates, not the photographs. The only thing that changes is who is asking, and the entire order rearranges with the reasons attached.
This guest says
- I train every day
- Strength is the priority
- Warm climates
- High protein
Which weights that produces
- Training
- 7.5
- City energy
- 4.14
- Beach
- 2.6
- Food
- 1.9
Derived from their answers by the same pipeline that serves every ParaVida user. No weight here was written by hand.
- 1Composite · South BeachMiami, United StatesFitsTraining 9/10TradeoffValue 2/1077good fit
- 2Composite · BrickellMiami, United StatesFitsCity energy 8/10TradeoffTraining 8/1073good fit
- 3Composite · La JollaSan Diego, United StatesFitsTraining 8/10TradeoffCity energy 6/1073good fit
- 4Composite · Batu BolongCanggu, IndonesiaFitsTraining 9/10TradeoffCity energy 5/1072good fit
- 5Composite · PererenanCanggu, IndonesiaFitsTraining 9/10TradeoffCity energy 5/1072good fit
- 6Composite · El BornBarcelona, SpainFitsCity energy 9/10TradeoffTraining 6/1070good fit
- 7Composite · Camps BayCape Town, South AfricaFitsClimate fit 9.070442393670264/10TradeoffTraining 5/1066mixed fit
- 8Composite · Príncipe RealLisbon, PortugalFitsValue 8/10TradeoffTraining 6/1064mixed fit
- 9Composite · Tulum Hotel ZoneTulum, MexicoFitsBeach 9/10TradeoffCity energy 4/1060mixed fit
- 10Composite · El PobladoMedellín, ColombiaFitsCity energy 8/10TradeoffTraining 6/1060mixed fit
- 11Composite · Playa GuionesNosara, Costa RicaFitsBeach 9/10TradeoffTraining 5/1052weak fit
- 12Composite · DIFCDubai, United Arab EmiratesFitsNo verified readingsTradeoffNothing can be assessedno score
What changed
The human. Nothing else.
Same twelve properties. Same amenities, same prices, same photographs, same descriptions. The order, the reasons, the tradeoffs and the numbers all moved because one person was replaced with another — and that is the only signal on a hospitality page that can do it.
So what
What that intelligence lets you do.
Mechanisms, not projections. ParaVida has never run a pilot and has no outcome data, so there is no percentage on this page — an executive can reason about a mechanism, and will rightly refuse to defend a number nobody can source.
01
Match guests to properties more intelligently
The same inventory produces a different order for a different person. A recommendation stops being the same list for everyone and starts carrying information.
02
Differentiate your portfolio
See where two properties genuinely overlap and where one gives you something nothing else does — measured across the environment, not asserted by a brand team.
03
Add a personalisation layer you do not have
Compatibility sits alongside price, loyalty and availability. It is the only signal on the page that changes with who is reading it.
04
Increase booking confidence
A guest is given a reason a property fits them and an honest tradeoff, instead of being asked to compare twelve near-identical descriptions themselves.
05
Strengthen the direct experience
An intelligence layer an OTA cannot replicate, on a surface you own, without a booking intermediary between you and the guest.
06
Create a portable environmental identity
A guest's ParaVida Passport travels with them. Where they let it, your properties can be assessed against who they actually are rather than what they last clicked.
Why your stack does not already do this
Everything you know is about the property. Nothing is about the person.
Your systems are excellent at describing inventory and at remembering behaviour. Neither answers whether this specific environment suits this specific human, and no amount of booking history produces it — because the missing half is a model of the place, not more data about the guest.
Your systems know today
- Price
- Availability
- Amenities
- Reviews
- Loyalty tier
- Browsing history
- Booking history
ParaVida adds
- Who this property fits
- Why it fits them
- Where it conflicts with them
- How confident that is
- What evidence supports it
- How trip context changes the answer
ParaVida Inside
You do not replace your platform. You add a pillar to it.
The same property page, with and without the intelligence layer. Toggle it and watch what changes — and, more importantly, what does not.
Your brand
Composite · Batu Bolong
Canggu, Indonesia · Batu Bolong
ParaVida · Environmental fit
72good fit
Confidence 82 · based on 100% of the dimensions this guest weights
- Why this fits
- Training 9/10Food 8/10Beach 8/10
- What to know
- Mountains 3/10Sleep 4/10
What is this trip for?
A description of the property, written by the partner, in the partner’s voice. ParaVida does not replace it, rewrite it, or rank it. This paragraph is identical in both states.
- Pool
- Fitness centre
- Spa
- Restaurant
- Airport transfer
Photograph: Mx. Granger · CC0. A licensed photograph of the destination, not of any specific hotel — this reference surface depicts no real property or company.
Everything the partner owned before is still theirs. ParaVida added one block, and it is the only thing on the page that knows who is looking.
The partner keeps
- Brand
- Booking
- Pricing
- Inventory
- Payments
- Loyalty
- Customer relationship
ParaVida adds
- Environmental compatibility
- Explanation
- Confidence
- Evidence and provenance
- Passport intelligence
- Contextual intelligence
Where AI sits
AI explains. The engine decides.
Every number in this product came from a deterministic engine — the same inputs always produce the same answer, and no model can move one. AI’s job is to interpret a question and put the engine’s output into language.
Deterministic engine
Computes compatibility, confidence, coverage and the refusal.
ParaVida AI
Interprets the question and explains the result. It never writes a number.
There is no language model in this build and no key configured for one. The answer beside this was produced by the deterministic query layer that a model would eventually sit in front of.
Why does Composite · South Beach score what it does for the movement-focused traveller?
Computed fact
77 — good fit. Carried by training (9 at weight 7.5), city energy (8 at weight 4.14), beach (9 at weight 2.6).
Computed fact
Most weighted ground given up: value at 2 and city energy at 8. Every dimension carried a counted reading.
Business inference
The number is a function of this guest's weights, not of the property's quality. A different guest reweights the same readings and gets a different answer from the same building.
Underneath
This is much deeper than a widget.
Everything above rests on a full enterprise operating environment: portfolio intelligence, per-property profiles with provenance on every reading, an evidence operation, a graph of the real environment around each property, and a pilot that computes its own readiness.
Portfolio Intelligence
Your inventory as one environmental system. One pair of properties are competing for the same guest.
Open →Property Intelligence
Every reading with its origin, grade, source and verification date — and what would have to change to move the number.
Open →The Diagnostic
7 findings the computation actually supported, each with the leadership decision it raises.
Open →Evidence operations
15 readings the engine refused to count, each with what confirming it would unlock.
Open →Environmental Graph
Your properties inside the real environment around them — neighbourhoods, destinations and seventy-six curated venues.
Open →Pilot Center
Readiness computed from the live workspace, with the blockers stated rather than hidden.
Open →Enter ParaVida EnterpriseThe full operating environment. Seventeen surfaces, all of them live.
Why this is credible
Guarantees, expressed as architecture.
Every line below is a property of how the system is built rather than a value the company holds. The difference matters: a value can be revised in a meeting, and a system with no parameter for something cannot be persuaded to have one.
- No paid ranking
- The scoring engine takes a weight vector and an entity. There is no parameter through which a commercial relationship could be expressed, so there is nothing to switch off.
- No sponsored boosts
- Ranking is a pure function of compatibility. A partner cannot buy a position because position is not an input.
- No AI-generated scores
- An AI-proposed reading is retained with its evidence and excluded from the arithmetic until a person confirms it. The exclusion happens on ingest, so no surface can forget to apply it.
- Unknown stays unknown
- A dimension with no verified reading is excluded from the numerator and the denominator both. It lowers confidence, never the score, and it is never treated as a zero.
- Evidence has provenance
- Every reading carries its grade, its named source, its verification date, and whether the property owns it or inherits it from where it stands.
- Confidence is explicit
- Evidence grade sets a hard ceiling on the confidence a reading may claim, applied when the property is projected rather than when it is displayed.
- Tradeoffs cannot be hidden
- The explanation is derived from the same breakdown as the score. The dimensions a property gives up ground on are in it by construction.
- Below the floor, no answer
- When weighted coverage falls under the floor, the engine returns a refusal rather than a number. A partner cannot request an estimate.
3 different properties lead for 5 guest profiles in this portfolio, one property returns no score at all, and 15 readings are excluded from every number on this page. None of that was arranged — it is what the engine returned.
The next step
A test, not a migration.
10 to 40 properties, one digital surface, one guest cohort, one compatibility experience, ninety days, then an executive review against criteria fixed before launch.
- Step 1
Provide selected inventory
Ten to forty properties, with whatever you already hold about them. No integration, no engineering, no data warehouse project.
Partner - Step 2
ParaVida builds verified profiles
Each property is projected onto the dimension spine. What is confirmed counts; what is proposed is queued; what is unknown stays unknown and says so.
ParaVida - Step 3
One compatibility response, one surface
A single read call rendered on a property page you already own. Nothing about your booking flow, pricing or loyalty is touched.
Partner engineering - Step 4
Run a controlled cohort
Ninety days, one cohort, one mark. A pilot that changed five things would tell you nothing about any of them.
Both - Step 5
Review the evidence
Against criteria fixed and hashed before launch, so nothing can be reinterpreted afterwards. The outcome may be that it did not work.
Both
What is ready today
- Environmental intelligence
- Portfolio assessment
- Compatibility API
- Property profiling and assessment on your inventory
What must be completed before a live pilot
- Multi-tenant accounts
- API authentication
- Verification write-back
- SSO and team access
- Usage metering and billing
- Generative language layer
Each one is scoped with a named dependency in the data room. They are prerequisites for running on real guests, not for starting the conversation or for profiling your properties.
Demonstration dataComposite demonstration portfolio. No hotel group has been assessed, and no company is depicted. Property-owned readings were authored for this demonstration; location-inherited readings are ParaVida's real curated destination data. Everything computed from it is real; the inventory it is computed over is not a customer.