Tanabbu · Pitch Deck
Aviation · PREDICTIVE MAINTENANCE
Tanabbuالتنبؤ بصيانة الطائرات
Know which component fails next — before it grounds an aircraft on the line.
neuriya.ai — Sovereign, Arabic-first enterprise AI
Problem
Unscheduled removals still ground aircraft that could have been fixed on a schedule
- MRO planners rely on fixed maintenance-program intervals and reactive defect reports, so components often fail in service instead of being replaced on a predicted schedule.
- Saudi carriers are growing fleets fast under Vision 2030 — Riyadh Air's launch, Saudia and flyadeal fleet expansion — without a proportional increase in maintenance-planning capacity, making every aircraft-on-ground event more costly to absorb.
- Reliability programs and MPD/CMR task tracking run in separate systems from flight-hour, cycle, and ACMS sensor data, so no one has a single tail-by-tail health view.
- Parts stocking decisions are typically based on historical usage rather than predicted removals, leading to either expensive expedited shipping or an aircraft waiting on a part.
Solution
Removal forecasting turns unscheduled failures into planned work
Product
Tanabbu in action
How it works
From first touch to outcome
Ingest
Flight-hour, cycle, ACMS/sensor, and historical defect data flow in from existing fleet and MRO systems.
Predict
Models forecast component removals and estimate remaining useful life, tail by tail.
Plan
Work packages, hangar slots, and parts are sequenced around the forecast, not around a fixed calendar.
Prevent
Components are replaced on a predicted schedule, ahead of in-service failure and unplanned AOG.
Market
Sizing the opportunity
- TAM — $1.9B. MENA airline & MRO predictive-maintenance and reliability-analytics market
- SAM — $170M. Saudi airline/MRO predictive maintenance and removal-forecasting software
- SOM — $13M. 3-year obtainable share via design-partner carriers and MRO operators
Saudi carriers are expanding fleets rapidly under Vision 2030 aviation growth — Riyadh Air's launch and Saudia's fleet renewal among them — and every added aircraft increases the cost of the unplanned AOG events that removal forecasting is built to prevent.
Business model
How Tanabbu makes money
Per-tail / per-fleet predictive-maintenance subscription
Recurring fee priced by the number of tails under active forecasting.
MRO planner workspace add-on
Per-seat license for the work-package and hangar-slot planning console.
Implementation & integration fee
One-time engagement to connect ACMS/sensor, reliability-program, and MPD/CMR systems.
Revenue streams
Where the revenue actually comes from
Competition
Where we win
Our edge
- Purpose-built removal-forecasting models trained on flight-hour, cycle, and sensor trend data — not adapted from generic industrial IoT.
- Integrates directly into existing reliability programs and MPD/CMR task tracking, augmenting the maintenance program instead of replacing it.
- Sovereign, PDPL-aligned, NDMO-classified data residency for fleet and maintenance data, a requirement Saudi carriers and MRO operators increasingly demand.
- Founding team combines Saudi MRO and airline operations experience with predictive-modeling engineering.
Traction
Early validation, built for pilot readiness
- Full predictive-maintenance product experience designed and built end-to-end: data ingestion, removal forecasting, work-package planning, fleet health dashboard.
- Architecture pre-aligned to GACA, IATA MRO, ISO 27001, PDPL, and NDMO Classification.
- Actively selecting design-partner carriers and MRO operators for a first pilot cohort.
Go-to-market
Land on one fleet type, expand across the network
- 1Land with 1-2 design-partner carriers or MRO operators, starting with a single fleet type or component family.
- 2Prove removal-forecast accuracy and AOG-reduction gains over a pilot maintenance cycle, then expand fleet-wide.
- 3Expand via GACA and MRO-operator/airline-group relationships.
Team
Built by operators, not just builders
The ask
Raising a seed round to reach first carrier and MRO pilots
We're raising to fund removal-forecasting model development, fleet data-pipeline integration, and the core team through first-revenue pilot deployments with design-partner carriers and MRO operators.