OBVO

Taking Overbooking Strategy to the next level

Increase your RASK by +2% optimizing your overbooking

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Flights optimized
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Overbooked Pax

Why OVBO?

OVBO overbooks automatically the adequate flights for each market, obtaining incremental revenue with minimum denied boarding costs and impact on passengers experience

OVBO is an innovative revenue management tool that takes the most from edge AI, big data and web cloud technologies to provide accurate boarding forecast for overbooking.

OVBO works plug & play for Navitaire and Radixx.

It’s the result of a partnership between FlywareLabs and Tarmac that made OVBO possible through:

Engineering experience

Data Science

RM know how

Consultancy Services

Leadership team

Passion for the airline industry

Charly Neuman

Charly Neuman

Founder and CEO

Former airline CIO, Delivery and Engineering manager with +20 years in the tech industry.

Alvaro Barros

Alvaro Barros

CTO

Former airline CTO, Strong engineering background building scalable and robust solutions for commerce.

Nicolas Andriano

Nicolas Andriano

Data Specialist Lead

Former airline Head of RM and data scientist with deep analytics & mathematics background.s

Juan Afeltra

RM Guru

Founder of TARMAC and former Sr. Director S&S LatAm.

Technology

Our technology helps RM teams to automatically optimize their overbooking strategy and stay focused on the core RM business

Efficient

Our AI Algorithm predicts each PAX no-show probability so we can recommend an optimal lid for each flight.

Automatic

Fully integrated with the PSS in order to provide a proactive recommendation on a daily basis.

Flexible

RM teams can customize business rules to adapt OVBO to markets and business requirements

How it works

Gráfico 1
  1. PSS Feed stored in data lake to keep 100% history available for regression.
  2. Training Algorithm uses Flown data to update predictive model.
  3. Training Algorithm uses Flown data to update predictive model.
  4. Based on probability, model defines a new LID to overbook each flight according to passenger individual no-show probability.
  5. Update LID into PSS to update overbooking limit.

How it works

We process and store the airline data from the PSS

Based on flown data, we train an AI algorithm to predict future no-show

How it works

Our algorithm predicts every PAX probability of no-show for all upcoming flights

OVBO sends the lid recommendation directly to the PSS

Based on the RM analyst inputs in our UI, OVBO generates an optimal lid recommendation for every flight

OVBO sends the lid recommendation directly to the PSS

1
PSS Feed stored in data lake to keep 100% history available for regression.
2
Training Algorithm uses Flown data to update predictive model.
3
Based on probability, model defines a new LID to overbook each flight according to passenger individual no-show probability.
4
Update LID into PSS to update overbooking limit.

Main Features

Web Responsive

Secure Cloud based solution

Live Health Check

Rules Management

Custom enhancement

Exclusive predictive model

RM Integration

Ad hoc airline features

Tableau integrated reporting services

RM consulting services

Sync with external DB

Contact us

Please send us a message if you want to know more about us. We will get back to you within 24 hours!