Nabd · Pitch Deck
Industrial · PREDICTIVE MAINTENANCE
Nabdنبض
Hear the machine before it breaks — failures forecast from vibration, temperature, and operational data, weeks out.
neuriya.ai — Sovereign, Arabic-first enterprise AI
Problem
Maintenance is scheduled by calendar, not by actual equipment condition
- Rotating and critical equipment is serviced on a fixed calendar schedule, so healthy assets get maintained too often and failing ones too late.
- Vibration, temperature, and current readings sit in separate single-sensor systems, so a failure mode that shows up across signals is missed by any one system alone.
- Unplanned downtime is usually discovered only when equipment actually stops, leaving maintenance teams reacting instead of scheduling around a known risk window.
- Maintenance budget is spread evenly across a fleet instead of going to the assets that are actually closest to failure.
Solution
Every asset carries a live failure-risk score, not a calendar date
Product
Nabd in action
How it works
From first touch to outcome
Connect
Existing vibration, temperature, and operational sensors are connected — no forced hardware replacement.
Monitor
Every asset is scored continuously against its own operating baseline, not a generic industry threshold.
Predict
Anomalies that indicate developing failure are surfaced with an estimated time-to-failure window.
Act
Maintenance teams schedule work before failure, closing the loop between prediction and action.
Market
Sizing the opportunity
- TAM — $1.8B. MENA predictive-maintenance & industrial IoT analytics market
- SAM — $190M. Saudi predictive-maintenance software market
- SOM — $14M. 3-year obtainable share via design-partner plants and operators
Vision 2030's industrial localization push under GAMI and NIDLP is raising uptime expectations on Saudi plants just as aging rotating-equipment fleets increase unplanned-failure risk, making a shift from calendar-based to condition-based maintenance a near-term operating priority.
Business model
How Nabd makes money
Per-asset monitoring subscription
Recurring fee priced by number of monitored rotating and critical assets.
Fleet-analytics tier
Add-on subscription for cross-plant asset ranking and maintenance-budget prioritization dashboards.
Sensor-integration & onboarding fee
One-time fee to connect existing vibration, temperature, and operational sensors.
Revenue streams
Where the revenue actually comes from
Competition
Where we win
Our edge
- Connects to sensors plants already have — no forced hardware replacement, shortest path to a pilot.
- Multi-signal fusion catches failure modes a single-sensor system misses, instead of monitoring vibration or temperature in isolation.
- Sovereign, in-Kingdom model hosting for sensor and asset data, satisfying PDPL and NDMO Classification requirements global platforms aren't built for.
- Founding team combines Saudi plant-operations experience with applied machine-learning engineering.
Traction
Early validation, built for pilot readiness
- Full predictive-maintenance product experience built end-to-end: fleet health dashboard, equipment risk ranking, and failure-prediction reporting.
- Architecture pre-aligned to IEC 62443, PDPL, NDMO Classification, and ISO 27001.
- Actively selecting design-partner plants and operators for a first pilot cohort.
Go-to-market
Land on one asset class, expand to fleet-wide risk scoring
- 1Land with 1-2 design-partner plants, starting on one critical-equipment class with existing sensor coverage.
- 2Prove time-to-failure accuracy and downtime-avoidance value over a pilot quarter.
- 3Expand to the full asset fleet and additional plants within the same operator.
Team
Built by operators, not just builders
The ask
Raising a seed round to reach first plant pilots
We're raising to fund multi-signal failure-prediction model development, sensor-integration tooling, and the core team through first-revenue pilot deployments.