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Demo listing. This page uses illustrative seed metadata to demonstrate the Atlas. Claims shown here are not verified product facts.

Baguprice

Sets session-specific prices for airline ancillaries (bags, seats, meals) by estimating purchase probability and maximising expected…

Sets session-specific prices for airline ancillaries (bags, seats, meals) by estimating purchase probability and maximising expected revenue under willingness-to-pay monotonicity.

DemoDeployment-Ready FoundationfazeZERO VerifiedProprietaryMarketing & Growth

Last verified 15 Sept 2026 · Updated 23 Sept 2026 · Metadata v4

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Configuration + implementation engagement

Problem

Airlines and OTAs with internet booking engines whose ancillary conversion is <5% under static price points invariant to itinerary and shopper context.

Outcome

A deployment-ready foundation that teams can configure into production.

Who uses it

  • Operations Analyst
  • Solution Architect

Key capabilities

  • Core workflow

    AI-assistedIncluded

    Primary operating workflow for this foundation.

  • Document intelligence

    AI-assistedIncluded

    Extract and ground decisions from operating documents.

Typical workflow

Primary operating flow

Trigger: An operator starts the primary use case

  1. Task

    Stage 1: Capture inputs

    Operator

  2. AI-assisted

    Stage 2: Assist with draft

    System

  3. Human checkpoint

    Stage 3: Human approval

    Approver

Task Automated AI-assisted Human checkpoint Decision

About this foundation

Contextual ancillary pricing. Fare engines price the right-to-fly; ancillaries still use human rule tables. Baguprice commercialises Deepair’s progression from logistic price mapping → DNN + discrete exhaustive search → end-to-end DNN with custom loss encoding WTP monotonicity, with live A/B gains of +36% conversion and +10% revenue per offer versus rule-based pricing — without using intrusive personal identity beyond session/itinerary context.

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