Shell.ai Futures Pitch 2025 – Qatar Edition

Competition format

A collaboration between Shell and QSTP, the Shell.ai Futures Pitch 2025 – Qatar Edition, offers a unique platform for start-ups to drive innovation, leveraging Shell's vast expertise in AI and energy technologies, and benefiting from QSTP's pivotal role in Qatar's innovation ecosystem. This competition invites startups from around the globe to present their cutting-edge digital solutions designed to revolutionise future energy systems.

Start-ups can pitch their innovative solutions for any of the below listed problem statements.

The start-ups can demonstrate how their solutions can address the selected problem statement.

Competition Stages

1. Jury Evaluation (Virtual): Submissions will be reviewed to shortlist the top startups.

2. Finale Pitch (Virtual): Shortlisted startups will present their solutions, showcasing their innovative approaches to a panel of expert jury.

Eligibility

• The competition is open to registered startups with at least two team members and Technology Readiness Level (TRL) 2-5 for the proposed solution for any of the below listed problem statements.

Timeline

Rewards

Subject to the competition's Rules, The cash prizes that will be awarded are:

• USD 5000 for the first prize winner.
• USD 2000 for the second prize winner.
• USD 1000 for the third prize winner.

Subject to the competition’s Rules, after the competition ends, the Shell or any of its Affiliates has the sole discretion to award each winning Start Up with an opportunity to collaborate with Shell to develop a proof of concept [with Organiser’s contribution being up to one hundred thousand USD ($100,000), as determined by Organiser] for the winning Start Up’s Submission.

Subject to the competition's Rules, each winner will have the opportunity to receive additional offerings from QSTP, subject to additional requirements and policies of QSTP.

Choose your problem statement:

1. Identification & Quantification of Supply Chain Risk
   Develop, test, and standardise a definition of supply chain risk and resilience. The definition should lead to a quantifiable metric to measure SC risk and resilience, leading to unambiguous and consistent decision-making. A supply chain risk model developed using a consistent definition can be used for stress testing.
2. HAZOP Based Fault Diagnosis and Root Cause Analysis in industrial systems
   In plant-wide anomaly monitoring systems, after identifying faults and key contributing variables, it’s crucial to predict affected variables and trace the problem's origin. This prognosis and root cause analysis (RCA) is challenging in unsupervised systems with limited fault data.
3. Improving data quality using data integration across tools and enabling workflows for Assets
   Data quality is a foundational and major requirement to enable integration across data source systems and end-to-end workflows resulting to automation, simplification and enhanced value from data. Currently, data is “siloed” across lifecycle resulting in lack of integration across systems.

Partner